docker-compose.prod: poistettu dist-volume mount joka yliajoi Docker-imagen frontendin

StatusBar ja kipina-node-skripti käyttävät ?v=timestamp-parametria
välimuistin ohittamiseen. Binäärin uudelleenlataus oletuksena Y.
deploy-binaries.sh kopioi myös kipina-node-skriptin palvelimelle.

.gitignore

@@ -38,5 +38,8 @@ Cargo.lock
 # Ajonaikaiset tietokannat
 *.db
 
+# Lokitiedostot
+*.log
+
 # Wanha versio
 temp/

kipina-node

@@ -0,0 +1,131 @@
+#!/bin/bash
+# Kipinä Node — lataa oikea binääri ja käynnistä
+set -e
+
+BASE_URL="https://kipina.studio/download"
+HUB_URL="${KIPINA_HUB:-wss://kipina.studio/ws}"
+OLLAMA_URL="${OLLAMA_URL:-http://localhost:11434}"
+
+# Tunnista OS ja arkkitehtuuri
+OS=$(uname -s | tr '[:upper:]' '[:lower:]')
+ARCH=$(uname -m)
+
+case "$OS-$ARCH" in
+    darwin-arm64)  BINARY="kipina-node-macos-arm64" ;;
+    darwin-x86_64) BINARY="kipina-node-macos-arm64" ;; # Rosetta
+    linux-x86_64)  BINARY="kipina-node-linux-x86_64" ;;
+    linux-aarch64) BINARY="kipina-node-linux-arm64" ;;
+    *) echo "Ei tuettu: $OS-$ARCH"; exit 1 ;;
+esac
+
+echo ""
+echo "  ╔══════════════════════════════════════╗"
+echo "  ║       Kipinä Agentic Node            ║"
+echo "  ╚══════════════════════════════════════╝"
+echo ""
+echo "  OS: $OS ($ARCH)"
+echo ""
+
+# Etsi Ollama-instanssit
+CANDIDATES=(
+    "http://localhost:11434"
+    "http://127.0.0.1:11434"
+    "http://ollama:11434"
+    "http://host.docker.internal:11434"
+)
+
+# Lisää OLLAMA_URL listaan jos asetettu ja ei jo mukana
+if [ -n "$OLLAMA_URL" ]; then
+    ALREADY=false
+    for c in "${CANDIDATES[@]}"; do
+        [ "$c" = "$OLLAMA_URL" ] && ALREADY=true
+    done
+    $ALREADY || CANDIDATES=("$OLLAMA_URL" "${CANDIDATES[@]}")
+fi
+
+echo "  Etsitään Ollama-instansseja..."
+FOUND=()
+for url in "${CANDIDATES[@]}"; do
+    if curl -s --connect-timeout 1 "$url/api/tags" &>/dev/null; then
+        FOUND+=("$url")
+    fi
+done
+
+if [ ${#FOUND[@]} -eq 0 ]; then
+    # Ei löytynyt — yritä käynnistää lokaali
+    if command -v ollama &>/dev/null; then
+        echo "  Käynnistetään Ollama..."
+        ollama serve &>/dev/null &
+        sleep 3
+        if curl -s --connect-timeout 1 "http://localhost:11434/api/tags" &>/dev/null; then
+            OLLAMA_URL="http://localhost:11434"
+            echo "  ✓ Ollama käynnistetty ($OLLAMA_URL)"
+        else
+            echo "  ✗ Ollaman käynnistys epäonnistui."
+            exit 1
+        fi
+    else
+        echo ""
+        echo "  ✗ Ollamaa ei löytynyt."
+        echo "    Kontti/remote: OLLAMA_URL=http://HOST:11434 ./kipina-node"
+        echo "    Asenna:        curl -fsSL https://ollama.ai/install.sh | sh"
+        exit 1
+    fi
+elif [ ${#FOUND[@]} -eq 1 ]; then
+    OLLAMA_URL="${FOUND[0]}"
+    echo "  ✓ Ollama löytyi: $OLLAMA_URL"
+else
+    echo ""
+    echo "  Löytyi ${#FOUND[@]} Ollama-instanssia:"
+    echo ""
+    for i in "${!FOUND[@]}"; do
+        echo "    $((i+1))) ${FOUND[$i]}"
+    done
+    echo ""
+    read -p "  Valitse [1-${#FOUND[@]}]: " -r CHOICE
+    if [[ "$CHOICE" =~ ^[0-9]+$ ]] && [ "$CHOICE" -ge 1 ] && [ "$CHOICE" -le ${#FOUND[@]} ]; then
+        OLLAMA_URL="${FOUND[$((CHOICE-1))]}"
+    else
+        OLLAMA_URL="${FOUND[0]}"
+        echo "  Käytetään oletusta: $OLLAMA_URL"
+    fi
+    echo "  ✓ Valittu: $OLLAMA_URL"
+fi
+
+echo ""
+echo "  Hub: $HUB_URL"
+echo "  Ollama: $OLLAMA_URL"
+if [ -n "$KIPINA_MODEL" ]; then
+    echo "  Malli: $KIPINA_MODEL (Ympäristömuuttujasta)"
+fi
+
+# Lataa binääri
+BIN_PATH="./kipina-node-bin"
+if [ -f "$BIN_PATH" ]; then
+    echo ""
+    read -p "  Löydettiin vanha kipina-node-bin lokaalisti. Haluatko poistaa sen ja ladata uusimman version? [Y/n] " -r DEL_CHOICE
+    if [[ "$DEL_CHOICE" =~ ^[Nn]$ ]]; then
+        echo "  ✓ Käytetään lokaalia versiota."
+    else
+        rm -f "$BIN_PATH"
+        echo "  ✓ Vanha binääri poistettu ja korvataan uudella."
+    fi
+fi
+
+if [ ! -f "$BIN_PATH" ]; then
+    echo "  Ladataan tuorein $BINARY..."
+    curl -sSL "$BASE_URL/$BINARY" -o "$BIN_PATH"
+    chmod +x "$BIN_PATH"
+fi
+
+echo ""
+echo "  ✓ Siirrytään Kipinä Noden hallintaan..."
+echo "    Ctrl+C pysäyttää"
+echo ""
+
+if [ -n "$KIPINA_MODEL" ]; then
+    export OLLAMA_MODEL="$KIPINA_MODEL"
+fi
+export HUB_URL="$HUB_URL"
+export OLLAMA_URL="$OLLAMA_URL"
+exec "$BIN_PATH"

network-poc/Dockerfile.prod

@@ -3,26 +3,29 @@
 # --- Vaihe 1: Frontend (Astro) ---
 FROM node:22-slim AS frontend
 WORKDIR /app/frontend
-# Riippuvuudet ensin → cache-kerros (muuttuu harvoin)
 COPY frontend/package.json frontend/package-lock.json* ./
 RUN npm install --silent
-# Lähdekoodi → muuttuu usein, mutta npm install on cachessa
+# Cache-buster: git hash pakottaa rebuildin kun koodi muuttuu
-COPY frontend/ .
+ARG CACHEBUST=0
+COPY frontend/src/ ./src/
+COPY frontend/public/ ./public/
+COPY frontend/astro.config.mjs frontend/tsconfig.json ./
 RUN npm run build
 
 # --- Vaihe 2: Wasm (wasm-pack) ---
+# Cargo registry cachetetaan mount-cachella, lähdekoodi kopioidaan tuoreena
 FROM rust:slim AS wasm-builder
 RUN apt-get update && apt-get install -y curl pkg-config libssl-dev g++ && rm -rf /var/lib/apt/lists/*
 RUN curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
 WORKDIR /app
 COPY Cargo.toml Cargo.lock* ./
 COPY node/Cargo.toml node/Cargo.toml
-COPY node/src node/src
-# Dummy-cratet jotta workspace Cargo.toml on tyytyväinen
 COPY hub/Cargo.toml hub/Cargo.toml
 COPY native-node/Cargo.toml native-node/Cargo.toml
 COPY cli/Cargo.toml cli/Cargo.toml
 RUN mkdir -p hub/src native-node/src cli/src && touch hub/src/main.rs native-node/src/main.rs cli/src/main.rs
+ARG CACHEBUST=0
+COPY node/src node/src
 RUN --mount=type=cache,target=/usr/local/cargo/registry \
     --mount=type=cache,target=/app/target \
     cd node && wasm-pack build --target web --out-dir /app/wasm-pkg
@@ -33,12 +36,12 @@ RUN apt-get update && apt-get install -y pkg-config libssl-dev && rm -rf /var/li
 WORKDIR /app
 COPY Cargo.toml Cargo.lock* ./
 COPY hub/Cargo.toml hub/Cargo.toml
-COPY hub/src hub/src
-# Tarvitaan dummy-cratet jotta workspace kompiloi
 COPY node/Cargo.toml node/Cargo.toml
 COPY native-node/Cargo.toml native-node/Cargo.toml
 COPY cli/Cargo.toml cli/Cargo.toml
 RUN mkdir -p node/src native-node/src cli/src && touch node/src/lib.rs native-node/src/main.rs cli/src/main.rs
+ARG CACHEBUST=0
+COPY hub/src hub/src
 RUN --mount=type=cache,target=/usr/local/cargo/registry \
     --mount=type=cache,target=/app/target \
     cargo build --release -p hub \
@@ -52,11 +55,6 @@ COPY --from=hub-builder /usr/local/bin/hub /usr/local/bin/hub
 COPY --from=frontend /app/frontend/dist /app/frontend/dist
 COPY --from=wasm-builder /app/wasm-pkg /app/frontend/dist/pkg
 
-# Kopioidaan GUIDE.md ja templates
-COPY frontend/public/GUIDE.md /app/frontend/dist/GUIDE.md
-COPY frontend/public/templates /app/frontend/dist/templates
-COPY frontend/public/avatars /app/frontend/dist/avatars
-
 WORKDIR /app
 ENV STATIC_DIR=/app/frontend/dist
 EXPOSE 3000

network-poc/build-binaries.sh

@@ -1,38 +0,0 @@
-#!/bin/bash
-# Käännä kipina-node binäärit kaikille alustoille
-set -e
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-OUT="$SCRIPT_DIR/frontend/public/download"
-mkdir -p "$OUT"
-
-echo "=== Kipinä Node — Binary Build ==="
-
-# macOS ARM (natiivi)
-echo "[1/3] macOS ARM64..."
-cd "$SCRIPT_DIR"
-cargo build --release -p native-node --no-default-features 2>&1 | tail -1
-cp target/release/native-node "$OUT/kipina-node-macos-arm64"
-echo "      $(ls -lh "$OUT/kipina-node-macos-arm64" | awk '{print $5}')"
-
-# Linux x86_64 (Docker)
-echo "[2/3] Linux x86_64..."
-docker run --rm \
-    -v "$SCRIPT_DIR":/app -w /app \
-    --platform linux/amd64 \
-    rust:slim \
-    bash -c "apt-get update -qq && apt-get install -y -qq pkg-config libssl-dev >/dev/null 2>&1 && cargo build --release -p native-node --no-default-features 2>&1 | tail -1 && cp target/release/native-node /app/frontend/public/download/kipina-node-linux-x86_64"
-echo "      $(ls -lh "$OUT/kipina-node-linux-x86_64" | awk '{print $5}')"
-
-# Linux ARM64 (Docker)
-echo "[3/3] Linux ARM64..."
-docker run --rm \
-    -v "$SCRIPT_DIR":/app -w /app \
-    --platform linux/arm64 \
-    rust:slim \
-    bash -c "apt-get update -qq && apt-get install -y -qq pkg-config libssl-dev >/dev/null 2>&1 && cargo build --release -p native-node --no-default-features 2>&1 | tail -1 && cp target/release/native-node /app/frontend/public/download/kipina-node-linux-arm64"
-echo "      $(ls -lh "$OUT/kipina-node-linux-arm64" | awk '{print $5}')"
-
-echo ""
-echo "=== Binäärit valmiina ==="
-ls -lh "$OUT"/kipina-node-*

network-poc/deploy-fast.sh

@@ -1,28 +0,0 @@
-#!/bin/bash
-# Nopea deploy: päivittää vain frontendin (ei kontin uudelleenkäynnistystä)
-# Hub-binäärin päivitys: käytä deploy.sh tai deploy-light.sh
-set -e
-
-SERVER="ubuntu@86.50.252.98"
-REMOTE_DIR="~/code/agentic-studio/network-poc"
-SSH_OPTS="-o StrictHostKeyChecking=no"
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-
-echo "=== Kipinä Studio — Frontend Deploy ==="
-
-# 1. Buildaa frontend paikallisesti
-echo "[1/2] Rakennetaan frontend..."
-cd "$SCRIPT_DIR/frontend"
-[ -d node_modules ] || npm install --silent
-npm run build --silent 2>&1 | tail -1
-
-# 2. Synkataan dist/ palvelimelle (vain muuttuneet tiedostot)
-echo "[2/2] Synkataan dist/ → palvelin..."
-ssh $SSH_OPTS $SERVER "mkdir -p $REMOTE_DIR/frontend/dist"
-rsync -az --delete -e "ssh $SSH_OPTS" "$SCRIPT_DIR/frontend/dist/" "$SERVER:$REMOTE_DIR/frontend/dist/"
-
-echo ""
-echo "=== Valmis! Frontend päivitetty — ei uudelleenkäynnistystä ==="
-echo "    https://kipina.studio"
-echo ""
-echo "Huom: Jos Rust-koodi (hub/) muuttui, aja: ./deploy.sh"

network-poc/deploy-light.sh

@@ -1,33 +0,0 @@
-#!/bin/bash
-# Kevyt deploy: lähetetään vain koodi, palvelin buildaa itse
-set -e
-
-SERVER="ubuntu@86.50.252.98"
-REMOTE_DIR="~/code/agentic-studio/network-poc"
-SSH_OPTS="-o StrictHostKeyChecking=no"
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-
-echo "=== Kipinä Studio Deploy (remote build) ==="
-
-# 1. Synkataan koodi palvelimelle (vain muuttuneet tiedostot)
-echo "[1/3] Synkataan koodi..."
-rsync -az --delete \
-    --exclude 'target/' \
-    --exclude 'node_modules/' \
-    --exclude 'dist/' \
-    --exclude '.astro/' \
-    --exclude 'temp/' \
-    --exclude '*.db' \
-    --exclude '.git/' \
-    "$SCRIPT_DIR/" "$SERVER:$REMOTE_DIR/"
-
-# 2. Rakennetaan image palvelimella
-echo "[2/3] Rakennetaan image palvelimella..."
-ssh $SSH_OPTS $SERVER "cd $REMOTE_DIR && docker build -f Dockerfile.prod -t kipina-agentic:latest ."
-
-# 3. Käynnistetään
-echo "[3/3] Käynnistetään..."
-ssh $SSH_OPTS $SERVER "cd $REMOTE_DIR && docker compose -f docker-compose.prod.yml down && docker compose -f docker-compose.prod.yml up -d"
-
-echo "=== Valmis! https://kipina.studio ==="

network-poc/deploy-local.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# Kipinä Studio — paikallinen kehitysympäristö
+# Buildaa frontendin, käynnistää hubin ja native-noden (Ollama)
+# Käyttö: ./deploy-local.sh
+set -e
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$SCRIPT_DIR"
+
+cleanup() { echo ""; echo "Pysäytetään..."; kill $HUB_PID $NODE_PID 2>/dev/null; exit 0; }
+trap cleanup INT TERM
+
+# Portti vapaaksi
+lsof -ti:3000 | xargs kill -9 2>/dev/null || true
+
+# Frontend
+echo "[1/3] Frontend..."
+cd "$SCRIPT_DIR/frontend"
+[ -d node_modules ] || npm install --silent
+npm run build 2>&1 | tail -1
+cd "$SCRIPT_DIR"
+
+# Hub
+echo "[2/3] Hub..."
+STATIC_DIR="$SCRIPT_DIR/frontend/dist" cargo run -p hub 2>&1 &
+HUB_PID=$!
+until curl -sf http://localhost:3000 >/dev/null 2>&1; do sleep 1; done
+
+# Native-node
+NODE_PID=""
+if curl -sf http://localhost:11434/api/tags >/dev/null 2>&1; then
+    MODEL=$(curl -s http://localhost:11434/api/tags | python3 -c "
+import sys,json
+ms=json.load(sys.stdin).get('models',[])
+for m in ms:
+    n=m['name']
+    if '7b' in n and 'coder' in n: print(n); exit()
+for m in ms:
+    if 'coder' in m['name']: print(m['name']); exit()
+if ms: print(ms[0]['name'])
+" 2>/dev/null)
+    if [ -n "$MODEL" ]; then
+        echo "[3/3] Native-node ($MODEL)..."
+        HUB_URL=ws://localhost:3000/ws OLLAMA_MODEL="$MODEL" \
+            cargo run -p native-node --no-default-features 2>&1 &
+        NODE_PID=$!
+    else
+        echo "[3/3] Ollama: ei malleja (ollama pull qwen2.5-coder:7b)"
+    fi
+else
+    echo "[3/3] Ei Ollamaa — Wasm-fallback selaimessa"
+fi
+
+echo ""
+echo "=== http://localhost:3000 === Ctrl+C pysäyttää"
+open http://localhost:3000 2>/dev/null || xdg-open http://localhost:3000 2>/dev/null || true
+wait $HUB_PID

network-poc/deploy-remote.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+# Kipinä Studio — tuotanto-deploy kipina.studioon
+# Buildaa Docker-imagen (frontend + hub + wasm) ja vie palvelimelle
+# Käyttö: ./deploy-remote.sh
+set -e
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$SCRIPT_DIR"
+
+SERVER="ubuntu@86.50.252.98"
+REMOTE_DIR="~/code/agentic-studio/network-poc"
+SSH_OPTS="-o StrictHostKeyChecking=no"
+
+# SSH-avain — yritetään yhdistää, jos ei onnistu, pyydetään avainta
+if ! ssh $SSH_OPTS "$SERVER" "echo ok" >/dev/null 2>&1; then
+    echo "SSH-yhteys ei onnistu, lisätään avain..."
+    ssh-add "$HOME/.ssh/id_rsa" 2>/dev/null || ssh-add
+fi
+
+# Auto-commit
+if ! git diff --quiet HEAD 2>/dev/null || \
+   [ -n "$(git ls-files --others --exclude-standard 2>/dev/null)" ]; then
+    echo "Uncommitted muutoksia — commitoidaan..."
+    read -rp "  Commit-viesti: " msg
+    [ -z "$msg" ] && msg="Deploy $(date +%Y-%m-%d\ %H:%M)"
+    git add -A && git commit -m "$msg"
+fi
+
+echo "=== Kipinä Studio Deploy → kipina.studio ==="
+
+# 1. Docker-image (CACHEBUST pakottaa lähdekoodin uudelleenkopioinnin)
+echo "[1/4] Docker build..."
+docker build --platform linux/amd64 -f Dockerfile.prod \
+    --build-arg CACHEBUST="$(git rev-parse HEAD)" \
+    -t kipina-agentic:latest .
+
+# 2. Pakkaus
+echo "[2/4] Pakataan..."
+docker save kipina-agentic:latest | gzip > /tmp/kipina-agentic.tar.gz
+echo "  $(du -h /tmp/kipina-agentic.tar.gz | cut -f1)"
+
+# 3. Siirto
+echo "[3/4] Siirretään..."
+scp $SSH_OPTS /tmp/kipina-agentic.tar.gz "$SERVER:/tmp/"
+scp $SSH_OPTS docker-compose.prod.yml Caddyfile.prod "$SERVER:$REMOTE_DIR/"
+
+# 4. Käynnistys
+echo "[4/4] Käynnistetään..."
+ssh $SSH_OPTS "$SERVER" "gunzip -c /tmp/kipina-agentic.tar.gz | docker load && rm /tmp/kipina-agentic.tar.gz"
+ssh $SSH_OPTS "$SERVER" "cd $REMOTE_DIR && docker compose -f docker-compose.prod.yml down && docker compose -f docker-compose.prod.yml up -d"
+
+# Discord
+WEBHOOK="https://discord.com/api/webhooks/1489504066898755687/8U02d0wug-3MkVax0xMmRoj0s_-V1psnNLPWdSOjnGnKRBUpPjaU6XiX9Iu8DgJI69AP"
+HASH=$(git log -1 --pretty=format:"%h" 2>/dev/null || echo "?")
+MSG=$(git log -1 --pretty=format:"%s" 2>/dev/null || echo "?")
+PAYLOAD=$(python3 -c "import json,sys; print(json.dumps({'content':sys.argv[1]}))" \
+    "🚀 **Kipinä Studio julkaistu!** \`${HASH}\` ${MSG} https://kipina.studio")
+curl -sf -H "Content-Type: application/json" -d "$PAYLOAD" "$WEBHOOK" >/dev/null || true
+
+echo "=== Valmis! https://kipina.studio ==="

network-poc/deploy.sh

@@ -1,70 +0,0 @@
-#!/bin/bash
-set -e
-
-if [ "$1" == "local" ]; then
-    echo "=== Kipinä Studio Local Development ==="
-    echo "Käynnistetään kokonaisuus puhtaasti Docker-kontissa..."
-    docker compose up agentic-poc
-    exit 0
-fi
-
-SERVER="ubuntu@86.50.252.98"
-REMOTE_DIR="~/code/agentic-studio/network-poc"
-KEY="$HOME/.ssh/id_rsa"
-SSH_OPTS="-o StrictHostKeyChecking=no -i $KEY"
-
-# Varmistetaan, että SSH-avain on agentissa
-if ! ssh-add -l 2>/dev/null | grep -q id_rsa; then
-    echo "SSH-avain ei ole agentissa. Lisätään..."
-    ssh-add "$KEY"
-fi
-
-echo "=== Kipinä Studio Deploy ==="
-
-# 0. Commitoidaan uncommitted muutokset ennen deployta
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-if ! git -C "$SCRIPT_DIR" diff --quiet HEAD 2>/dev/null || \
-   [ -n "$(git -C "$SCRIPT_DIR" ls-files --others --exclude-standard 2>/dev/null)" ]; then
-    echo "[0] Uncommitted muutoksia havaittu — commitoidaan..."
-    read -rp "    Commit-viesti: " DEPLOY_MSG
-    if [ -z "$DEPLOY_MSG" ]; then
-        DEPLOY_MSG="Deploy $(date +%Y-%m-%d\ %H:%M)"
-    fi
-    git -C "$SCRIPT_DIR" add -A
-    git -C "$SCRIPT_DIR" commit -m "$DEPLOY_MSG"
-    echo "    Commitoitu: $DEPLOY_MSG"
-fi
-
-# 1. Rakennetaan Docker-image lokaalisti
-echo "[1/4] Rakennetaan image lokaalisti..."
-docker build --platform linux/amd64 -f Dockerfile.prod -t kipina-agentic:latest .
-
-# 2. Tallennetaan tiedostoon
-echo "[2/5] Pakataan image..."
-docker save kipina-agentic:latest | gzip > /tmp/kipina-agentic.tar.gz
-echo "       Koko: $(du -h /tmp/kipina-agentic.tar.gz | cut -f1)"
-
-# 3. Siirretään palvelimelle
-echo "[3/5] Siirretään palvelimelle..."
-scp $SSH_OPTS /tmp/kipina-agentic.tar.gz $SERVER:/tmp/
-scp $SSH_OPTS docker-compose.prod.yml Caddyfile.prod $SERVER:$REMOTE_DIR/
-
-# 4. Ladataan image ja käynnistetään
-echo "[4/5] Ladataan image palvelimella..."
-ssh $SSH_OPTS $SERVER "gunzip -c /tmp/kipina-agentic.tar.gz | docker load && rm /tmp/kipina-agentic.tar.gz"
-
-echo "[5/5] Käynnistetään palvelut uudelleen..."
-ssh $SSH_OPTS $SERVER "cd $REMOTE_DIR && docker compose -f docker-compose.prod.yml down && docker compose -f docker-compose.prod.yml up -d"
-
-echo "=== Valmis! https://kipina.studio ==="
-
-# Discord-notifikaatio
-DISCORD_WEBHOOK="https://discord.com/api/webhooks/1489504066898755687/8U02d0wug-3MkVax0xMmRoj0s_-V1psnNLPWdSOjnGnKRBUpPjaU6XiX9Iu8DgJI69AP"
-COMMIT_HASH=$(git -C "$SCRIPT_DIR" log -1 --pretty=format:"%h" 2>/dev/null || echo "?")
-COMMIT_MSG=$(git -C "$SCRIPT_DIR" log -1 --pretty=format:"%s" 2>/dev/null || echo "?")
-# python3 escapettaa erikoismerkit JSON-turvallisesti
-PAYLOAD=$(python3 -c "import json,sys; print(json.dumps({'content': sys.argv[1]}))" \
-  "🚀 **Kipinä Studio julkaistu!**
-> \`${COMMIT_HASH}\` ${COMMIT_MSG}
-> https://kipina.studio")
-curl -s -H "Content-Type: application/json" -d "$PAYLOAD" "$DISCORD_WEBHOOK" > /dev/null

network-poc/docker-compose.prod.yml

@@ -24,7 +24,6 @@ services:
       - NODE_API_KEY=${NODE_API_KEY:-}
     volumes:
       - hub_data:/data
-      - ./frontend/dist:/app/frontend/dist:ro
 
 volumes:
   caddy_data:

network-poc/frontend/public/download/.build-hash

@@ -0,0 +1 @@
+dirty-3e9cdd70c60dadfb970cee47ebbd912c

network-poc/frontend/public/kipina-node

@@ -4,7 +4,6 @@ set -e
 
 BASE_URL="https://kipina.studio/download"
 HUB_URL="${KIPINA_HUB:-wss://kipina.studio/ws}"
-MODEL="${KIPINA_MODEL:-qwen2.5-coder:3b}"
 OLLAMA_URL="${OLLAMA_URL:-http://localhost:11434}"
 
 # Tunnista OS ja arkkitehtuuri
@@ -96,26 +95,37 @@ fi
 echo ""
 echo "  Hub: $HUB_URL"
 echo "  Ollama: $OLLAMA_URL"
-echo "  Malli: $MODEL"
+if [ -n "$KIPINA_MODEL" ]; then
-
+    echo "  Malli: $KIPINA_MODEL (Ympäristömuuttujasta)"
-# Lataa malli (toimii sekä lokaalilla binäärillä että API:n kautta)
-if ! curl -s "$OLLAMA_URL/api/tags" | grep -q "$MODEL"; then
-    echo "  Ladataan $MODEL..."
-    curl -s "$OLLAMA_URL/api/pull" -d "{\"name\":\"$MODEL\"}" > /dev/null
 fi
-echo "  ✓ Malli $MODEL valmis"
 
 # Lataa binääri
 BIN_PATH="./kipina-node-bin"
+if [ -f "$BIN_PATH" ]; then
+    echo ""
+    read -p "  Löydettiin vanha kipina-node-bin lokaalisti. Haluatko poistaa sen ja ladata uusimman version? [Y/n] " -r DEL_CHOICE
+    if [[ "$DEL_CHOICE" =~ ^[Nn]$ ]]; then
+        echo "  ✓ Käytetään lokaalia versiota."
+    else
+        rm -f "$BIN_PATH"
+        echo "  ✓ Vanha binääri poistettu ja korvataan uudella."
+    fi
+fi
+
 if [ ! -f "$BIN_PATH" ]; then
-    echo "  Ladataan $BINARY..."
+    echo "  Ladataan tuorein $BINARY..."
-    curl -sSL "$BASE_URL/$BINARY" -o "$BIN_PATH"
+    curl -sSL "$BASE_URL/$BINARY?v=$(date +%s)" -o "$BIN_PATH"
     chmod +x "$BIN_PATH"
 fi
 
 echo ""
-echo "  ✓ Yhdistetään laskentaverkkoon..."
+echo "  ✓ Siirrytään Kipinä Noden hallintaan..."
 echo "    Ctrl+C pysäyttää"
 echo ""
 
-HUB_URL="$HUB_URL" OLLAMA_URL="$OLLAMA_URL" OLLAMA_MODEL="$MODEL" exec "$BIN_PATH"
+if [ -n "$KIPINA_MODEL" ]; then
+    export OLLAMA_MODEL="$KIPINA_MODEL"
+fi
+export HUB_URL="$HUB_URL"
+export OLLAMA_URL="$OLLAMA_URL"
+exec "$BIN_PATH"

network-poc/frontend/public/templates/data-analytics.json

@@ -0,0 +1,33 @@
+{
+  "name": "Data Analytics Pipeline",
+  "description": "ETL, analysis, and visualization with Docker (MariaDB + Jupyter)",
+  "keywords": ["data", "analytics", "csv", "etl", "visualization", "statistics", "dashboard", "jupyter", "pandas", "matplotlib"],
+  "files": {
+    "etl.py": {
+      "description": "Data loading, cleaning, and transformation",
+      "example": "import pandas as pd\nfrom pathlib import Path\nfrom sqlalchemy import create_engine\n\nDB_URL = \"mysql+pymysql://root:secret@localhost:3306/analytics\"\nengine = create_engine(DB_URL)\n\ndef load_csv(path: str) -> pd.DataFrame:\n    df = pd.read_csv(path)\n    print(f\"Loaded {len(df)} rows from {path}\")\n    return df\n\ndef clean(df: pd.DataFrame) -> pd.DataFrame:\n    df = df.dropna(subset=[\"x\", \"y\"])\n    df = df[(df[\"x\"] >= 0) & (df[\"y\"] >= 0)]  # Remove outliers\n    df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n    return df.sort_values(\"timestamp\").reset_index(drop=True)\n\ndef to_database(df: pd.DataFrame, table: str):\n    df.to_sql(table, engine, if_exists=\"replace\", index=False)\n    print(f\"Wrote {len(df)} rows to {table}\")\n\nif __name__ == \"__main__\":\n    for csv_file in sorted(Path(\"data\").glob(\"*.csv\")):\n        df = load_csv(str(csv_file))\n        df = clean(df)\n        to_database(df, \"measurements\")",
+      "instructions": "Write the ETL pipeline:\n- Load CSV files from data/ directory using pandas\n- Clean: remove nulls, filter outliers, parse timestamps\n- Transform: convert units, compute derived columns\n- Load into MariaDB via SQLAlchemy\n- Make it runnable as a standalone script"
+    },
+    "analysis.py": {
+      "description": "Statistical analysis and metrics computation",
+      "example": "import pandas as pd\nfrom sqlalchemy import create_engine\n\nDB_URL = \"mysql+pymysql://root:secret@localhost:3306/analytics\"\nengine = create_engine(DB_URL)\n\ndef load_data() -> pd.DataFrame:\n    return pd.read_sql(\"SELECT * FROM measurements\", engine)\n\ndef summary_stats(df: pd.DataFrame) -> dict:\n    return {\n        \"total_rows\": len(df),\n        \"date_range\": f\"{df['timestamp'].min()} to {df['timestamp'].max()}\",\n        \"unique_entities\": df[\"entity_id\"].nunique(),\n    }\n\ndef hourly_distribution(df: pd.DataFrame) -> pd.DataFrame:\n    df[\"hour\"] = df[\"timestamp\"].dt.hour\n    return df.groupby(\"hour\").size().reset_index(name=\"count\")\n\nif __name__ == \"__main__\":\n    df = load_data()\n    stats = summary_stats(df)\n    for k, v in stats.items():\n        print(f\"{k}: {v}\")",
+      "instructions": "Write analysis functions:\n- Load cleaned data from MariaDB\n- Compute summary statistics (counts, date ranges, distributions)\n- Time-based analysis (hourly, daily, weekly patterns)\n- Group-level metrics (per entity, per zone)\n- Return DataFrames and dicts suitable for visualization"
+    },
+    "visualize.py": {
+      "description": "Charts and visualizations with matplotlib",
+      "example": "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom analysis import load_data, hourly_distribution\n\ndef plot_heatmap(df: pd.DataFrame, title: str, output: str):\n    fig, ax = plt.subplots(figsize=(12, 8))\n    scatter = ax.scatter(df[\"x\"], df[\"y\"], c=df[\"density\"], cmap=\"hot\", alpha=0.5, s=2)\n    ax.set_title(title)\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.invert_yaxis()\n    plt.colorbar(scatter, label=\"Density\")\n    plt.tight_layout()\n    plt.savefig(output, dpi=150)\n    print(f\"Saved {output}\")\n\ndef plot_bar(df: pd.DataFrame, x: str, y: str, title: str, output: str):\n    fig, ax = plt.subplots(figsize=(10, 5))\n    ax.bar(df[x], df[y], color=\"steelblue\")\n    ax.set_title(title)\n    ax.set_xlabel(x)\n    ax.set_ylabel(y)\n    plt.tight_layout()\n    plt.savefig(output, dpi=150)\n\nif __name__ == \"__main__\":\n    df = load_data()\n    hourly = hourly_distribution(df)\n    plot_bar(hourly, \"hour\", \"count\", \"Hourly Distribution\", \"output/hourly.png\")",
+      "instructions": "Write visualization functions:\n- Import analysis functions for data\n- Heatmaps, bar charts, line charts as appropriate\n- Save figures to output/ directory (PNG, 150 DPI)\n- Use matplotlib with clear titles, labels, colorbars\n- Make it runnable as standalone to generate all charts"
+    },
+    "docker-compose.yml": {
+      "description": "Docker Compose stack for database and Jupyter",
+      "example": "services:\n  db:\n    image: mariadb:11\n    environment:\n      MYSQL_ROOT_PASSWORD: secret\n      MYSQL_DATABASE: analytics\n    ports:\n      - \"3306:3306\"\n    volumes:\n      - db_data:/var/lib/mysql\n\n  jupyter:\n    image: jupyter/scipy-notebook:latest\n    ports:\n      - \"8888:8888\"\n    volumes:\n      - .:/home/jovyan/work\n    environment:\n      JUPYTER_TOKEN: kipina\n    depends_on:\n      - db\n\nvolumes:\n  db_data:",
+      "instructions": "Write docker-compose.yml:\n- MariaDB service with persistent volume\n- JupyterLab service with project mounted\n- Correct environment variables\n- Port mappings for local development\n- Write ONLY the YAML, no explanations"
+    },
+    "pyproject.toml": {
+      "description": "Project dependencies",
+      "example": "[project]\nname = \"analytics\"\nversion = \"0.1.0\"\nrequires-python = \">=3.11\"\ndependencies = [\n    \"pandas\",\n    \"matplotlib\",\n    \"sqlalchemy\",\n    \"pymysql\",\n]\n\n[project.scripts]\netl = \"python etl.py\"\nanalyze = \"python analysis.py\"\nvisualize = \"python visualize.py\"",
+      "instructions": "Use [project] format (PEP 621). List all data science dependencies. Add scripts for ETL, analysis, and visualization."
+    }
+  },
+  "order": ["etl.py", "analysis.py", "visualize.py", "docker-compose.yml", "pyproject.toml"]
+}

network-poc/frontend/public/templates/fastapi-crud.json

@@ -1,6 +1,7 @@
 {
   "name": "FastAPI CRUD",
   "description": "REST API with SQLite database",
+  "keywords": ["api", "rest", "crud", "endpoint", "fastapi", "web", "backend", "server", "database", "sqlite"],
   "files": {
     "models.py": {
       "description": "SQLAlchemy models, engine, and session",

network-poc/frontend/src/components/StatusBar.astro

@@ -40,8 +40,8 @@
     <div style="padding:12px;background:var(--bg);border-radius:4px;border-left:3px solid var(--green)">
         <div style="color:#e6edf3;font-weight:600;margin-bottom:6px">2. Käynnistä Kipinä-node</div>
         <div style="display:flex;gap:6px;align-items:center;margin-bottom:6px">
-            <code style="flex:1;background:#010409;padding:8px 12px;border-radius:4px;color:var(--green);font-family:'Courier New',monospace;font-size:13px;user-select:all">curl -sSL https://kipina.studio/kipina-node -o kipina-node && chmod +x kipina-node && ./kipina-node</code>
+            <code style="flex:1;background:#010409;padding:8px 12px;border-radius:4px;color:var(--green);font-family:'Courier New',monospace;font-size:13px;user-select:all">curl -sSL "https://kipina.studio/kipina-node?v=$(date +%s)" -o kipina-node && chmod +x kipina-node && ./kipina-node</code>
-            <button onclick="navigator.clipboard.writeText('curl -sSL https://kipina.studio/kipina-node -o kipina-node && chmod +x kipina-node && ./kipina-node');this.textContent='✓';setTimeout(()=>this.textContent='Kopioi',1500)" class="btn btn-green" style="padding:6px 10px">Kopioi</button>
+            <button onclick="navigator.clipboard.writeText('curl -sSL &quot;https://kipina.studio/kipina-node?v=$(date +%s)&quot; -o kipina-node && chmod +x kipina-node && ./kipina-node');this.textContent='✓';setTimeout(()=>this.textContent='Kopioi',1500)" class="btn btn-green" style="padding:6px 10px">Kopioi</button>
         </div>
         <div style="color:#8b949e;font-size:12px">Lataa kielimallin (~2GB) automaattisesti ensimmäisellä kerralla. Ctrl+C pysäyttää.</div>
     </div>

network-poc/frontend/src/pages/index.astro

@@ -71,7 +71,25 @@ import Settings from "../components/Settings.astro";
 
     // === Globaalit tilat ===
     const defaultAgents = {
-        manager: { name: 'Manageri', avatar: '/avatars/karhunpentu.webp', model: 'qwen-coder', order: 0,
+        client: { name: 'Asiakas', avatar: '/avatars/kettu_notext.webp', model: 'qwen-coder', order: 0,
+            temperature: 0.6, topK: 40, repeatPenalty: 1.15, maxTokens: 512,
+            prompt: `You are a product owner who turns vague ideas into clear, actionable software requirements.
+
+GIVEN a short project description from the user, produce a structured brief:
+
+1. PROJECT NAME: a short, descriptive name
+2. GOAL: one sentence explaining what the software does and who it's for
+3. CORE FEATURES: numbered list of 3-5 concrete features (not vague wishes)
+4. DATA MODEL: list the main entities and their key fields
+5. API ENDPOINTS: list the essential REST endpoints (method + path + purpose)
+6. CONSTRAINTS: any technical constraints (e.g. "must use SQLite", "no auth needed for MVP")
+
+RULES:
+- Be specific: "User can filter todos by status" not "todo management"
+- Keep scope small — MVP only, no nice-to-haves
+- Use plain English, no code
+- Maximum 200 words total` },
+        manager: { name: 'Manageri', avatar: '/avatars/karhunpentu.webp', model: 'qwen-coder', order: 1,
             temperature: 0.5, topK: 40, repeatPenalty: 1.15, maxTokens: 512,
             prompt: `You are a senior project manager and software architect. Your job is to plan the file structure of a software project.
 
@@ -88,7 +106,7 @@ models.py: SQLAlchemy database models and engine setup
 schemas.py: Pydantic request/response schemas
 main.py: FastAPI application with CRUD endpoints
 pyproject.toml: project dependencies` },
-        coder: { name: 'Koodari', avatar: '/avatars/kipina_notext.webp', model: 'qwen-coder', order: 1,
+        coder: { name: 'Koodari', avatar: '/avatars/kipina_notext.webp', model: 'qwen-coder', order: 2,
             temperature: 0.7, topK: 40, repeatPenalty: 1.15, maxTokens: 1024,
             prompt: `You are an expert Python developer. Write complete, production-ready code.
 
@@ -109,7 +127,7 @@ NEVER:
 - Forget to import from other project files
 - Use requirements.txt or Poetry — always use pyproject.toml with [project] format (PEP 621)
 - Use pip install — use uv (e.g. uv run uvicorn main:app --reload)` },
-        data: { name: 'Data', avatar: '/avatars/pesukarhu_notext.webp', model: 'qwen-coder', order: 2,
+        data: { name: 'Data', avatar: '/avatars/pesukarhu_notext.webp', model: 'qwen-coder', order: 3,
             temperature: 0.5, topK: 40, repeatPenalty: 1.15, maxTokens: 1024,
             prompt: `You are a database architect specializing in SQLAlchemy and relational databases.
 
@@ -126,7 +144,7 @@ ALWAYS INCLUDE:
 - from sqlalchemy.ext.declarative import declarative_base
 - from sqlalchemy.orm import sessionmaker
 - DATABASE_URL, engine, SessionLocal, Base` },
-        qa: { name: 'QA', avatar: '/avatars/susi_notext.webp', model: 'qwen-coder', order: 3,
+        qa: { name: 'QA', avatar: '/avatars/susi_notext.webp', model: 'qwen-coder', order: 4,
             temperature: 0.4, topK: 40, repeatPenalty: 1.15, maxTokens: 1024,
             prompt: `You are a QA engineer writing automated tests.
 
@@ -143,7 +161,7 @@ TEST STRUCTURE:
 5. test_delete: DELETE → 204, verify GET returns 404 after
 
 ALWAYS: from fastapi.testclient import TestClient` },
-        tester: { name: 'DevOps', avatar: '/avatars/laiskiainen_notext.webp', model: 'qwen-coder', order: 4,
+        tester: { name: 'DevOps', avatar: '/avatars/laiskiainen_notext.webp', model: 'qwen-coder', order: 5,
             temperature: 0.3, topK: 40, repeatPenalty: 1.1, maxTokens: 512,
             prompt: `You are a strict code reviewer and static analysis expert. Analyze the code line by line.
 
@@ -162,7 +180,7 @@ RESPOND:
 - If all checks pass: "LGTM"
 - If issues found: list each as "ISSUE: filename.py: description"
 - Be specific and actionable, not vague` },
-        observer: { name: 'Tarkkailija', avatar: '/avatars/aikuinen_susi.webp', model: 'qwen-coder', order: 5,
+        observer: { name: 'Tarkkailija', avatar: '/avatars/aikuinen_susi.webp', model: 'qwen-coder', order: 6,
             temperature: 0.6, topK: 40, repeatPenalty: 1.15, maxTokens: 512,
             prompt: `You are an independent technical observer and risk analyst.
 
@@ -178,7 +196,7 @@ OUTPUT FORMAT:
 - End with overall assessment: "SHIP IT" or "NEEDS WORK: reason"` },
     };
     // Versio: kasvata kun oletuspromptit muuttuvat
-    const AGENTS_VERSION = 2;
+    const AGENTS_VERSION = 3;
     let agents;
     const savedVersion = parseInt(localStorage.getItem('kpn-agents-version') || '0');
     if (savedVersion < AGENTS_VERSION && localStorage.getItem('kpn-agents')) {
@@ -483,10 +501,16 @@ OUTPUT FORMAT:
 
     // Wasm-autostart vain jos natiivisolmua ei löydy (tarkistetaan onopen:ssa)
 
+    // === Pipeline-keskeytys ===
+    let pipelineAbort = null; // AbortController tai null
+
     // === kpnRun: lähettää promptin mallille ===
     const activeStreams = {};
 
-    async function kpnRun(model, prompt, silent) {
+    async function kpnRun(model, prompt, silent, agentOpts) {
+        // Tarkistetaan keskeytys
+        if (pipelineAbort?.signal?.aborted) return null;
+
         const taskId = crypto.randomUUID();
         const statusDiv = document.createElement('div');
         statusDiv.className = 'terminal-line';
@@ -496,10 +520,6 @@ OUTPUT FORMAT:
         termPanel.scrollTop = termPanel.scrollHeight;
 
         try {
-            // Ei odotetaan Wasmia — lähetetään suoraan hubille.
-            // Jos hub löytää natiivisolmun, vastaus tulee nopeasti.
-            // Jos 503, käynnistetään Wasm-fallback.
-
             if (!silent) {
                 const streamDiv = document.createElement('div');
                 streamDiv.className = 'terminal-line';
@@ -511,10 +531,24 @@ OUTPUT FORMAT:
 
             statusDiv.innerHTML = `  <span style="color:#8b949e">→ <span style="color:var(--accent)">${model}</span> käsittelee...</span>`;
 
+            // Rakennetaan pyyntö: agentin asetukset tai globaalit oletukset
+            const opts = agentOpts || {};
+            const payload = {
+                model,
+                prompt,
+                task_id: taskId,
+                system_prompt: opts.prompt || settings.systemPrompt || undefined,
+                temperature: opts.temperature ?? settings.temperature ?? undefined,
+                top_k: opts.topK ?? settings.topK ?? undefined,
+                max_tokens: opts.maxTokens ?? settings.maxTokens ?? undefined,
+                repeat_penalty: opts.repeatPenalty ?? settings.repeatPenalty ?? undefined,
+            };
+
             const res = await fetch('/api/v1/chat/completions', {
                 method: 'POST',
                 headers: { 'Content-Type': 'application/json' },
-                body: JSON.stringify({ model, prompt, task_id: taskId }),
+                body: JSON.stringify(payload),
+                signal: pipelineAbort?.signal,
             });
 
             if (res.status === 503 && !wasmNodeStarted) {
@@ -578,8 +612,8 @@ OUTPUT FORMAT:
 
     // === Terminal commands ===
     const kpnCommands = {
-        'kpn': ['help','run','project','pipeline','load','status','models','clear'],
+        'kpn': ['help','run','project','pipeline','stop','load','status','models','clear'],
-        'kpn run': ['coder','coder-3b','manager','tester','qa','qwen-coder','smollm-135m'],
+        'kpn run': ['coder','coder-3b','manager','tester','qa','qwen-coder'],
         'kpn load': ['1','2'],
         'kpn project': ['"'],
         'kpn pipeline': ['"'],
@@ -587,7 +621,7 @@ OUTPUT FORMAT:
     const kpnExamples = {
         'kpn run coder': ['"hello world in python"','"fibonacci in rust"','"quicksort in javascript"'],
         'kpn run coder-3b': ['"REST API with Flask"','"binary search tree"'],
-        'kpn project': ['"FastAPI + SQLite REST API"','"CLI tool for CSV processing"'],
+        'kpn project': ['"FastAPI + SQLite REST API"','"UWB indoor positioning analytics — CSV cart tracking data, heatmaps, statistics, Docker (MariaDB + Jupyter)"','"CLI tool for CSV processing"'],
         'kpn pipeline': ['"todo-sovellus"','"laskin pythonilla"'],
     };
 
@@ -648,20 +682,29 @@ OUTPUT FORMAT:
         if (sub === 'help' || !sub) {
             termLog('  kpn run &lt;malli&gt; "prompti"  — aja tehtävä', '#a5d6ff');
             termLog('  kpn project "kuvaus"        — monivaiheinen projekti', '#a5d6ff');
-            termLog('  kpn pipeline "tehtävä"      — nopea: manageri→koodari→testaaja', '#a5d6ff');
+            termLog('  kpn pipeline "tehtävä"      — nopea: asiakas→manageri→koodari→testaaja', '#a5d6ff');
+            termLog('  kpn stop                    — keskeytä pipeline', '#a5d6ff');
             termLog('  kpn load                    — lataa kielimalli', '#a5d6ff');
             termLog('  kpn models                  — mallit', '#a5d6ff');
             termLog('  kpn status                  — verkon tila', '#a5d6ff');
             termLog('  kpn clear                   — tyhjennä', '#a5d6ff');
+        } else if (sub === 'stop') {
+            if (pipelineAbort) {
+                pipelineAbort.abort();
+                pipelineAbort = null;
+                termLog('  ✋ Pipeline keskeytetty', '#d29922');
+                highlightAgent(null);
+            } else {
+                termLog('  Ei käynnissä olevaa pipelinea', '#8b949e');
+            }
         } else if (sub === 'clear') { termPanel.innerHTML = '';
         } else if (sub === 'load') {
             const btn = document.getElementById('compute-btn');
             if (btn && btn.textContent.includes('Valmis')) { termLog('  ✓ Malli jo ladattu', '#3fb950'); }
             else { btn?.click(); }
         } else if (sub === 'models') {
-            termLog('  <span style="color:var(--accent)">1</span>  qwen-coder     Qwen2.5-Coder:0.5B  <span style="color:#8b949e">~990 MB</span>');
+            termLog('  <span style="color:var(--accent)">1</span>  qwen-coder     Qwen2.5-Coder:0.5B  <span style="color:#8b949e">~990 MB (selain)</span>');
-            termLog('  <span style="color:var(--accent)">2</span>  qwen-coder-3b  Qwen2.5-Coder:3B    <span style="color:#8b949e">~6.2 GB</span>');
+            termLog('  <span style="color:var(--accent)">2</span>  qwen-coder-3b  Qwen2.5-Coder:3B    <span style="color:#8b949e">~6.2 GB (Ollama)</span>');
-            termLog('  <span style="color:var(--accent)">3</span>  smollm-135m    SmolLM 135M         <span style="color:#8b949e">~270 MB</span>');
         } else if (sub === 'status') {
             termLog(`  Hub: ${document.getElementById('hub-label').textContent} | Laskenta: ${document.getElementById('compute-label').textContent}`, '#a5d6ff');
         } else if (sub === 'run') {
@@ -721,62 +764,116 @@ OUTPUT FORMAT:
 
     // === Template-pohjainen projektipipeline ===
     let templates = {};
+    const TEMPLATE_FILES = ['fastapi-crud.json', 'data-analytics.json'];
 
     // Ladataan mallipohjat
     (async () => {
-        try {
+        for (const file of TEMPLATE_FILES) {
-            const res = await fetch('/templates/fastapi-crud.json');
+            try {
-            if (res.ok) { const t = await res.json(); templates[t.name] = t; }
+                const res = await fetch(`/templates/${file}`);
-        } catch(e) {}
+                if (res.ok) { const t = await res.json(); templates[t.name] = t; }
+            } catch(e) {}
+        }
     })();
 
+    // Valitaan mallipohja Asiakkaan briefin perusteella (keywords-match)
+    function selectTemplate(brief) {
+        const lower = brief.toLowerCase();
+        let best = null, bestScore = 0;
+        for (const t of Object.values(templates)) {
+            const keywords = t.keywords || [];
+            const score = keywords.filter(k => lower.includes(k)).length;
+            if (score > bestScore) { bestScore = score; best = t; }
+        }
+        return best; // null = vapaa tila
+    }
+
     function explainStep(title, explanation) {
         termLog(`\n  <span style="color:#a371f7;font-size:12px">💡 ${esc(title)}</span>`);
         termLog(`  <span style="color:#8b949e;font-size:12px">${esc(explanation)}</span>`);
     }
 
     async function kpnProject(task) {
-        const cdr = agents.coder || Object.values(agents)[1];
+        pipelineAbort = new AbortController();
+        const cli = agents.client || Object.values(agents)[0];
+        const mgr = agents.manager || Object.values(agents)[1];
+        const cdr = agents.coder || Object.values(agents)[2];
 
-        // Etsitään sopivin mallipohja
+        // Asiakas: jalostaa vaatimukset
-        const template = Object.values(templates)[0]; // Toistaiseksi vain FastAPI CRUD
+        termLog(`<span style="color:var(--purple);font-weight:bold">━━━ Projekti — ${esc(task)} ━━━</span>`);
-        if (!template) {
+        termLog(`\n<span style="color:#f0883e;font-weight:bold">[0] ${esc(cli.name)}</span> — vaatimusmäärittely`);
-            termLog('  ✗ Mallipohjia ei ladattu', '#f85149');
+        highlightAgent('client');
-            return;
+        explainStep('Vaatimusmäärittely', `${cli.name} muotoilee idean selkeiksi vaatimuksiksi: ominaisuudet, datamallit, rajapinnat.`);
+        const brief = await kpnRun(cli.model, `${task}`, false, cli);
+        if (!brief) { termLog('  ✗ Vaatimusmäärittely epäonnistui', '#f85149'); return; }
+        termLog(`  <span style="color:#8b949e">Vaatimukset valmiit → Manageri</span>`);
+
+        // Valitaan mallipohja automaattisesti briefin perusteella
+        const template = selectTemplate(brief);
+
+        // Tiedostolista: mallipohjasta tai managerin dynaamisesta suunnitelmasta
+        let fileOrder = [];
+        let fileDefs = {};
+
+        if (template) {
+            // Mallipohja löytyi — käytetään sen rakennetta
+            fileOrder = template.order;
+            fileDefs = template.files;
+            explainStep('Mallipohja', `Tunnistettiin "${template.name}" — ${fileOrder.length} tiedostoa: ${fileOrder.join(', ')}.`);
+        } else {
+            // Vapaa tila — Manageri päättää tiedostorakenteen
+            termLog(`\n<span style="color:#d29922;font-weight:bold">[1] ${esc(mgr.name)}</span> — tiedostorakenne`);
+            highlightAgent('manager');
+            explainStep('Vapaa tila', 'Sopivaa mallipohjaa ei löytynyt. Manageri suunnittelee tiedostorakenteen vaatimusten perusteella.');
+            const planPrompt = `PROJECT REQUIREMENTS:\n${brief}\n\nPlan the file structure for this project. List each file on its own line:\nfilename.ext: one-line description\n\nMaximum 6 files. List dependency files first.`;
+            const plan = await kpnRun(mgr.model, planPrompt, false, mgr);
+            if (!plan) { termLog('  ✗ Suunnittelu epäonnistui', '#f85149'); return; }
+
+            // Parsitaan managerin tuottama tiedostolista
+            for (const line of plan.split('\n')) {
+                const m = line.match(/^\s*[-*]?\s*(\S+\.\w+)\s*[:\-–]\s*(.+)/);
+                if (m) {
+                    const fname = m[1].replace(/^`|`$/g, '');
+                    fileOrder.push(fname);
+                    fileDefs[fname] = { description: m[2].trim(), instructions: m[2].trim() };
+                }
+            }
+            if (fileOrder.length === 0) {
+                termLog('  ✗ Manageri ei tuottanut tiedostolistaa', '#f85149');
+                return;
+            }
+            explainStep('Suunnitelma', `${fileOrder.length} tiedostoa: ${fileOrder.join(', ')}`);
         }
 
-        termLog(`<span style="color:var(--purple);font-weight:bold">━━━ ${esc(template.name)} — ${esc(task)} ━━━</span>`);
-        explainStep('Mallipohja', `Käytetään "${template.name}" -mallipohjaa jossa ${template.order.length} tiedostoa: ${template.order.join(', ')}. Jokainen tiedosto generoidaan järjestyksessä, ja aiemmat tiedostot annetaan kontekstina seuraavalle.`);
-
         const files = {};
 
-        for (let i = 0; i < template.order.length; i++) {
+        for (let i = 0; i < fileOrder.length; i++) {
-            const fileName = template.order[i];
+            const fileName = fileOrder[i];
-            const fileDef = template.files[fileName];
+            const fileDef = fileDefs[fileName];
             if (!fileDef) continue;
 
             const step = i + 1;
             // Valitaan oikea agentti tiedostotyypin mukaan
-            const isDbFile = fileName === 'models.py' || fileName === 'database.py';
+            const isDbFile = fileName === 'models.py' || fileName === 'database.py' || fileName === 'etl.py';
-            const dataAgent = agents.data || Object.values(agents)[2];
+            const dataAgent = agents.data || Object.values(agents)[3];
             const fileAgent = isDbFile && dataAgent ? dataAgent : cdr;
             const fileAgentKey = isDbFile && dataAgent ? 'data' : 'coder';
 
-            termLog(`\n<span style="color:#3fb950;font-weight:bold">[${step}/${template.order.length}] ${esc(fileAgent.name)}</span> — ${esc(fileName)}`);
+            termLog(`\n<span style="color:#3fb950;font-weight:bold">[${step}/${fileOrder.length}] ${esc(fileAgent.name)}</span> — ${esc(fileName)}`);
             highlightAgent(fileAgentKey);
 
-            // Opettava selitys: miksi tämä tiedosto, mitä se sisältää
+            explainStep(fileName, fileDef.instructions || fileDef.description);
-            explainStep(fileName, fileDef.instructions);
 
-            // Rakennetaan prompti: esimerkki + konteksti + ohje
+            // Rakennetaan prompti
             let prompt = '';
 
-            // Agentin system prompt (data-agentti models.py:lle, koodari muille)
             if (fileAgent.prompt) prompt += fileAgent.prompt + '\n\n';
 
-            // Esimerkki (few-shot)
+            // Esimerkki (vain mallipohjatilassa)
-            prompt += `EXAMPLE of ${fileName} (for a different project, adapt to this one):\n`;
+            if (fileDef.example) {
-            prompt += '```\n' + fileDef.example + '\n```\n\n';
+                prompt += `EXAMPLE of ${fileName} (for a different project, adapt to this one):\n`;
+                prompt += '```\n' + fileDef.example + '\n```\n\n';
+            }
 
             // Aiemmin generoidut tiedostot (konteksti)
             const prevFiles = Object.entries(files);
@@ -787,12 +884,15 @@ OUTPUT FORMAT:
                 }
             }
 
+            // Asiakkaan vaatimusmäärittely
+            prompt += `PROJECT REQUIREMENTS (from product owner):\n${brief}\n\n`;
+
             // Tehtävä
             prompt += `NOW write "${fileName}" for THIS project: ${task}\n`;
-            prompt += fileDef.instructions + '\n';
+            if (fileDef.instructions) prompt += fileDef.instructions + '\n';
-            prompt += 'Adapt the example to match the project description. Import from already written files. Write ONLY the code, no explanations.';
+            prompt += 'Adapt to the project requirements. Import from already written files. Write ONLY the code, no explanations.';
 
-            const code = await kpnRun(fileAgent.model, prompt);
+            const code = await kpnRun(fileAgent.model, prompt, false, fileAgent);
             if (!code) {
                 termLog(`  ✗ Keskeytyi (${fileName})`, '#f85149');
                 return;
@@ -804,7 +904,7 @@ OUTPUT FORMAT:
         let stepN = template.order.length + 1;
 
         // Review-korjausluuppi: max 2 kierrosta
-        const tst = agents.tester || Object.values(agents)[4];
+        const tst = agents.tester || Object.values(agents)[5];
         const MAX_REVIEW_ROUNDS = 3;
 
         for (let round = 0; round < MAX_REVIEW_ROUNDS; round++) {
@@ -817,7 +917,7 @@ OUTPUT FORMAT:
             else explainStep('Uudelleentarkistus', `${tst.name} tarkistaa korjaukset.`);
 
             const reviewPrompt = (tst.prompt ? tst.prompt+'\n\n' : '') + `Review this project:\n\n${currentCode}`;
-            const review = await kpnRun(tst.model, reviewPrompt);
+            const review = await kpnRun(tst.model, reviewPrompt, false, tst);
             stepN++;
 
             // LGTM → ei korjauksia tarvita
@@ -832,7 +932,7 @@ OUTPUT FORMAT:
             explainStep('Korjaus', `${tst.name} löysi ongelmia. ${cdr.name} saa palautteen ja korjaa.`);
 
             const fixPrompt = `${cdr.prompt ? cdr.prompt+'\n\n' : ''}Fix these issues:\n${review}\n\nCurrent code:\n${currentCode}\n\nWrite ALL corrected files. Start each file with: --- filename.py ---`;
-            const fixedCode = await kpnRun(cdr.model, fixPrompt);
+            const fixedCode = await kpnRun(cdr.model, fixPrompt, false, cdr);
 
             // Parsitaan korjatut tiedostot takaisin files-objektiin
             if (fixedCode) {
@@ -852,13 +952,13 @@ OUTPUT FORMAT:
         const updatedCode = Object.entries(files).map(([n,c]) => `--- ${n} ---\n${c}`).join('\n\n');
 
         // QA: testit (saa korjatut tiedostot)
-        const qaAgent = agents.qa || Object.values(agents)[3];
+        const qaAgent = agents.qa || Object.values(agents)[4];
         if (qaAgent) {
             termLog(`\n<span style="color:#d2a8ff;font-weight:bold">[${stepN}] ${esc(qaAgent.name)}</span> — testit`);
             highlightAgent('qa');
             explainStep('Testit', `${qaAgent.name} kirjoittaa pytest-testit korjatulle koodille.`);
             const qaPrompt = (qaAgent.prompt ? qaAgent.prompt+'\n\n' : '') + `Write pytest tests for this project:\n\n${updatedCode}\n\nWrite a complete test_main.py file with TestClient.`;
-            const tests = await kpnRun(qaAgent.model, qaPrompt);
+            const tests = await kpnRun(qaAgent.model, qaPrompt, false, qaAgent);
             if (tests) files['test_main.py'] = tests;
             stepN++;
         }
@@ -878,12 +978,12 @@ OUTPUT FORMAT:
             `- Expose port 8000\n` +
             `- CMD: uv run uvicorn main:app --host 0.0.0.0 --port 8000\n` +
             `\nWrite ONLY the Dockerfile, no explanations.`;
-        const dockerfile = await kpnRun(tst.model, dockerPrompt);
+        const dockerfile = await kpnRun(tst.model, dockerPrompt, false, tst);
         if (dockerfile) files['Dockerfile'] = dockerfile;
         stepN++;
 
         // Tarkkailija: yhteenveto + raportti + arvosana
-        const obs = agents.observer || Object.values(agents)[5];
+        const obs = agents.observer || Object.values(agents)[6];
         if (obs) {
             termLog(`\n<span style="color:#8b949e;font-weight:bold">[${stepN}] ${esc(obs.name)}</span> — projektin yhteenveto`);
             highlightAgent('observer');
@@ -913,7 +1013,7 @@ OUTPUT FORMAT:
                 `## Architecture\nDescribe the project structure and design decisions.\n\n` +
                 `## Risk Assessment\n| Severity | Issue |\n|----------|-------|\n| ... | ... |\n\n` +
                 `Project code:\n${finalCode}`;
-            const readme = await kpnRun(obs.model, obsPrompt);
+            const readme = await kpnRun(obs.model, obsPrompt, false, obs);
             if (readme) {
                 files['README.md'] = readme;
                 // Tallennetaan raportti globaalisti jotta tarkkailija-klikkaus avaa sen
@@ -944,15 +1044,25 @@ OUTPUT FORMAT:
     }
 
     async function kpnPipelineSimple(task) {
+        pipelineAbort = new AbortController();
+        const cli = agents.client || Object.values(agents)[0];
         termLog(`<span style="color:var(--purple);font-weight:bold">━━━ Pipeline ━━━</span>`);
-        termLog(`\n<span style="color:#d29922;font-weight:bold">[1/3] Manageri</span>`);
+        termLog(`\n<span style="color:#f0883e;font-weight:bold">[1/4] ${esc(cli.name)}</span> — vaatimukset`);
-        const plan = await kpnRun('qwen-coder', `Analyse briefly, write a spec:\n${task}`);
+        highlightAgent('client');
+        const brief = await kpnRun(cli.model, `${task}`, false, cli);
+        if (!brief) return;
+        termLog(`\n<span style="color:#d29922;font-weight:bold">[2/4] Manageri</span>`);
+        highlightAgent('manager');
+        const plan = await kpnRun('qwen-coder', `Requirements:\n${brief}\n\nAnalyse briefly, write a spec:\n${task}`);
         if (!plan) return;
-        termLog(`\n<span style="color:#3fb950;font-weight:bold">[2/3] Koodari</span>`);
+        termLog(`\n<span style="color:#3fb950;font-weight:bold">[3/4] Koodari</span>`);
+        highlightAgent('coder');
         const code = await kpnRun('qwen-coder', `${plan}\n\nWrite the code.`);
         if (!code) return;
-        termLog(`\n<span style="color:var(--accent);font-weight:bold">[3/3] Testaaja</span>`);
+        termLog(`\n<span style="color:var(--accent);font-weight:bold">[4/4] Testaaja</span>`);
+        highlightAgent('tester');
         await kpnRun('qwen-coder', `Review briefly:\n${code}`);
+        highlightAgent(null);
         termLog(`\n<span style="color:var(--purple);font-weight:bold">━━━ Valmis ━━━</span>`);
     }
 

network-poc/hub-local.log

@@ -0,0 +1,34 @@
+   Compiling hub v0.3.1 (/Users/jaakko/code/kipina-codes/playground/agentic-studio/network-poc/hub)
+    Finished `dev` profile [unoptimized + debuginfo] target(s) in 2.95s
+     Running `target/debug/hub`
+2026-04-12T04:56:09.723604Z  INFO hub: Tietokanta alustettu
+2026-04-12T04:56:09.725088Z  INFO hub: Kipinä Agent Hub v0.3.1 käynnistyy osoitteessa http://localhost:3000
+2026-04-12T04:56:18.997935Z  INFO hub: Solmu 1 yhdistyi osoitteesta 127.0.0.1
+2026-04-12T04:56:19.027478Z  INFO hub: Solmu 1 (natiivi) | 127.0.0.1 | Mac | Darwin 26.3.1 | 12 ydintä | 32768 MB RAM | varaus: 4 GB
+2026-04-12T04:56:19.029931Z  INFO hub:   GPU 0: Apple M2 Max | VRAM: 0/24576 MB | 0°C | 0%
+2026-04-12T04:56:31.260470Z  INFO hub: Solmu 2 yhdistyi osoitteesta 127.0.0.1
+2026-04-12T04:56:31.281759Z  INFO hub: Solmu 2 (selain) | 127.0.0.1 | MacIntel | 11 ydintä | ~8 GB RAM | GPU: ei GPU:ta | tehtävä: viewer | varaus: 0 GB
+2026-04-12T04:56:31.283313Z  INFO hub: Reititettiin API-pyyntö solmulle 1 (Malli: qwen-coder)
+
+━━━ Solmu 1 ━━━ qwen2.5-coder:7b-instruct-q4_K_M (Ollama) ━━━
+  Prompt: "ping"
+  Vastaus: Pong! How can I assist you today?
+  11 tokenia | 4502ms | 56.3 tok/s
+2026-04-12T04:56:36.419646Z  INFO hub: Solmu 2 (127.0.0.1) poistui verkosta.
+2026-04-12T04:56:36.433155Z  INFO hub: Solmu 3 yhdistyi osoitteesta 127.0.0.1
+2026-04-12T04:56:36.445127Z  INFO hub: Solmu 3 (selain) | 127.0.0.1 | MacIntel | 11 ydintä | ~8 GB RAM | GPU: ei GPU:ta | tehtävä: viewer | varaus: 0 GB
+2026-04-12T04:56:36.445818Z  INFO hub: Reititettiin API-pyyntö solmulle 1 (Malli: qwen-coder)
+
+━━━ Solmu 1 ━━━ qwen2.5-coder:7b-instruct-q4_K_M (Ollama) ━━━
+  Prompt: "ping"
+  Vastaus: Pong! How can I assist you today? If you have any questions or need information on a specific topic, feel free to let me know.
+  31 tokenia | 679ms | 57.5 tok/s
+2026-04-12T04:56:39.466711Z  INFO hub: Solmu 3 (127.0.0.1) poistui verkosta.
+2026-04-12T04:56:43.881216Z  INFO hub: Solmu 4 yhdistyi osoitteesta 127.0.0.1
+2026-04-12T04:56:43.894385Z  INFO hub: Solmu 4 (selain) | 127.0.0.1 | MacIntel | 3 ydintä | ~16 GB RAM | GPU: ei GPU:ta | tehtävä: viewer | varaus: 0 GB
+2026-04-12T04:56:43.894960Z  INFO hub: Reititettiin API-pyyntö solmulle 1 (Malli: qwen-coder)
+
+━━━ Solmu 1 ━━━ qwen2.5-coder:7b-instruct-q4_K_M (Ollama) ━━━
+  Prompt: "ping"
+  Vastaus: Pong! How can I assist you today?
+  11 tokenia | 333ms | 58.7 tok/s

network-poc/hub/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "hub"
-version = "0.3.1"
+version = "0.3.2"
 edition = "2024"
 
 [dependencies]
@@ -11,7 +11,6 @@ serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
 tracing = "0.1"
 tracing-subscriber = { version = "0.3", features = ["env-filter"] }
-uuid = { version = "1.7.0", features = ["v4", "serde"] }
 futures = "0.3"
 rusqlite = { version = "0.31", features = ["bundled"] }
 chrono = "0.4"

network-poc/hub/src/db.rs

@@ -49,6 +49,13 @@ impl NodeDb {
                 INSERT INTO _schema_version VALUES (3);
             ");
         }
+        if version < 4 {
+            let _ = conn.execute_batch("
+                ALTER TABLE node_sessions ADD COLUMN is_paused BOOLEAN DEFAULT 0;
+                DELETE FROM _schema_version;
+                INSERT INTO _schema_version VALUES (4);
+            ");
+        }
 
         conn.execute_batch("
             CREATE TABLE IF NOT EXISTS node_sessions (
@@ -84,7 +91,10 @@ impl NodeDb {
                 has_webgpu BOOLEAN,
 
                 -- Tehtävätilastot
-                tasks_completed INTEGER DEFAULT 0
+                tasks_completed INTEGER DEFAULT 0,
+                
+                -- Ohjaustilat
+                is_paused BOOLEAN DEFAULT 0
             );
 
             CREATE TABLE IF NOT EXISTS pair_results (
@@ -183,6 +193,14 @@ impl NodeDb {
         );
     }
 
+    pub fn update_session_status(&self, node_id: u64, is_paused: bool) {
+        let conn = self.conn.lock().unwrap_or_else(|e| e.into_inner());
+        let _ = conn.execute(
+            "UPDATE node_sessions SET is_paused = ?1 WHERE node_id = ?2 AND disconnected_at IS NULL",
+            params![is_paused as i64, node_id as i64],
+        );
+    }
+
     /// Sulkee saman IP:n viewer-sessiot kun aktiivinen node liittyy
     pub fn close_viewers_by_ip(&self, ip: &str) {
         let conn = self.conn.lock().unwrap_or_else(|e| e.into_inner());
@@ -216,7 +234,7 @@ impl NodeDb {
             "SELECT id, node_id, ip, node_type, connected_at, disconnected_at,
                     platform, hostname, os, cpu_cores, cpu_model, ram_mb,
                     gpu_name, gpu_vendor, gpu_backend, vram_total_mb, gpu_temp_c, gpu_util_pct,
-                    allocated_gb, selected_task, has_webgpu, tasks_completed
+                    allocated_gb, selected_task, has_webgpu, tasks_completed, is_paused
              FROM node_sessions ORDER BY id DESC LIMIT ?1"
         ).unwrap();
 
@@ -244,6 +262,7 @@ impl NodeDb {
                 "selected_task": row.get::<_, Option<String>>(19)?,
                 "has_webgpu": row.get::<_, Option<bool>>(20)?,
                 "tasks_completed": row.get::<_, i64>(21)?,
+                "is_paused": row.get::<_, Option<bool>>(22)?.unwrap_or(false),
             }))
         }).unwrap().filter_map(|r| r.ok()).collect()
     }

network-poc/hub/src/main.rs

@@ -25,7 +25,7 @@ const ALLOWED_ORIGINS: &[&str] = &[
 ];
 
 // Sallitut viestityyypit clientilta
-const ALLOWED_MSG_TYPES: &[&str] = &["auth", "result", "pair_done", "llm_chunk", "llm_done", "llm_error", "download_progress", "user_text", "single_tokenize_done"];
+const ALLOWED_MSG_TYPES: &[&str] = &["auth", "result", "pair_done", "llm_chunk", "llm_done", "llm_error", "download_progress", "user_text", "single_tokenize_done", "status_update"];
 
 struct AppState {
     next_node_id: Mutex<u64>,
@@ -40,9 +40,12 @@ struct AppState {
     node_ips: Mutex<HashMap<u64, IpAddr>>,
     node_tasks: Mutex<HashMap<u64, String>>,  // node_id → selected_task
     node_types: Mutex<HashMap<u64, String>>,  // node_id → "native" | "browser"
+    node_paused: Mutex<std::collections::HashSet<u64>>, // node_id → onko tauolla
     node_busy: Mutex<std::collections::HashSet<u64>>,  // Solmut joilla on aktiivinen tehtävä
     pending_task_ids: Mutex<std::collections::HashSet<String>>,  // Hubin jakamat task_id:t (gamification-validointi)
+    pending_responses: Mutex<HashMap<String, tokio::sync::oneshot::Sender<serde_json::Value>>>,  // task_id → oneshot API-vastaukselle
     api_rate_limits: Mutex<HashMap<IpAddr, (std::time::Instant, u32)>>,  // IP → (ikkuna-alku, pyyntömäärä)
+    node_models: tokio::sync::RwLock<HashMap<u64, serde_json::Value>>, // node_id → ollama tags JSON
     db: db::NodeDb,
 }
 
@@ -80,6 +83,8 @@ tr:hover td { background:#1c2333; }
 .table-wrap { overflow-x:auto; max-height:70vh; overflow-y:auto; }
 .online { color:var(--green); }
 .offline { color:#8b949e; }
+.pause-btn { background:var(--panel); border:1px solid var(--border); color:var(--text); padding:4px 8px; border-radius:4px; cursor:pointer; font-size:12px; }
+.pause-btn:hover { border-color:var(--yellow); }
 </style>
 </head>
 <body>
@@ -91,6 +96,7 @@ tr:hover td { background:#1c2333; }
 <div class="tabs">
     <div class="tab active" onclick="showTab('sessions')">Sessiot</div>
     <div class="tab" onclick="showTab('pairs')">Tokenisointiparit</div>
+    <div class="tab" onclick="showTab('hardware')">Laitteisto & Mallit</div>
 </div>
 
 <div id="sessions" class="panel active">
@@ -99,12 +105,12 @@ tr:hover td { background:#1c2333; }
         <colgroup>
             <col style="width:35px"><col style="width:85px"><col style="width:95px"><col style="width:65px"><col style="width:110px"><col style="width:80px">
             <col style="width:65px"><col style="width:40px"><col style="width:70px"><col style="width:90px"><col style="width:60px">
-            <col style="width:65px"><col style="width:40px"><col style="width:130px"><col style="width:60px">
+            <col style="width:65px"><col style="width:40px"><col style="width:130px"><col style="width:60px"><col style="width:80px">
         </colgroup>
         <thead><tr>
             <th>ID</th><th>Tila</th><th>Tehtävä</th><th>Tyyppi</th><th>IP</th><th>Alusta</th>
             <th>OS</th><th>CPU</th><th>RAM</th><th>GPU</th><th>VRAM</th>
-            <th>WebGPU</th><th>Teht.</th><th>Yhdistetty</th><th>Kesto</th>
+            <th>WebGPU</th><th>Teht.</th><th>Yhdistetty</th><th>Kesto</th><th>Toiminnot</th>
         </tr></thead><tbody id="sessions-body"></tbody></table>
     </div>
 </div>
@@ -118,6 +124,19 @@ tr:hover td { background:#1c2333; }
     </div>
 </div>
 
+<div id="hardware" class="panel">
+    <div class="stats-grid" id="hardware-stats"></div>
+    <h2 style="margin-top: 10px; margin-bottom: 10px; color: var(--accent); font-size: 16px;">Käytettävissä olevat paikalliset kielimallit</h2>
+    <div class="table-wrap">
+        <table>
+            <thead><tr>
+                <th>Nimi</th><th>Koko</th><th>Parametrit</th>
+            </tr></thead>
+            <tbody id="models-body"></tbody>
+        </table>
+    </div>
+</div>
+
 <script>
 function showTab(name) {
     document.querySelectorAll('.panel').forEach(p => p.classList.remove('active'));
@@ -149,12 +168,16 @@ function duration(start, end) {
 }
 
 async function load() {
-    const [statsRes, sessionsRes, pairsRes] = await Promise.all([
+    const [statsRes, sessionsRes, pairsRes, hwRes, modelsRes] = await Promise.all([
-        fetch('/api/stats'), fetch('/api/sessions'), fetch('/api/pairs')
+        fetch('/api/stats'), fetch('/api/sessions'), fetch('/api/pairs'),
+        fetch('/api/v1/hardware').catch(() => ({json: async()=>({gpu_name:'', vram_mb:0, ram_mb:0})})),
+        fetch('/api/v1/ollama/tags').catch(() => ({json: async()=>({models:[]})}))
     ]);
     const stats = await statsRes.json();
     const sessions = await sessionsRes.json();
     const pairs = await pairsRes.json();
+    const hw = await hwRes.json().catch(() => ({gpu_name:'', vram_mb:0, ram_mb:0}));
+    const modelsData = await modelsRes.json().catch(() => ({models:[]}));
 
     // Versio
     if (stats.version) document.getElementById('admin-version').textContent = 'v' + stats.version;
@@ -173,7 +196,7 @@ async function load() {
     ].map(s => `<div class="stat-card"><div class="val">${s.v}</div><div class="label">${s.l}</div></div>`).join('');
 
     // Sessions — lajittelu: 1) aktiiviset nodet (online + ei viewer), 2) katsojat (online + viewer), 3) offline
-    const taskNames = {'tokenize':'Tokenisaatio','smollm-135m':'SmolLM 135M','qwen-05b':'Qwen2.5 0.5B','phi3-mini':'Phi-3 Mini','qwen-coder-05b':'Coder 0.5B','qwen-coder-3b':'Coder 3B','viewer':'Katsoja','codelab-viewer':'Koodilabra'};
+    const taskNames = {'tokenize':'Tokenisaatio','qwen-05b':'Qwen2.5 0.5B','qwen-coder-05b':'Coder 0.5B','qwen-coder-3b':'Coder 3B','viewer':'Katsoja','codelab-viewer':'Koodilabra'};
     sessions.sort((a, b) => {
         const aOnline = !a.disconnected_at;
         const bOnline = !b.disconnected_at;
@@ -190,9 +213,17 @@ async function load() {
     document.getElementById('sessions-body').innerHTML = sessions.map(s => {
         const online = !s.disconnected_at;
         const isViewer = s.selected_task === 'viewer';
-        const status = online
+        let status;
-            ? (isViewer ? '<span style="color:#d29922">CONNECTED</span>' : '<span class="online">ACTIVE</span>')
+        if (!online) {
-            : '<span class="offline">offline</span>';
+            status = '<span class="offline">offline</span>';
+        } else if (isViewer) {
+            status = '<span style="color:#d29922">CONNECTED</span>';
+        } else if (s.is_paused) {
+            status = '<span style="color:#8b949e">PAUSED</span>';
+        } else {
+            status = '<span class="online">ACTIVE</span>';
+        }
+        
         const typeBadge = s.node_type === 'native' ? badge('native','blue') : badge('browser','yellow');
         const taskColor = isViewer ? 'yellow' : s.selected_task === 'tokenize' ? 'green' : 'blue';
         const taskBadge = badge(taskNames[s.selected_task] || s.selected_task || '?', taskColor);
@@ -205,11 +236,16 @@ async function load() {
         const os = s.os || '-';
         const time = s.connected_at ? new Date(s.connected_at).toLocaleString('fi-FI') : '';
         const dur = duration(s.connected_at, s.disconnected_at);
+        const actionBtn = online && !isViewer 
+            ? `<button class="pause-btn" onclick="togglePause(${s.node_id}, ${s.is_paused})">${s.is_paused ? '▶ Työhön' : '⏸ Tauolle'}</button>` 
+            : '';
+            
         return `<tr>
             <td>${s.node_id}</td><td>${status}</td><td>${taskBadge}</td><td>${typeBadge}</td><td>${s.ip}</td>
             <td>${plat}</td><td>${os}</td><td>${cores}</td><td>${ram}</td>
             <td>${gpu}</td><td>${vram}</td><td>${gpuBadge}</td>
             <td>${s.tasks_completed}</td><td>${time}</td><td>${dur}</td>
+            <td>${actionBtn}</td>
         </tr>`;
     }).join('');
 
@@ -229,6 +265,35 @@ async function load() {
             <td>${p.duration_ms||0}ms</td>
         </tr>`;
     }).join('');
+
+    // Hardware
+    document.getElementById('hardware-stats').innerHTML = [
+        {v: hw.gpu_name || '-', l: 'Paikallinen GPU tila'},
+        {v: hw.vram_mb ? hw.vram_mb + ' MB' : '-', l: 'GPU Muisti (VRAM)'},
+        {v: hw.ram_mb ? hw.ram_mb + ' MB' : '-', l: 'RAM'},
+    ].map(s => `<div class="stat-card"><div class="val">${s.v}</div><div class="label">${s.l}</div></div>`).join('');
+
+    // Models
+    document.getElementById('models-body').innerHTML = (modelsData.models || []).map(m => {
+        const sizeGb = (m.size / (1024*1024*1024)).toFixed(2) + ' GB';
+        const params = m.details?.parameter_size || '-';
+        return `<tr>
+            <td><strong>${m.name}</strong></td>
+            <td>${sizeGb}</td>
+            <td>${params}</td>
+        </tr>`;
+    }).join('');
+}
+
+async function togglePause(nodeId, isPaused) {
+    try {
+        await fetch('/api/v1/control/' + nodeId, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ action: isPaused ? 'resume' : 'pause' })
+        });
+        load(); // virkistetään
+    } catch(e) { console.error(e); }
 }
 
 load();
@@ -262,9 +327,12 @@ async fn main() {
         node_ips: Mutex::new(HashMap::new()),
         node_tasks: Mutex::new(HashMap::new()),
         node_types: Mutex::new(HashMap::new()),
+        node_paused: Mutex::new(std::collections::HashSet::new()),
         node_busy: Mutex::new(std::collections::HashSet::new()),
         pending_task_ids: Mutex::new(std::collections::HashSet::new()),
+        pending_responses: Mutex::new(HashMap::new()),
         api_rate_limits: Mutex::new(HashMap::new()),
+        node_models: tokio::sync::RwLock::new(HashMap::new()),
         db: db::NodeDb::new(&std::env::var("DATABASE_PATH").unwrap_or_else(|_| "nodes.db".to_string())),
     });
 
@@ -351,9 +419,7 @@ async fn main() {
                         // Vapaa node -> lähetetään oikea tehtävä
                         let msg = match task.as_str() {
                             "tokenize" => Some(serde_json::json!({ "type": "pair_task", "en": en, "fi": fi })),
-                            "smollm-135m" => Some(serde_json::json!({ "type": "llm_prompt", "prompt": llm_prompts[llm_idx], "model": "smollm-135m" })),
                             "qwen-05b" => Some(serde_json::json!({ "type": "llm_prompt", "prompt": llm_prompts[llm_idx], "model": "qwen-05b" })),
-                            "phi3-mini" => Some(serde_json::json!({ "type": "llm_prompt", "prompt": llm_prompts[llm_idx], "model": "phi3-mini" })),
                             _ => None, // Coder ja viewer ei saa auto-tehtäviä
                         };
 
@@ -381,6 +447,7 @@ async fn main() {
         .route("/api/pairs", get(api_pairs))
         .route("/api/stats", get(api_stats))
         .route("/api/v1/chat/completions", axum::routing::post(api_chat_completions))
+        .route("/api/v1/control/:id", axum::routing::post(api_control_node))
         .route("/api/v1/model", axum::routing::post(api_change_model))
         .route("/api/v1/hardware", get(api_hardware))
         .route("/api/v1/ollama/tags", get(api_ollama_tags))
@@ -400,6 +467,26 @@ async fn main() {
     axum::serve(listener, app.into_make_service_with_connect_info::<SocketAddr>()).await.unwrap();
 }
 
+async fn api_control_node(
+    headers: axum::http::HeaderMap,
+    axum::extract::State(state): axum::extract::State<Arc<AppState>>,
+    axum::extract::Path(id): axum::extract::Path<u64>,
+    axum::Json(payload): axum::Json<serde_json::Value>,
+) -> axum::response::Response {
+    if !check_admin_auth(&headers) { return admin_unauthorized(); }
+    let action = payload.get("action").and_then(|v| v.as_str()).unwrap_or("");
+    if action == "pause" || action == "resume" {
+        let msg = serde_json::json!({ "type": "control", "action": action });
+        let channels = state.node_channels.read().await;
+        if let Some(tx) = channels.get(&id) {
+            let _ = tx.send(msg.to_string());
+            tracing::info!("Lähetetty control: {} solmulle {}", action, id);
+            return axum::Json(serde_json::json!({"status": "ok"})).into_response();
+        }
+    }
+    (axum::http::StatusCode::BAD_REQUEST, "Invalid action or node offline").into_response()
+}
+
 async fn api_sessions(
     headers: axum::http::HeaderMap,
     axum::extract::State(state): axum::extract::State<Arc<AppState>>,
@@ -563,6 +650,17 @@ async fn broadcast_stats(state: &Arc<AppState>) {
         "tasks": completed
     });
     let _ = state.stats_tx.send(stats_msg.to_string());
+
+    // Uutta: Laitetaan sama tieto myös kaikille yhdistyneille solmuille (viesti Hubilta Solmuille)
+    let node_status = serde_json::json!({
+        "type": "network_status",
+        "active_nodes": total_nodes,
+        "tasks": completed
+    });
+    let msg_str = node_status.to_string();
+    for tx in state.node_channels.read().await.values() {
+        let _ = tx.send(msg_str.clone());
+    }
 }
 
 /// Validoi client-viesti: pakollinen "type"-kenttä, sallittu tyyppi, validi JSON
@@ -730,6 +828,9 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
                 }
                 state.node_tasks.lock().unwrap().insert(node_id, selected_task);
                 state.node_types.lock().unwrap().insert(node_id, node_type.to_string());
+                // Uudelleen-kirjautuessa nollataan tauko
+                state.node_paused.lock().unwrap().remove(&node_id);
+                state.db.update_session_status(node_id, false);
 
                 if node_type == "native" {
                     let sys = json.get("system");
@@ -743,6 +844,12 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
                         node_id, ip, hostname, os, cores, ram, allocated
                     );
 
+                    // Tallennetaan välitetyt mallit muistiin
+                    if let Some(models) = json.get("models") {
+                        let mut nm = state.node_models.write().await;
+                        nm.insert(node_id, models.clone());
+                    }
+
                     if let Some(gpus) = json.get("gpus").and_then(|v| v.as_array()) {
                         for gpu in gpus {
                             tracing::info!(
@@ -780,6 +887,18 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
             });
             let _ = state.stats_tx.send(join_msg.to_string());
 
+        } else if msg_type == "status_update" {
+            let status = json.get("status").and_then(|v| v.as_str()).unwrap_or("active");
+            if status == "paused" {
+                state.node_paused.lock().unwrap().insert(node_id);
+                state.db.update_session_status(node_id, true);
+                tracing::info!("Solmu {} ({}) asettui tauolle.", node_id, ip);
+            } else {
+                state.node_paused.lock().unwrap().remove(&node_id);
+                state.db.update_session_status(node_id, false);
+                tracing::info!("Solmu {} ({}) on taas aktiivinen.", node_id, ip);
+            }
+            broadcast_stats(&state).await;
         } else if msg_type == "result" {
             tracing::info!("Solmu {} sai tuloksen: {}", node_id, text);
             {
@@ -875,11 +994,18 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
         } else if msg_type == "llm_done" {
             // Vapautetaan solmu ja tarkistetaan task_id:n aitous
             state.node_busy.lock().unwrap().remove(&node_id);
-            let valid_task = if let Some(tid) = json.get("task_id").and_then(|v| v.as_str()) {
+            let task_id = json.get("task_id").and_then(|v| v.as_str()).map(|s| s.to_string());
-                state.pending_task_ids.lock().unwrap().remove(tid)
+            let valid_task = if let Some(ref tid) = task_id {
+                state.pending_task_ids.lock().unwrap().remove(tid.as_str())
             } else {
                 false
             };
+
+            // Jos API-pyyntö odottaa tätä vastausta, reititetään suoraan oneshot-kanavaan
+            let api_sender = task_id.as_ref().and_then(|tid| {
+                state.pending_responses.lock().unwrap().remove(tid)
+            });
+
             {
                 let mut json = json;
                 if let Some(obj) = json.as_object_mut() {
@@ -899,6 +1025,12 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
                     state.db.increment_tasks(node_id);
                     obj.insert("node_id".to_string(), serde_json::json!(node_id));
                 }
+
+                if let Some(sender) = api_sender {
+                    // API-pyyntö: reititetään vastaus suoraan odottajalle
+                    let _ = sender.send(json.clone());
+                }
+                // UI-broadcast jatkuu normaalisti
                 let _ = state.stats_tx.send(json.to_string());
 
                 let active_incentives = state.feature_flags.read().await.get("Insentiivit").copied().unwrap_or(false);
@@ -908,7 +1040,7 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
                 {
                     let mut task_count = state.total_tasks.lock().unwrap();
                     *task_count += 1;
-                    
+
                     if active_incentives && valid_task {
                         let mut tokens = state.nodes_tokens.lock().unwrap();
                         let balance = tokens.entry(node_id).or_insert(0);
@@ -916,7 +1048,7 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
                         current_balance = *balance;
                     }
                 }
-                
+
                 if active_incentives && ui_sync {
                     if let Some(tx) = state.node_channels.read().await.get(&node_id) {
                          let msg = serde_json::json!({
@@ -926,45 +1058,50 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
                          let _ = tx.send(msg.to_string());
                     }
                 }
-                
+
                 broadcast_stats(&state).await;
             }
         } else if msg_type == "llm_error" {
             state.node_busy.lock().unwrap().remove(&node_id);
-            if let Some(tid) = json.get("task_id").and_then(|v| v.as_str()) {
+            let task_id = json.get("task_id").and_then(|v| v.as_str()).map(|s| s.to_string());
-                state.pending_task_ids.lock().unwrap().remove(tid);
+            if let Some(ref tid) = task_id {
+                state.pending_task_ids.lock().unwrap().remove(tid.as_str());
             }
+            // Jos API-pyyntö odottaa, reititetään virhe oneshot-kanavaan
+            let api_sender = task_id.as_ref().and_then(|tid| {
+                state.pending_responses.lock().unwrap().remove(tid)
+            });
             {
                 let mut json = json;
                 if let Some(obj) = json.as_object_mut() {
                     obj.insert("node_id".to_string(), serde_json::json!(node_id));
                 }
+                if let Some(sender) = api_sender {
+                    let _ = sender.send(json.clone());
+                }
                 let _ = state.stats_tx.send(json.to_string());
             }
         } else if msg_type == "user_text" {
-            // Käyttäjän lähettämä teksti — broadcastataan pair_taskina ja llm_promptina
+            // Käyttäjän lähettämä teksti — kohdennettu reititys lähettäjäsolmulle
             let text = json.get("text").and_then(|v| v.as_str()).unwrap_or("").to_string();
             let task_type = json.get("task_type").and_then(|v| v.as_str()).unwrap_or("tokenize");
             if !text.is_empty() {
                 let preview: String = text.chars().take(80).collect();
                 tracing::info!("Solmu {} lähetti oman tekstin ({}): \"{}\"", node_id, task_type, preview);
-                match task_type {
+                let msg = match task_type {
-                    "tokenize" => {
+                    "tokenize" => serde_json::json!({
-                        let msg = serde_json::json!({
+                        "type": "single_tokenize",
-                            "type": "single_tokenize",
+                        "text": text,
-                            "text": text,
+                    }),
-                        });
+                    _ => serde_json::json!({
-                        let _ = state.stats_tx.send(msg.to_string());
+                        "type": "llm_prompt",
-                    }
+                        "prompt": text,
-                    _ => {
+                        "model": task_type,
-                        // LLM-prompti: lähetetään VAIN valitulle mallille, ei kaikille (välttää turhaa ruuhkaa ja busy-tiloja)
+                    }),
-                        let prompt = serde_json::json!({
+                };
-                            "type": "llm_prompt",
+                // Lähetetään takaisin lähettäjäsolmulle (käyttäjä haluaa oman tekstinsä tuloksen)
-                            "prompt": text,
+                if let Some(tx) = state.node_channels.read().await.get(&node_id) {
-                            "model": task_type,
+                    let _ = tx.send(msg.to_string());
-                        });
-                        let _ = state.stats_tx.send(prompt.to_string());
-                    }
                 }
             }
         }
@@ -989,6 +1126,8 @@ async fn handle_socket(socket: WebSocket, state: Arc<AppState>, ip: IpAddr) {
         vram.remove(&node_id);
     }
     state.node_types.lock().unwrap().remove(&node_id);
+    state.node_paused.lock().unwrap().remove(&node_id);
+    state.node_models.write().await.remove(&node_id);
     tracing::info!("Solmu {} ({}) poistui verkosta.", node_id, ip);
     broadcast_stats(&state).await;
     sender_task.abort();
@@ -1000,6 +1139,16 @@ struct ChatCompletionRequest {
     task_id: String,
     #[serde(default)]
     max_tokens: Option<u64>,
+    #[serde(default)]
+    system_prompt: Option<String>,
+    #[serde(default)]
+    temperature: Option<f64>,
+    #[serde(default)]
+    top_k: Option<u64>,
+    #[serde(default)]
+    repeat_penalty: Option<f64>,
+    #[serde(default)]
+    stop: Option<Vec<String>>,
 }
 
 #[derive(serde::Serialize)]
@@ -1009,7 +1158,16 @@ struct ChatCompletionResponse {
     tokens_generated: u64,
 }
 
-async fn api_ollama_tags() -> axum::response::Response {
+async fn api_ollama_tags(
+    axum::extract::State(state): axum::extract::State<Arc<AppState>>,
+) -> axum::response::Response {
+    // Haetaan natiivisolmun tila muistista — priorisoidaan aito verkko-solmu
+    let node_models = state.node_models.read().await;
+    if let Some((_, models_json)) = node_models.iter().next() {
+        return axum::Json(models_json.clone()).into_response();
+    }
+
+    // Fallback: Haetaan lokaalista infra-Ollamasta ohjaimesta käsin (esim dev ympäristö)
     let ollama_url = std::env::var("OLLAMA_URL").unwrap_or_else(|_| "http://ollama:11434".to_string());
     match reqwest::get(format!("{}/api/tags", ollama_url)).await {
         Ok(resp) => {
@@ -1033,11 +1191,10 @@ async fn api_hardware(
     });
 
     let (mut vram_mb, mut gpu_name, ram_mb) = if let Some(s) = native {
-        let gpus = s.get("gpus").and_then(|v| v.as_array());
+        // Tieto on tietokannassa litteänä
-        let gpu = gpus.and_then(|g| g.first());
+        let vram = s.get("vram_total_mb").and_then(|v| v.as_u64()).unwrap_or(0);
-        let vram = gpu.and_then(|g| g.get("vram_total_mb")).and_then(|v| v.as_u64()).unwrap_or(0);
+        let name = s.get("gpu_name").and_then(|v| v.as_str()).unwrap_or("").to_string();
-        let name = gpu.and_then(|g| g.get("name")).and_then(|v| v.as_str()).unwrap_or("").to_string();
+        let ram = s.get("ram_mb").and_then(|v| v.as_u64()).unwrap_or(0);
-        let ram = s.get("system").and_then(|v| v.get("ram_total_mb")).and_then(|v| v.as_u64()).unwrap_or(0);
         (vram, name, ram)
     } else {
         (0, String::new(), 0)
@@ -1106,7 +1263,9 @@ async fn api_chat_completions(
         let tasks = state.node_tasks.lock().unwrap();
         let _busy = state.node_busy.lock().unwrap();
         let node_types = state.node_types.lock().unwrap();
-        let matching: Vec<u64> = tasks.iter().filter(|(_, task)| {
+        let paused = state.node_paused.lock().unwrap();
+        let matching: Vec<u64> = tasks.iter().filter(|(k, task)| {
+            if paused.contains(k) { return false; } // Ei sallita tauotettuja
             // Eksakti match tai qwen-perheen yhteensopivuus (selain: qwen-coder-05b, natiivi: qwen2.5-coder:7b)
             let req_model = payload.model.to_lowercase();
             let node_task = task.to_lowercase();
@@ -1157,12 +1316,17 @@ async fn api_chat_completions(
         "model": payload.model,
         "task_id": payload.task_id,
     });
-    if let Some(mt) = payload.max_tokens {
+    let obj = msg.as_object_mut().unwrap();
-        msg.as_object_mut().unwrap().insert("max_tokens".to_string(), serde_json::json!(mt));
+    if let Some(mt) = payload.max_tokens { obj.insert("max_tokens".to_string(), serde_json::json!(mt)); }
-    }
+    if let Some(ref sp) = payload.system_prompt { obj.insert("system_prompt".to_string(), serde_json::json!(sp)); }
+    if let Some(t) = payload.temperature { obj.insert("temperature".to_string(), serde_json::json!(t)); }
+    if let Some(k) = payload.top_k { obj.insert("top_k".to_string(), serde_json::json!(k)); }
+    if let Some(rp) = payload.repeat_penalty { obj.insert("repeat_penalty".to_string(), serde_json::json!(rp)); }
+    if let Some(ref s) = payload.stop { obj.insert("stop".to_string(), serde_json::json!(s)); }
 
-    // Odotuskanava valmiiksi (solmu palauttaa tuloksen stats_tx kautta)
+    // Oneshot-kanava: solmu palauttaa tuloksen suoraan tälle pyynnölle
-    let mut rx = state.stats_tx.subscribe();
+    let (resp_tx, resp_rx) = tokio::sync::oneshot::channel::<serde_json::Value>();
+    state.pending_responses.lock().unwrap().insert(payload.task_id.clone(), resp_tx);
 
     // Kohdennettu reititys: lähetetään AI-tehtävä suoraan VAIN valitulle solmulle
     {
@@ -1171,48 +1335,34 @@ async fn api_chat_completions(
             let _ = tx.send(msg.to_string());
             tracing::info!("Reititettiin API-pyyntö solmulle {} (Malli: {})", target_node_id, payload.model);
         } else {
+             state.pending_responses.lock().unwrap().remove(&payload.task_id);
              return (axum::http::StatusCode::SERVICE_UNAVAILABLE, "Verkkovirhe: solmun yhteys katkesi reitityksen aikana").into_response();
         }
     }
 
-    let timeout = tokio::time::timeout(std::time::Duration::from_secs(600), async move {
+    let timeout = tokio::time::timeout(std::time::Duration::from_secs(600), resp_rx).await;
-        loop {
-            let msg_str = match rx.recv().await {
-                Ok(msg) => msg,
-                Err(broadcast::error::RecvError::Lagged(n)) => {
-                    tracing::debug!("API-kanava lagged {} viestiä", n);
-                    continue;
-                }
-                Err(_) => return Ok(None), // Kanava suljettu
-            };
-            if let Ok(v) = serde_json::from_str::<serde_json::Value>(&msg_str) {
-                if v["type"].as_str() == Some("llm_done") {
-                    if let Some(tid) = v["task_id"].as_str() {
-                        if tid == payload.task_id {
-                            return Ok(Some(ChatCompletionResponse {
-                                response: v["response"].as_str().unwrap_or("").to_string(),
-                                model: v["model"].as_str().unwrap_or("").to_string(),
-                                tokens_generated: v["tokens_generated"].as_u64().unwrap_or(0),
-                            }));
-                        }
-                    }
-                } else if v["type"].as_str() == Some("llm_error") {
-                    if let Some(tid) = v["task_id"].as_str() {
-                        if tid == payload.task_id {
-                            return Err(v["error"].as_str().unwrap_or("Määrittelemätön virhe solmussa").to_string());
-                        }
-                    }
-                }
-            }
-        }
-        #[allow(unreachable_code)]
-        Ok(None)
-    }).await;
 
     match timeout {
-        Ok(Ok(Some(res))) => axum::Json(res).into_response(),
+        Ok(Ok(v)) => {
-        Ok(Ok(None)) => (axum::http::StatusCode::INTERNAL_SERVER_ERROR, "Verkkovirhe: yhteys katkesi").into_response(),
+            if v["type"].as_str() == Some("llm_error") {
-        Ok(Err(err)) => (axum::http::StatusCode::CONFLICT, err).into_response(),
+                let err = v["error"].as_str().unwrap_or("Määrittelemätön virhe solmussa").to_string();
-        Err(_) => (axum::http::StatusCode::GATEWAY_TIMEOUT, "Aikakatkaisu: solmu ei saanut tehtävää ajoissa valmiiksi").into_response(),
+                (axum::http::StatusCode::CONFLICT, err).into_response()
+            } else {
+                axum::Json(ChatCompletionResponse {
+                    response: v["response"].as_str().unwrap_or("").to_string(),
+                    model: v["model"].as_str().unwrap_or("").to_string(),
+                    tokens_generated: v["tokens_generated"].as_u64().unwrap_or(0),
+                }).into_response()
+            }
+        }
+        Ok(Err(_)) => {
+            // Oneshot-kanava sulkeutui (solmu katosi)
+            state.pending_responses.lock().unwrap().remove(&payload.task_id);
+            (axum::http::StatusCode::INTERNAL_SERVER_ERROR, "Verkkovirhe: yhteys katkesi").into_response()
+        }
+        Err(_) => {
+            state.pending_responses.lock().unwrap().remove(&payload.task_id);
+            (axum::http::StatusCode::GATEWAY_TIMEOUT, "Aikakatkaisu: solmu ei saanut tehtävää ajoissa valmiiksi").into_response()
+        }
     }
 }

network-poc/install.sh

@@ -1,59 +0,0 @@
-#!/bin/bash
-# Kipinä Agentic Studio — asennusskripti (Debian/Ubuntu)
-set -e
-
-echo "=== Kipinä Agentic Studio — Asennus ==="
-echo ""
-
-# Tarkistetaan käyttöjärjestelmä
-if [ ! -f /etc/debian_version ]; then
-    echo "⚠ Tämä skripti on suunniteltu Debian/Ubuntu-järjestelmille."
-    echo "  Muilla jakeluilla voit asentaa riippuvuudet manuaalisesti."
-    read -p "  Jatketaanko? (k/e) " -n 1 -r; echo
-    [[ $REPLY =~ ^[Kk]$ ]] || exit 1
-fi
-
-echo "[1/6] Päivitetään pakettilistaus..."
-sudo apt-get update -qq
-
-echo "[2/6] Asennetaan peruspaketteja..."
-sudo apt-get install -y -qq curl git build-essential pkg-config libssl-dev
-
-# Rust
-if command -v rustc &>/dev/null; then
-    echo "[3/6] Rust löytyi: $(rustc --version)"
-else
-    echo "[3/6] Asennetaan Rust..."
-    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
-    source "$HOME/.cargo/env"
-fi
-
-# Node.js (Astro-frontend vaatii)
-if command -v node &>/dev/null; then
-    echo "[4/6] Node.js löytyi: $(node --version)"
-else
-    echo "[4/6] Asennetaan Node.js 22..."
-    curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
-    sudo apt-get install -y -qq nodejs
-fi
-
-# Ollama
-if command -v ollama &>/dev/null; then
-    echo "[5/6] Ollama löytyi"
-else
-    echo "[5/6] Asennetaan Ollama..."
-    curl -fsSL https://ollama.ai/install.sh | sh
-fi
-
-# Malli
-echo "[6/6] Ladataan kielimalli (qwen2.5-coder:3b)..."
-ollama pull qwen2.5-coder:3b
-
-echo ""
-echo "=== Asennus valmis! ==="
-echo ""
-echo "Käynnistä:"
-echo "  cd $(pwd)"
-echo "  ./network-poc/local.sh"
-echo ""
-echo "Avaa selaimessa: http://localhost:3000"

network-poc/local.sh

@@ -1,37 +0,0 @@
-#!/bin/bash
-set -e
-
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-echo "=== Kipinä Studio Local Development ==="
-
-# Frontend
-echo "[1/3] Rakennetaan frontend..."
-cd "$SCRIPT_DIR/frontend"
-[ -d node_modules ] || npm install --silent
-npm run build --silent 2>&1 | tail -1
-
-# Hub
-echo "[2/3] Käynnistetään hub..."
-cd "$SCRIPT_DIR/hub"
-cargo run &
-HUB_PID=$!
-sleep 3
-
-# Native-node (jos Ollama on käynnissä)
-if curl -s http://localhost:11434/api/tags >/dev/null 2>&1; then
-    echo "[3/3] Ollama löytyi — käynnistetään native-node..."
-    cd "$SCRIPT_DIR/native-node"
-    HUB_URL=ws://localhost:3000/ws cargo run --no-default-features &
-    NODE_PID=$!
-    echo "      Native-node PID: $NODE_PID"
-else
-    echo "[3/3] Ollama ei käynnissä — käytetään selaimen Wasm-laskentaa"
-    echo "      Nopeampi: ollama serve & ollama pull qwen2.5-coder:7b && ./local.sh"
-fi
-
-echo ""
-echo "=== http://localhost:3000 ==="
-echo "    Ctrl+C pysäyttää"
-
-# Odotetaan hub-prosessia
-wait $HUB_PID

network-poc/native-node/Cargo.toml

@@ -19,3 +19,7 @@ wgpu = { version = "24", optional = true }
 reqwest = { version = "0.12", features = ["json"] }
 tracing = "0.1"
 tracing-subscriber = { version = "0.3", features = ["env-filter"] }
+dialoguer = "0.12.0"
+ratatui = "0.29.0"
+crossterm = { version = "0.28.1", features = ["event-stream"] }
+tracing-appender = "0.2.4"

network-poc/native-node/src/inference.rs

@@ -1,6 +1,15 @@
 use std::time::Instant;
 use std::cell::RefCell;
 
+pub struct GenerateOptions {
+    pub max_tokens: usize,
+    pub system_prompt: Option<String>,
+    pub temperature: Option<f64>,
+    pub top_k: Option<u64>,
+    pub repeat_penalty: Option<f64>,
+    pub stop: Option<Vec<String>>,
+}
+
 pub struct LlmEngine {
     ollama_url: String,
     model: RefCell<String>,
@@ -9,8 +18,6 @@ pub struct LlmEngine {
 
 impl LlmEngine {
     pub async fn load() -> Result<Self, String> {
-        let model = std::env::var("OLLAMA_MODEL").unwrap_or_else(|_| "qwen2.5-coder:3b".to_string());
-
         let client = reqwest::Client::builder()
             .timeout(std::time::Duration::from_secs(600))
             .connect_timeout(std::time::Duration::from_secs(3))
@@ -48,6 +55,12 @@ impl LlmEngine {
             })
         };
 
+        // Kysytään malli TUI:lla jos ei pakotettu ympäristöstä
+        let model = match std::env::var("OLLAMA_MODEL") {
+            Ok(m) if !m.is_empty() => m,
+            _ => crate::tui::select_model(&ollama_url, &client).await?
+        };
+
         tracing::info!("Ollama backend: {} | malli: {}", ollama_url, model);
         Ok(LlmEngine { ollama_url, model: RefCell::new(model), client })
     }
@@ -78,28 +91,55 @@ impl LlmEngine {
         }
     }
 
-    pub async fn generate(&self, prompt: &str, max_tokens: usize) -> Result<GenerateResult, String> {
+    /// Hakee kaikki Ollamaan asennetut mallit
-        // System prompt tulee agentin konfiguraatiosta (frontend lähettää sen osana promptia).
+    pub async fn fetch_models(&self) -> Result<serde_json::Value, String> {
-        // Tässä ei yliajeta sitä — Ollama saa vain prompt-kentän.
+        let resp = self.client.get(format!("{}/api/tags", self.ollama_url))
-        let model = self.model.borrow().clone();
-
-        let start = Instant::now();
-        let resp = self.client.post(format!("{}/api/generate", self.ollama_url))
-            .json(&serde_json::json!({
-                "model": model,
-                "prompt": prompt,
-                "stream": false,
-                "options": {
-                    "num_predict": max_tokens,
-                    "temperature": 0.7,
-                    "top_k": 40,
-                    "repeat_penalty": 1.15,
-                    "stop": ["<|im_end|>", "\n###", "\nExplanation", "\nNote:", "\nPlease note", "\nThis is", "\n```\n\n", "\n// Example", "\n# Example"]
-                }
-            }))
             .send()
             .await
-            .map_err(|e| format!("Ollama generate: {}", e))?;
+            .map_err(|e| format!("Ollama tags fetch: {}", e))?;
+
+        if resp.status().is_success() {
+            resp.json().await.map_err(|e| format!("Ollama tags json: {}", e))
+        } else {
+            Err(format!("Ollama tags epäonnistui: {}", resp.status()))
+        }
+    }
+
+    pub async fn generate(&self, prompt: &str, opts: &GenerateOptions) -> Result<GenerateResult, String> {
+        let model = self.model.borrow().clone();
+
+        let default_stop: Vec<String> = vec![
+            "<|im_end|>".into(),
+        ];
+
+        // Rakennetaan messages-lista (chat API)
+        let mut messages = Vec::new();
+        if let Some(ref sp) = opts.system_prompt {
+            if !sp.is_empty() {
+                messages.push(serde_json::json!({"role": "system", "content": sp}));
+            }
+        }
+        messages.push(serde_json::json!({"role": "user", "content": prompt}));
+
+        let body = serde_json::json!({
+            "model": model,
+            "messages": messages,
+            "stream": false,
+            "options": {
+                "num_predict": opts.max_tokens,
+                "temperature": opts.temperature.unwrap_or(0.7),
+                "top_k": opts.top_k.unwrap_or(40),
+                "repeat_penalty": opts.repeat_penalty.unwrap_or(1.15),
+                "stop": opts.stop.as_ref().unwrap_or(&default_stop),
+            }
+        });
+
+        let start = Instant::now();
+        let resp = self.client.post(format!("{}/api/chat", self.ollama_url))
+            .json(&body)
+            .send()
+            .await
+            .map_err(|e| format!("Ollama chat: {}", e))?;
 
         if !resp.status().is_success() {
             return Err(format!("Ollama HTTP {}", resp.status()));
@@ -108,7 +148,7 @@ impl LlmEngine {
         let body: serde_json::Value = resp.json().await
             .map_err(|e| format!("Ollama JSON: {}", e))?;
 
-        let text = body["response"].as_str().unwrap_or("").to_string();
+        let text = body["message"]["content"].as_str().unwrap_or("").to_string();
         let _total_duration_ns = body["total_duration"].as_u64().unwrap_or(0);
         let eval_count = body["eval_count"].as_u64().unwrap_or(0) as usize;
         let eval_duration_ns = body["eval_duration"].as_u64().unwrap_or(1);
@@ -127,40 +167,15 @@ impl LlmEngine {
     }
 }
 
-/// Siivoa markdown-koodiblokki-merkit ja selitystekstit
+/// Siivoa markdown-koodiblokki-merkit vastauksesta
 fn strip_code_fences(text: &str) -> String {
-    // Poistetaan kaikki ```-rivit ja kielitunnisteet (```python, ```rust jne.)
     let lines: Vec<&str> = text.lines().collect();
     let filtered: Vec<&str> = lines.into_iter().filter(|line| {
         let trimmed = line.trim();
         // Poista rivit jotka ovat pelkkiä ``` tai ```kielitunniste
-        if trimmed.starts_with("```") {
+        trimmed != "```" && !(trimmed.starts_with("```") && !trimmed[3..].contains('`'))
-            return false;
-        }
-        true
     }).collect();
-    let mut result = filtered.join("\n").trim().to_string();
+    filtered.join("\n").trim().to_string()
-
-    // Poista selitysteksti lopusta (kaikki rivin "\nPlease note" jälkeen jne.)
-    let lower = result.to_lowercase();
-    for stop in &["\nplease note", "\nthis is a basic", "\nthis code", "\nnote that", "\nremember to", "\nyou can", "\nto run"] {
-        if let Some(pos) = lower.find(stop) {
-            result = result[..pos].trim_end().to_string();
-        }
-    }
-
-    // Poista johdantolauseet alusta
-    let lower = result.to_lowercase();
-    for prefix in &["sure!", "here is", "here's", "certainly!", "below is"] {
-        if lower.starts_with(prefix) {
-            if let Some(nl) = result.find('\n') {
-                result = result[nl + 1..].to_string();
-            }
-            break;
-        }
-    }
-
-    result.trim().to_string()
 }
 
 pub struct GenerateResult {

network-poc/native-node/src/main.rs

@@ -1,10 +1,13 @@
 use futures_util::{SinkExt, StreamExt};
 use serde_json::json;
+use std::io::IsTerminal;
 use sysinfo::System;
 use tokio_tungstenite::connect_async;
 use tokio_tungstenite::tungstenite::Message;
 
 mod inference;
+mod tui;
+mod tui_dashboard;
 
 /// GPU-tietorakenne — yhtenäinen kaikille valmistajille
 struct GpuInfo {
@@ -222,7 +225,7 @@ fn collect_system_info() -> serde_json::Value {
 }
 
 /// Koko auth-viesti hubille
-fn build_auth_message(allocated_gb: u32) -> String {
+fn build_auth_message(allocated_gb: u32, model_name: &str, models_data: Option<serde_json::Value>) -> String {
     let sys = collect_system_info();
     let gpus = collect_all_gpus();
 
@@ -239,7 +242,7 @@ fn build_auth_message(allocated_gb: u32) -> String {
         "status": "agent_ready",
         "node_type": "native",
         "allocated_gb": allocated_gb,
-        "selected_task": "qwen2.5-coder:7b",
+        "selected_task": model_name,
         "system": sys,
     });
 
@@ -251,6 +254,10 @@ fn build_auth_message(allocated_gb: u32) -> String {
         msg.as_object_mut().unwrap().insert("gpus".to_string(), json!(gpu_json));
     }
 
+    if let Some(models) = models_data {
+        msg.as_object_mut().unwrap().insert("models".to_string(), models);
+    }
+
     msg.to_string()
 }
 
@@ -263,10 +270,24 @@ fn format_optional<T: std::fmt::Display>(val: Option<T>, suffix: &str) -> String
 
 #[tokio::main]
 async fn main() {
+    let file_appender = tracing_appender::rolling::never(".", "native-node.log");
+    let (non_blocking, _guard) = tracing_appender::non_blocking(file_appender);
+    
     tracing_subscriber::fmt()
         .with_env_filter("native_node=debug")
+        .with_writer(non_blocking)
         .init();
 
+    // Hookataan paniikkitilanteet palauttamaan terminaalin raw-moodista
+    let original_hook = std::panic::take_hook();
+    std::panic::set_hook(Box::new(move |panic_info| {
+        tui_dashboard::restore_terminal();
+        original_hook(panic_info);
+    }));
+
+    let tui_state = std::sync::Arc::new(tokio::sync::RwLock::new(tui_dashboard::DashboardState::new()));
+    let (cmd_tx, mut cmd_rx) = tokio::sync::mpsc::unbounded_channel::<String>();
+
     let hub_url = std::env::var("HUB_URL").unwrap_or_else(|_| "ws://hub:3000/ws".to_string());
     let allocated_gb: u32 = std::env::var("ALLOCATED_GB")
         .ok()
@@ -282,6 +303,18 @@ async fn main() {
         sys["cpu_cores"],
         sys["ram_total_mb"]
     );
+    
+    {
+        let mut st = tui_state.write().await;
+        st.sys_info = format!("{} | {} | {} ydintä | {} MB RAM",
+            sys["hostname"].as_str().unwrap_or("?"),
+            sys["os"].as_str().unwrap_or("?"),
+            sys["cpu_cores"],
+            sys["ram_total_mb"]
+        );
+        let i = st.sys_info.clone();
+        st.push_log("System", format!("Järjestelmä: {}", i), None);
+    }
 
     let gpus = collect_all_gpus();
     if gpus.is_empty() {
@@ -321,6 +354,40 @@ async fn main() {
         }
     };
 
+    let active_model = llm.as_ref().map(|e| e.model_name()).unwrap_or_else(|| "unknown".to_string());
+    tracing::info!("Käytettävä kielimalli konfiguroitu (selected_task): {}", active_model);
+    
+    {
+        let mut st = tui_state.write().await;
+        st.model_name = active_model.clone();
+        st.push_log("System", format!("Malli valmis: {}", active_model), None);
+    }
+
+    // Käynnistetään graafinen TUI vain jos stdin on terminaali (ei taustaprosessina)
+    let ui_state = tui_state.clone();
+    if std::io::stdin().is_terminal() {
+        tokio::spawn(async move {
+            if let Err(e) = tui_dashboard::run_dashboard(ui_state, cmd_tx).await {
+                tracing::error!("Pääluupin TUI kaatui: {}", e);
+            }
+        });
+    } else {
+        tracing::info!("Ei terminaalia — TUI ohitettu, lokitetaan stdoutiin");
+    };
+
+    // Haetaan paikalliset mallit hubille lähetettäväksi
+    let mut available_models = None;
+    if let Some(ref engine) = llm {
+        match engine.fetch_models().await {
+            Ok(models) => {
+                available_models = Some(models);
+            }
+            Err(e) => {
+                tracing::warn!("Mallilistauksen haku epäonnistui: {}", e);
+            }
+        }
+    }
+
     // Yhdistetään hubiin
     loop {
         match connect_async(&hub_url).await {
@@ -328,80 +395,226 @@ async fn main() {
                 tracing::info!("Yhdistetty hubiin!");
                 let (mut write, mut read) = ws_stream.split();
 
-                let auth = build_auth_message(allocated_gb);
+                let auth = build_auth_message(allocated_gb, &active_model, available_models.clone());
                 if write.send(Message::Text(auth)).await.is_err() {
                     tracing::error!("Auth-viestin lähetys epäonnistui");
                     continue;
                 }
 
-                while let Some(Ok(msg)) = read.next().await {
+                loop {
-                    if let Message::Text(text) = msg {
+                    tokio::select! {
-                        // LLM-promptit
+                        cmd = cmd_rx.recv() => {
-                        if text.contains("llm_prompt") {
+                            if let Some(cmd_str) = cmd {
-                            if let Ok(task) = serde_json::from_str::<serde_json::Value>(&text) {
+                                if cmd_str == "pause" {
-                                let prompt = task.get("prompt").and_then(|v| v.as_str()).unwrap_or("");
+                                    tracing::info!("Tauotetaan solmun suoritus (Hub ei lähetä tehtäviä)...");
-                                let task_id = task.get("task_id").and_then(|v| v.as_str()).unwrap_or("?");
+                                    let req = json!({"type": "status_update", "status": "paused"});
-                                let msg_model = task.get("model").and_then(|v| v.as_str()).unwrap_or("");
+                                    let _ = write.send(Message::Text(req.to_string())).await;
-                                
+                                    {
-                                if !prompt.is_empty() && (msg_model.starts_with("qwen-coder") || msg_model.starts_with("qwen2.5-coder")) {
+                                        let mut st = tui_state.write().await;
-
+                                        st.status = "PAUSED".to_string();
+                                        st.push_log("Network", "Solmu siirretty taukotilaan".to_string(), None);
+                                    }
+                                } else if cmd_str == "resume" {
+                                    tracing::info!("Jatketaan solmun suoritusta...");
+                                    let req = json!({"type": "status_update", "status": "active"});
+                                    let _ = write.send(Message::Text(req.to_string())).await;
+                                    {
+                                        let mut st = tui_state.write().await;
+                                        st.status = "ACTIVE".to_string();
+                                        st.push_log("System", "Suoritus jatkuu...".to_string(), None);
+                                    }
+                                } else if cmd_str == "fetch_models" {
+                                    // Haetaan mallit Ollamasta ja avataan valikkö
                                     if let Some(ref engine) = llm {
-                                        let max_tokens = task.get("max_tokens").and_then(|v| v.as_u64()).unwrap_or(1024) as usize;
+                                        match engine.fetch_models().await {
-                                        let prompt_lines = prompt.lines().count();
+                                            Ok(tags) => {
-                                        let prompt_last: String = prompt.lines().last().unwrap_or("").chars().take(60).collect();
+                                                let models: Vec<String> = tags.get("models")
-                                        tracing::info!("→ task_id:{} | {}r prompti | \"{}...\"", task_id, prompt_lines, prompt_last);
+                                                    .and_then(|v| v.as_array())
-
+                                                    .map(|arr| arr.iter()
-                                        let model_name = engine.model_name();
+                                                        .filter_map(|m| m.get("name").and_then(|n| n.as_str()).map(|s| s.to_string()))
-                                        match engine.generate(prompt, max_tokens).await {
+                                                        .collect())
-                                            Ok(result) => {
+                                                    .unwrap_or_default();
-                                                tracing::info!(
+                                                let mut st = tui_state.write().await;
-                                                    "✓ {} | {} tok | {:.0}ms | {:.1} tok/s",
+                                                st.model_picker_items = models;
-                                                    model_name,
+                                                st.model_picker_idx = 0;
-                                                    result.tokens_generated,
+                                                st.model_picker_open = true;
-                                                    result.duration_ms,
-                                                    result.tokens_per_sec,
-                                                );
-
-                                                // Lähetetään vain lyhyt prompti-esikatselu (ei koko kontekstia)
-                                                let prompt_short: String = prompt.lines().last().unwrap_or("").chars().take(100).collect();
-                                                let done = json!({
-                                                    "type": "llm_done",
-                                                    "prompt": prompt_short,
-                                                    "model": format!("{} (Ollama)", model_name),
-                                                    "response": result.text,
-                                                    "tokens_generated": result.tokens_generated,
-                                                    "duration_ms": result.duration_ms,
-                                                    "tokens_per_sec": (result.tokens_per_sec * 10.0).round() / 10.0,
-                                                    "load_time_ms": 0,
-                                                    "task_id": task_id,
-                                                });
-                                                let _ = write.send(Message::Text(done.to_string())).await;
                                             }
                                             Err(e) => {
-                                                tracing::error!("Inferenssivirhe: {}", e);
+                                                let mut st = tui_state.write().await;
+                                                st.push_log("System", format!("Mallilistan haku epäonnistui: {}", e), None);
+                                            }
+                                        }
+                                    }
+                                } else if let Some(model) = cmd_str.strip_prefix("change_model:") {
+                                    // TUI:sta valittu malli — vaihdetaan
+                                    if let Some(ref engine) = llm {
+                                        engine.set_model(model.to_string());
+                                        match engine.ensure_model().await {
+                                            Ok(()) => {
+                                                tracing::info!("Malli vaihdettu: {}", model);
+                                                let mut st = tui_state.write().await;
+                                                st.model_name = model.to_string();
+                                                st.push_log("System", format!("Malli vaihdettu: {}", model), None);
+                                                // Ilmoitetaan hubille
+                                                let auth = build_auth_message(allocated_gb, model, available_models.clone());
+                                                let _ = write.send(Message::Text(auth)).await;
+                                            }
+                                            Err(e) => {
+                                                let mut st = tui_state.write().await;
+                                                st.push_log("System", format!("Mallin vaihto epäonnistui: {}", e), None);
                                             }
                                         }
                                     }
                                 }
                             }
                         }
-                        // Mallin vaihto lennossa
+                        ws_msg = read.next() => {
-                        if text.contains("change_model") {
+                            match ws_msg {
-                            if let Ok(task) = serde_json::from_str::<serde_json::Value>(&text) {
+                                Some(Ok(Message::Text(text))) => {
-                                if let Some(new_model) = task.get("model").and_then(|v| v.as_str()) {
+                                    // Hubin control-viestit
-                                    if let Some(ref engine) = llm {
+                                    if text.contains(r#""type":"control""#) {
-                                        tracing::info!("Vaihdetaan malli: {}", new_model);
+                                        if let Ok(task) = serde_json::from_str::<serde_json::Value>(&text) {
-                                        engine.set_model(new_model.to_string());
+                                            if let Some(action) = task.get("action").and_then(|v| v.as_str()) {
-                                        match engine.ensure_model().await {
+                                                if action == "pause" {
-                                            Ok(()) => tracing::info!("Malli {} valmis!", new_model),
+                                                    tracing::info!("Hub pakotti solmun tauolle (Pause)");
-                                            Err(e) => tracing::error!("Mallin lataus epäonnistui: {}", e),
+                                                    let req = json!({"type": "status_update", "status": "paused"});
+                                                    let _ = write.send(Message::Text(req.to_string())).await;
+                                                    {
+                                                        let mut st = tui_state.write().await;
+                                                        st.status = "PAUSED".to_string();
+                                                        st.push_log("Network", "Hub kytki solmun tauolle".to_string(), None);
+                                                    }
+                                                } else if action == "resume" {
+                                                    tracing::info!("Hub aktivoi solmun suorituksen (Resume)");
+                                                    let req = json!({"type": "status_update", "status": "active"});
+                                                    let _ = write.send(Message::Text(req.to_string())).await;
+                                                    {
+                                                        let mut st = tui_state.write().await;
+                                                        st.status = "ACTIVE".to_string();
+                                                        st.push_log("Network", "Hub palautti solmun töihin".to_string(), None);
+                                                    }
+                                                }
+                                            }
+                                        }
+                                    }
+                                    // Verkon globaali tila
+                                    if text.contains(r#""type":"network_status""#) {
+                                        if let Ok(status) = serde_json::from_str::<serde_json::Value>(&text) {
+                                            if let Some(nodes) = status.get("active_nodes").and_then(|v| v.as_u64()) {
+                                                if let Some(tasks) = status.get("tasks").and_then(|v| v.as_u64()) {
+                                                    let mut st = tui_state.write().await;
+                                                    st.network_active_nodes = nodes as usize;
+                                                    st.network_total_tasks = tasks;
+                                                }
+                                            }
+                                        }
+                                    }
+
+                                    // LLM-promptit
+                                    if text.contains("llm_prompt") {
+                                        if let Ok(task) = serde_json::from_str::<serde_json::Value>(&text) {
+                                            let prompt = task.get("prompt").and_then(|v| v.as_str()).unwrap_or("");
+                                            let task_id = task.get("task_id").and_then(|v| v.as_str()).unwrap_or("?");
+                                            let msg_model = task.get("model").and_then(|v| v.as_str()).unwrap_or("");
+
+                                            if !prompt.is_empty() && (msg_model.starts_with("qwen-coder") || msg_model.starts_with("qwen2.5-coder") || msg_model.starts_with("phi")) {
+                                                if let Some(ref engine) = llm {
+                                                    let gen_opts = inference::GenerateOptions {
+                                                        max_tokens: task.get("max_tokens").and_then(|v| v.as_u64()).unwrap_or(1024) as usize,
+                                                        system_prompt: task.get("system_prompt").and_then(|v| v.as_str()).map(|s| s.to_string()),
+                                                        temperature: task.get("temperature").and_then(|v| v.as_f64()),
+                                                        top_k: task.get("top_k").and_then(|v| v.as_u64()),
+                                                        repeat_penalty: task.get("repeat_penalty").and_then(|v| v.as_f64()),
+                                                        stop: task.get("stop").and_then(|v| v.as_array()).map(|a| a.iter().filter_map(|s| s.as_str().map(|s| s.to_string())).collect()),
+                                                    };
+                                                    let prompt_lines = prompt.lines().count();
+                                                    let prompt_last: String = prompt.lines().last().unwrap_or("").chars().take(60).collect();
+                                                    tracing::info!("→ task_id:{} | {}r prompti | \"{}...\"", task_id, prompt_lines, prompt_last);
+                                                    {
+                                                        let mut st = tui_state.write().await;
+                                                        st.cur_task_id = Some(task_id.to_string());
+                                                        st.cur_prompt = Some(format!("→ {} riviä | \"{}...\"", prompt_lines, prompt_last));
+                                                    }
+
+                                                    let model_name = engine.model_name();
+                                                    match engine.generate(prompt, &gen_opts).await {
+                                                        Ok(result) => {
+                                                            let tokens_sec = (result.tokens_per_sec * 10.0).round() / 10.0;
+                                                            tracing::info!(
+                                                                "✓ {} | {} tok | {:.0}ms | {:.1} tok/s",
+                                                                model_name,
+                                                                result.tokens_generated,
+                                                                result.duration_ms,
+                                                                tokens_sec,
+                                                            );
+                                                            {
+                                                                let mut st = tui_state.write().await;
+                                                                st.tasks_completed += 1;
+                                                                st.last_tokens_sec = tokens_sec as f64;
+                                                                st.cur_task_id = None;
+                                                                st.cur_prompt = None;
+                                                                
+                                                                let msg_type = if task_id == "status-check" { "Ping" } else { "Task" };
+                                                                let msg_text = format!("{}  ({} tok)", task_id, result.tokens_generated);
+                                                                st.push_log(msg_type, msg_text, Some(tokens_sec as f64));
+                                                            }
+                                                            let prompt_short: String = prompt.lines().last().unwrap_or("").chars().take(100).collect();
+                                                            let done = json!({
+                                                                "type": "llm_done",
+                                                                "prompt": prompt_short,
+                                                                "model": format!("{} (Ollama)", model_name),
+                                                                "response": result.text,
+                                                                "tokens_generated": result.tokens_generated,
+                                                                "duration_ms": result.duration_ms,
+                                                                "tokens_per_sec": tokens_sec,
+                                                                "load_time_ms": 0,
+                                                                "task_id": task_id,
+                                                            });
+                                                            let _ = write.send(Message::Text(done.to_string())).await;
+                                                        }
+                                                        Err(e) => {
+                                                            tracing::error!("Inferenssivirhe: {}", e);
+                                                            {
+                                                                let mut st = tui_state.write().await;
+                                                                st.cur_task_id = None;
+                                                                st.cur_prompt = None;
+                                                                st.push_log("System", format!("Virhe inferenssissä: {}", e), None);
+                                                            }
+                                                        }
+                                                    }
+                                                }
+                                            }
+                                        }
+                                    }
+
+                                    // Mallin vaihto lennossa
+                                    if text.contains("change_model") {
+                                        if let Ok(task) = serde_json::from_str::<serde_json::Value>(&text) {
+                                            if let Some(new_model) = task.get("model").and_then(|v| v.as_str()) {
+                                                if let Some(ref engine) = llm {
+                                                    tracing::info!("Vaihdetaan malli: {}", new_model);
+                                                    engine.set_model(new_model.to_string());
+                                                    match engine.ensure_model().await {
+                                                        Ok(()) => {
+                                                            tracing::info!("Malli {} valmis!", new_model);
+                                                            let mut st = tui_state.write().await;
+                                                            st.model_name = new_model.to_string();
+                                                            st.push_log("System", format!("Malli {} ladattu & valmis!", new_model), None);
+                                                        }
+                                                        Err(e) => tracing::error!("Mallin lataus epäonnistui: {}", e),
+                                                    }
+                                                }
+                                            }
                                         }
                                     }
                                 }
+                                Some(Ok(_)) => {} // Muut viestityypit (binary/ping)
+                                Some(Err(_)) | None => break, // Yhteys poikki
                             }
                         }
                     }
                 }
+                
                 tracing::warn!("Yhteys hubiin katkesi — yritetään uudelleen 5s...");
             }
             Err(e) => {

network-poc/native-node/src/tui.rs

@@ -0,0 +1,67 @@
+use dialoguer::{Select, Input, theme::ColorfulTheme};
+use reqwest::Client;
+
+pub async fn select_model(ollama_url: &str, client: &Client) -> Result<String, String> {
+    // 1. Hae tagit
+    let mut models = vec![];
+    println!("  Haetaan asennettuja malleja osoitteesta {}...", ollama_url);
+    if let Ok(resp) = client.get(&format!("{}/api/tags", ollama_url)).send().await {
+        if resp.status().is_success() {
+            if let Ok(json) = resp.json::<serde_json::Value>().await {
+                if let Some(arr) = json.get("models").and_then(|v| v.as_array()) {
+                    for m in arr {
+                        if let Some(name) = m.get("name").and_then(|v| v.as_str()) {
+                            models.push(name.to_string());
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    let download_opt = "[➕ Lataa uusi malli internetistä]";
+    let mut options = vec![download_opt.to_string()];
+    options.extend(models);
+
+    // 2. Kysy käyttäjältä Selectillä
+    let theme = ColorfulTheme::default();
+    let selection = Select::with_theme(&theme)
+        .with_prompt("Valitse Ollama-malli Kipinä-verkkoa varten:")
+        .default(if options.len() > 1 { 1 } else { 0 })
+        .items(&options)
+        .interact()
+        .map_err(|e| format!("TUI virhe: {}", e))?;
+
+    let selected = &options[selection];
+
+    // 3. Jos käyttäjä haluaa uuden, kysy nimeä
+    if selected == download_opt {
+        let new_model: String = Input::with_theme(&theme)
+            .with_prompt("Syötä ladattavan mallin nimi (esim. llama3 tai qwen2.5-coder:3b)")
+            .interact_text()
+            .map_err(|e| format!("TUI virhe: {}", e))?;
+        
+        let new_model = new_model.trim().to_string();
+        if new_model.is_empty() {
+            return Err("Mallin nimi ei voi olla tyhjä".to_string());
+        }
+        
+        println!("  Ladataan malleja taustalla... Tämä voi kestää hetken ({})", new_model);
+        // Odotetaan että pull on valmis
+        let pull_body = serde_json::json!({ "name": &new_model });
+        let resp = client.post(&format!("{}/api/pull", ollama_url))
+            .json(&pull_body)
+            .send()
+            .await
+            .map_err(|e| format!("Pull req virhe: {}", e))?;
+            
+        if resp.status().is_success() {
+            println!("  ✓ Malli {} ladattu onnistuneesti!", new_model);
+            return Ok(new_model);
+        } else {
+            return Err(format!("Ollama pull epäonnistui: {}", resp.status()));
+        }
+    }
+
+    Ok(selected.clone())
+}

network-poc/native-node/src/tui_dashboard.rs

@@ -0,0 +1,296 @@
+use crossterm::{
+    event::{self, Event, EventStream, KeyCode},
+    execute,
+    terminal::{disable_raw_mode, enable_raw_mode, EnterAlternateScreen, LeaveAlternateScreen},
+};
+use ratatui::{
+    backend::CrosstermBackend,
+    layout::{Constraint, Direction, Layout, Alignment},
+    style::{Color, Modifier, Style},
+    widgets::{Block, Borders, Paragraph, Wrap},
+    Terminal,
+};
+use std::io;
+use tokio::sync::RwLock;
+use std::sync::Arc;
+use futures_util::StreamExt;
+use std::time::Duration;
+
+#[derive(Clone)]
+pub struct LogEntry {
+    pub ty: String,
+    pub msg: String,
+    pub speed: Option<f64>,
+}
+
+pub struct DashboardState {
+    pub logs: Vec<LogEntry>,
+    pub status: String,
+    pub node_id: Option<u64>,
+    pub sys_info: String,
+    pub model_name: String,
+    pub cur_task_id: Option<String>,
+    pub cur_prompt: Option<String>,
+    pub tasks_completed: u32,
+    pub last_tokens_sec: f64,
+    pub network_active_nodes: usize,
+    pub network_total_tasks: u64,
+    // Mallivalikko
+    pub model_picker_open: bool,
+    pub model_picker_items: Vec<String>,
+    pub model_picker_idx: usize,
+}
+
+impl DashboardState {
+    pub fn new() -> Self {
+        Self {
+            logs: Vec::new(),
+            status: "ACTIVE".to_string(),
+            node_id: None,
+            sys_info: "".to_string(),
+            model_name: "Yhdistetään...".to_string(),
+            cur_task_id: None,
+            cur_prompt: None,
+            tasks_completed: 0,
+            last_tokens_sec: 0.0,
+            network_active_nodes: 1, // oletetaan itsemme
+            network_total_tasks: 0,
+            model_picker_open: false,
+            model_picker_items: Vec::new(),
+            model_picker_idx: 0,
+        }
+    }
+
+    pub fn push_log(&mut self, ty: &str, msg: String, speed: Option<f64>) {
+        self.logs.push(LogEntry {
+            ty: ty.to_string(),
+            msg,
+            speed,
+        });
+        if self.logs.len() > 100 {
+            self.logs.remove(0);
+        }
+    }
+}
+
+pub async fn run_dashboard(
+    state: Arc<RwLock<DashboardState>>,
+    cmd_tx: tokio::sync::mpsc::UnboundedSender<String>,
+) -> Result<(), io::Error> {
+    enable_raw_mode()?;
+    let mut stdout = io::stdout();
+    execute!(stdout, EnterAlternateScreen)?;
+    let backend = CrosstermBackend::new(stdout);
+    let mut terminal = Terminal::new(backend)?;
+    terminal.clear()?;
+
+    let mut reader = EventStream::new();
+    let mut interval = tokio::time::interval(Duration::from_millis(100));
+
+    loop {
+        tokio::select! {
+            _ = interval.tick() => {
+                let st = state.read().await;
+                terminal.draw(|f| ui(f, &st))?;
+            }
+            ev = reader.next() => {
+                if let Some(Ok(Event::Key(key))) = ev {
+                    let picker_open = state.read().await.model_picker_open;
+
+                    if picker_open {
+                        // Mallivalikko auki — navigointi
+                        match key.code {
+                            KeyCode::Up | KeyCode::Char('k') => {
+                                let mut st = state.write().await;
+                                if st.model_picker_idx > 0 { st.model_picker_idx -= 1; }
+                            }
+                            KeyCode::Down | KeyCode::Char('j') => {
+                                let mut st = state.write().await;
+                                let max = st.model_picker_items.len().saturating_sub(1);
+                                if st.model_picker_idx < max { st.model_picker_idx += 1; }
+                            }
+                            KeyCode::Enter => {
+                                let mut st = state.write().await;
+                                let idx = st.model_picker_idx;
+                                if let Some(model) = st.model_picker_items.get(idx).cloned() {
+                                    st.model_picker_open = false;
+                                    st.push_log("System", format!("Vaihdetaan malliin: {}...", model), None);
+                                    let _ = cmd_tx.send(format!("change_model:{}", model));
+                                }
+                            }
+                            KeyCode::Esc | KeyCode::Char('m') | KeyCode::Char('M') => {
+                                state.write().await.model_picker_open = false;
+                            }
+                            _ => {}
+                        }
+                    } else {
+                        // Normaali tila
+                        match key.code {
+                            KeyCode::Char('q') | KeyCode::Esc => {
+                                disable_raw_mode()?;
+                                execute!(terminal.backend_mut(), LeaveAlternateScreen)?;
+                                std::process::exit(0);
+                            }
+                            KeyCode::Char('p') | KeyCode::Char('P') => {
+                                let _ = cmd_tx.send("pause".to_string());
+                            }
+                            KeyCode::Char('r') | KeyCode::Char('R') | KeyCode::Char('s') => {
+                                let _ = cmd_tx.send("resume".to_string());
+                            }
+                            KeyCode::Char('m') | KeyCode::Char('M') => {
+                                let _ = cmd_tx.send("fetch_models".to_string());
+                            }
+                            _ => {}
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+pub fn restore_terminal() {
+    let _ = disable_raw_mode();
+    let _ = execute!(io::stdout(), LeaveAlternateScreen);
+}
+
+fn ui(f: &mut ratatui::Frame, st: &DashboardState) {
+    let chunks = Layout::default()
+        .direction(Direction::Vertical)
+        .constraints([
+            Constraint::Length(3),  // Header
+            Constraint::Min(0),     // Body
+            Constraint::Length(3),  // Footer / Status
+        ].as_ref())
+        .split(f.area());
+
+    // --- Header ---
+    let header_text = match st.node_id {
+        Some(id) => format!(" Kipinä Agentic Node #{} ", id),
+        None => " Kipinä Agentic Node (Yhdistää...) ".to_string(),
+    };
+    let header = Paragraph::new(header_text)
+        .style(Style::default().fg(Color::Cyan).add_modifier(Modifier::BOLD))
+        .alignment(Alignment::Center)
+        .block(Block::default().borders(Borders::ALL).style(Style::default().fg(Color::DarkGray)));
+    f.render_widget(header, chunks[0]);
+
+    // --- Body ---
+    let body_chunks = Layout::default()
+        .direction(Direction::Vertical)
+        .constraints([
+            Constraint::Length(7), // Yläosan info ja tehtävä
+            Constraint::Min(0),    // Lokit / Chat alas
+        ].as_ref())
+        .split(chunks[1]);
+
+    let top_panels = Layout::default()
+        .direction(Direction::Horizontal)
+        .constraints([
+            Constraint::Percentage(40), // Vasen paneeli (Info)
+            Constraint::Percentage(60), // Oikea paneeli (Tehtävä)
+        ].as_ref())
+        .split(body_chunks[0]);
+
+    // Vasen paneeli: Laitteisto, Malli & Verkosto
+    let info_text = format!(
+        "🚀 Malli: {}\n💻 Järjestelmä: {}\n📊 Tehdyt: {} | Nopeus: {} t/s\n🌐 Verkosto: {} solmua | {} tehtävää",
+        st.model_name, st.sys_info, st.tasks_completed, st.last_tokens_sec, st.network_active_nodes, st.network_total_tasks
+    );
+    let left_panel = Paragraph::new(info_text)
+        .block(Block::default().title(" Laitteisto ja AI ").borders(Borders::ALL))
+        .style(Style::default().fg(Color::White))
+        .wrap(Wrap { trim: true });
+    f.render_widget(left_panel, top_panels[0]);
+
+    // Oikea paneeli: Käynnissä oleva tehtävä
+    let task_title = match &st.cur_task_id {
+        Some(id) => format!(" Työn alla: {} ", id),
+        None => " Vapaana ".to_string(),
+    };
+    let task_content = st.cur_prompt.clone().unwrap_or_else(|| "Odotetaan tehtäviä Hubilta...".to_string());
+    
+    let task_style = if st.cur_task_id.is_some() {
+        Style::default().fg(Color::Magenta)
+    } else {
+        Style::default().fg(Color::DarkGray)
+    };
+    
+    let task_panel = Paragraph::new(task_content)
+        .wrap(Wrap { trim: true })
+        .block(Block::default().title(task_title).borders(Borders::ALL).style(task_style));
+    f.render_widget(task_panel, top_panels[1]);
+
+    // Alaosan paneeli: Tapahtumaloki koko leveydeltä
+    let area_height = body_chunks[1].height.saturating_sub(2) as usize;
+    let skip_count = if st.logs.len() > area_height { st.logs.len() - area_height } else { 0 };
+    
+    let visible_logs: Vec<ratatui::text::Line> = st.logs.iter().skip(skip_count).map(|log| {
+        let ty_color = match log.ty.as_str() {
+            "System" => Color::Yellow,
+            "Network" => Color::Blue,
+            "Task" => Color::Magenta,
+            "Ping" => Color::DarkGray,
+            _ => Color::White,
+        };
+        
+        let speed_str = if let Some(s) = log.speed {
+            format!(" | {:.1} tok/s", s)
+        } else {
+            "".to_string()
+        };
+        
+        ratatui::text::Line::from(vec![
+            ratatui::text::Span::styled(format!("{: <8}", log.ty), Style::default().fg(ty_color).add_modifier(Modifier::BOLD)),
+            ratatui::text::Span::raw(" | "),
+            ratatui::text::Span::styled(log.msg.clone(), Style::default().fg(Color::White)),
+            ratatui::text::Span::styled(speed_str, Style::default().fg(ty_color)),
+        ])
+    }).collect();
+
+    let logs_panel = Paragraph::new(visible_logs)
+        .block(Block::default().title(" Tapahtumaloki ").borders(Borders::ALL).style(Style::default().fg(Color::Cyan)));
+    f.render_widget(logs_panel, body_chunks[1]);
+
+    // --- Footer / Status ---
+    let status_color = if st.status == "ACTIVE" { Color::Green } else { Color::Yellow };
+    let status_text = format!(" Tila: {} | [P] Pause [R] Työhön [M] Malli [Q] Sulje ", st.status);
+    let footer = Paragraph::new(status_text)
+        .style(Style::default().fg(status_color).add_modifier(Modifier::BOLD))
+        .alignment(Alignment::Center)
+        .block(Block::default().borders(Borders::ALL));
+    f.render_widget(footer, chunks[2]);
+
+    // --- Mallivalikko-overlay ---
+    if st.model_picker_open && !st.model_picker_items.is_empty() {
+        let area = f.area();
+        let popup_h = (st.model_picker_items.len() as u16 + 4).min(area.height - 4);
+        let popup_w = 50.min(area.width - 4);
+        let popup = ratatui::layout::Rect::new(
+            (area.width - popup_w) / 2,
+            (area.height - popup_h) / 2,
+            popup_w,
+            popup_h,
+        );
+
+        // Tausta
+        f.render_widget(ratatui::widgets::Clear, popup);
+
+        let items: Vec<ratatui::text::Line> = st.model_picker_items.iter().enumerate().map(|(i, name)| {
+            if i == st.model_picker_idx {
+                ratatui::text::Line::from(format!(" ▸ {} ", name))
+                    .style(Style::default().fg(Color::Cyan).add_modifier(Modifier::BOLD))
+            } else {
+                ratatui::text::Line::from(format!("   {} ", name))
+                    .style(Style::default().fg(Color::White))
+            }
+        }).collect();
+
+        let picker = Paragraph::new(items)
+            .block(Block::default()
+                .title(" Vaihda malli [↑↓] Enter=valitse Esc=peruuta ")
+                .borders(Borders::ALL)
+                .style(Style::default().fg(Color::Cyan)));
+        f.render_widget(picker, popup);
+    }
+}

network-poc/node/Cargo.toml

@@ -10,32 +10,22 @@ crate-type = ["cdylib"]
 wasm-bindgen = "0.2.91"
 js-sys = "0.3.68"
 web-sys = { version = "0.3.68", features = [
-    "Window",
-    "Document",
-    "HtmlElement",
     "WebSocket",
     "MessageEvent",
     "Performance",
     "console",
-    "Request",
-    "RequestInit",
     "Response",
-    "Headers",
     "ReadableStream",
     "ReadableStreamDefaultReader",
 ] }
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
-burn = { version = "0.14.0", features = ["wgpu", "ndarray"] }
-burn-wgpu = "0.14.0"
-burn-ndarray = "0.14.0"
 wasm-bindgen-futures = "0.4"
 console_error_panic_hook = "0.1.7"
 reqwest = { version = "0.12", default-features = false, features = ["json"] }
 tokenizers = { version = "0.19.1", default-features = false, features = ["unstable_wasm"] }
 rexie = "0.6"
-log = "0.4"
+candle-core = "0.8"
-candle-core = { version = "0.8" }
 candle-nn = "0.8"
 candle-transformers = "0.8"
 getrandom = { version = "0.3", features = ["wasm_js"] }

network-poc/node/src/burn_smollm/attention.rs

@@ -1,118 +0,0 @@
-use burn::module::{Module, Param};
-use burn::tensor::{backend::Backend, Tensor};
-use super::rope::RoPE;
-use super::config::SmolLMConfig;
-
-#[derive(Clone, Debug)]
-pub struct KVCache<B: Backend> {
-    pub k: Tensor<B, 4>,
-    pub v: Tensor<B, 4>,
-}
-
-#[derive(Module, Debug)]
-pub struct Attention<B: Backend> {
-    pub q_proj: Param<Tensor<B, 2>>,  // [hidden, num_heads * head_dim]
-    pub k_proj: Param<Tensor<B, 2>>,  // [hidden, num_kv_heads * head_dim]
-    pub v_proj: Param<Tensor<B, 2>>,  // [hidden, num_kv_heads * head_dim]
-    pub o_proj: Param<Tensor<B, 2>>,  // [num_heads * head_dim, hidden]
-
-    num_heads: usize,
-    num_kv_heads: usize,
-    head_dim: usize,
-
-    rope: RoPE<B>,
-}
-
-impl<B: Backend> Attention<B> {
-    pub fn new(config: &SmolLMConfig, device: &B::Device) -> Self {
-        let head_dim = config.hidden_size / config.num_attention_heads;
-
-        Self {
-            q_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_attention_heads * head_dim], device)),
-            k_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_key_value_heads * head_dim], device)),
-            v_proj: Param::from_tensor(Tensor::zeros([config.hidden_size, config.num_key_value_heads * head_dim], device)),
-            o_proj: Param::from_tensor(Tensor::zeros([config.num_attention_heads * head_dim, config.hidden_size], device)),
-
-            num_heads: config.num_attention_heads,
-            num_kv_heads: config.num_key_value_heads,
-            head_dim,
-
-            rope: RoPE::new(head_dim, config.max_position_embeddings, config.rope_theta, device),
-        }
-    }
-
-    pub fn forward(
-        &self,
-        x: Tensor<B, 3>,
-        offset: usize,
-        cache: Option<KVCache<B>>
-    ) -> (Tensor<B, 3>, KVCache<B>) {
-        let [batch, seq_len, hidden_dim] = x.dims();
-
-        // Project Q, K, V: x @ W -> [batch, seq, proj_dim]
-        let q = x.clone().matmul(self.q_proj.val().unsqueeze());
-        let k = x.clone().matmul(self.k_proj.val().unsqueeze());
-        let v = x.matmul(self.v_proj.val().unsqueeze());
-
-        // Reshape: [batch, seq, heads, head_dim] -> [batch, heads, seq, head_dim]
-        let q = q.reshape([batch, seq_len, self.num_heads, self.head_dim]).swap_dims(1, 2);
-        let k = k.reshape([batch, seq_len, self.num_kv_heads, self.head_dim]).swap_dims(1, 2);
-        let v = v.reshape([batch, seq_len, self.num_kv_heads, self.head_dim]).swap_dims(1, 2);
-
-        // Apply RoPE
-        let q = self.rope.forward(q, offset);
-        let k = self.rope.forward(k, offset);
-
-        // KV cache
-        let (k, v) = if let Some(c) = cache {
-            (Tensor::cat(vec![c.k, k], 2), Tensor::cat(vec![c.v, v], 2))
-        } else {
-            (k, v)
-        };
-
-        let new_cache = KVCache { k: k.clone(), v: v.clone() };
-        let kv_len = k.dims()[2];
-
-        // GQA: repeat K,V heads — [batch, kv_heads, kv_len, hd] -> [batch, num_heads, kv_len, hd]
-        let num_reps = self.num_heads / self.num_kv_heads;
-        let k = if num_reps > 1 {
-            let [b, kv_h, s, hd] = k.dims();
-            k.reshape([b, kv_h, 1, s, hd]).repeat_dim(2, num_reps).reshape([b, self.num_heads, s, hd])
-        } else { k };
-        let v = if num_reps > 1 {
-            let [b, kv_h, s, hd] = v.dims();
-            v.reshape([b, kv_h, 1, s, hd]).repeat_dim(2, num_reps).reshape([b, self.num_heads, s, hd])
-        } else { v };
-
-        // Attention: Q @ K^T / sqrt(d)
-        let scale = 1.0 / (self.head_dim as f64).sqrt();
-        let scores = q.matmul(k.swap_dims(2, 3)).mul_scalar(scale);
-        // scores: [batch, heads, seq_len, kv_len]
-
-        // Causal mask for prefill (seq_len > 1)
-        let scores = if seq_len > 1 {
-            let mask_data: Vec<f32> = (0..seq_len).flat_map(|i| {
-                (0..kv_len).map(move |j| {
-                    if j > offset + i { f32::NEG_INFINITY } else { 0.0 }
-                })
-            }).collect();
-            let mask = Tensor::<B, 2>::from_data(
-                burn::tensor::TensorData::new(mask_data, [seq_len, kv_len]),
-                &scores.device()
-            ).reshape([1, 1, seq_len, kv_len]);
-            scores + mask
-        } else {
-            scores
-        };
-
-        let attn_weights = burn::tensor::activation::softmax(scores, 3);
-
-        let context = attn_weights.matmul(v);
-        // [batch, heads, seq, hd] -> [batch, seq, heads*hd]
-        let context = context.swap_dims(1, 2).reshape([batch, seq_len, self.num_heads * self.head_dim]);
-
-        let output = context.matmul(self.o_proj.val().unsqueeze());
-
-        (output, new_cache)
-    }
-}

network-poc/node/src/burn_smollm/config.rs

@@ -1,28 +0,0 @@
-#[derive(Clone, Debug)]
-pub struct SmolLMConfig {
-    pub hidden_size: usize,
-    pub intermediate_size: usize,
-    pub vocab_size: usize,
-    pub num_hidden_layers: usize,
-    pub num_attention_heads: usize,
-    pub num_key_value_heads: usize,
-    pub rms_norm_eps: f64,
-    pub rope_theta: f32,
-    pub max_position_embeddings: usize,
-}
-
-impl Default for SmolLMConfig {
-    fn default() -> Self {
-        Self {
-            hidden_size: 576,
-            intermediate_size: 1536,
-            vocab_size: 49152,
-            num_hidden_layers: 30,
-            num_attention_heads: 9,
-            num_key_value_heads: 3,
-            rms_norm_eps: 1e-5,
-            rope_theta: 10000.0,
-            max_position_embeddings: 2048,
-        }
-    }
-}

network-poc/node/src/burn_smollm/loader.rs

@@ -1,90 +0,0 @@
-use burn::tensor::{backend::Backend, Tensor, TensorData};
-use candle_core::safetensors;
-use candle_core::Device as CandleDevice;
-use burn::module::Param;
-use super::model::LlamaModel;
-use super::config::SmolLMConfig;
-
-fn load_tensor_2d<B: Backend>(
-    tensors_map: &std::collections::HashMap<String, candle_core::Tensor>,
-    name: &str,
-    device: &B::Device,
-    shape_out_in: [usize; 2]
-) -> Result<Param<Tensor<B, 2>>, String> {
-    let t = tensors_map.get(name).ok_or_else(|| format!("Puuttuu: {}", name))?;
-    let t = t.to_dtype(candle_core::DType::F32).unwrap();
-    let vec = t.flatten_all().unwrap().to_vec1::<f32>().unwrap();
-    let t_burn = Tensor::<B, 2>::from_data(burn::tensor::TensorData::new(vec, shape_out_in), device);
-    // transpose from [out, in] to [in, out]
-    Ok(Param::from_tensor(t_burn.transpose()))
-}
-
-fn load_tensor_1d<B: Backend>(
-    tensors_map: &std::collections::HashMap<String, candle_core::Tensor>,
-    name: &str,
-    device: &B::Device,
-    _shape: [usize; 1]
-) -> Result<Param<Tensor<B, 1>>, String> {
-    let t = tensors_map.get(name).ok_or_else(|| format!("Puuttuu: {}", name))?;
-    let t = t.to_dtype(candle_core::DType::F32).unwrap();
-    let vec = t.flatten_all().unwrap().to_vec1::<f32>().unwrap();
-    Ok(Param::from_tensor(Tensor::<B, 1>::from_floats(vec.as_slice(), device)))
-}
-
-fn load_embed<B: Backend>(
-    tensors_map: &std::collections::HashMap<String, candle_core::Tensor>,
-    name: &str,
-    device: &B::Device,
-    shape: [usize; 2]
-) -> Result<Param<Tensor<B, 2>>, String> {
-    let t = tensors_map.get(name).ok_or_else(|| format!("Puuttuu: {}", name))?;
-    let t = t.to_dtype(candle_core::DType::F32).unwrap();
-    let vec = t.flatten_all().unwrap().to_vec1::<f32>().unwrap();
-    // Embed ei transponoi samalla tavalla, se pysyy [vocab, hidden]
-    Ok(Param::from_tensor(Tensor::<B, 2>::from_data(burn::tensor::TensorData::new(vec, shape), device)))
-}
-
-pub fn load_safetensors_to_model<B: Backend>(
-    buffer: &[u8], 
-    config: &SmolLMConfig, 
-    device: &B::Device
-) -> Result<LlamaModel<B>, String> {
-    
-    let mut model = LlamaModel::new(config, device);
-    let tensors_map = safetensors::load_buffer(buffer, &CandleDevice::Cpu)
-        .map_err(|e| format!("Virhe Safetensors luennassa: {}", e))?;
-    
-    // Embeddings
-    model.embed_tokens = load_embed(&tensors_map, "model.embed_tokens.weight", device, [config.vocab_size, config.hidden_size])?;
-    model.norm.weight = load_tensor_1d(&tensors_map, "model.norm.weight", device, [config.hidden_size])?;
-    model.lm_head = load_embed(&tensors_map, "lm_head.weight", device, [config.vocab_size, config.hidden_size]).or_else(|_| {
-        load_embed(&tensors_map, "model.embed_tokens.weight", device, [config.vocab_size, config.hidden_size])
-    })?;
-    
-    let head_dim = config.hidden_size / config.num_attention_heads;
-
-    for i in 0..config.num_hidden_layers {
-        let prefix = format!("model.layers.{}", i);
-        
-        let layer = &mut model.layers[i];
-        
-        // Norms
-        layer.input_layernorm.weight = load_tensor_1d(&tensors_map, &format!("{}.input_layernorm.weight", prefix), device, [config.hidden_size])?;
-        layer.post_attention_layernorm.weight = load_tensor_1d(&tensors_map, &format!("{}.post_attention_layernorm.weight", prefix), device, [config.hidden_size])?;
-        
-        // Attention
-        let num_heads = config.num_attention_heads;
-        let num_kv_heads = config.num_key_value_heads;
-        layer.self_attn.q_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.q_proj.weight", prefix), device, [num_heads * head_dim, config.hidden_size])?;
-        layer.self_attn.k_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.k_proj.weight", prefix), device, [num_kv_heads * head_dim, config.hidden_size])?;
-        layer.self_attn.v_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.v_proj.weight", prefix), device, [num_kv_heads * head_dim, config.hidden_size])?;
-        layer.self_attn.o_proj = load_tensor_2d(&tensors_map, &format!("{}.self_attn.o_proj.weight", prefix), device, [config.hidden_size, num_heads * head_dim])?;
-        
-        // MLP
-        layer.mlp.gate_proj = load_tensor_2d(&tensors_map, &format!("{}.mlp.gate_proj.weight", prefix), device, [config.intermediate_size, config.hidden_size])?;
-        layer.mlp.up_proj = load_tensor_2d(&tensors_map, &format!("{}.mlp.up_proj.weight", prefix), device, [config.intermediate_size, config.hidden_size])?;
-        layer.mlp.down_proj = load_tensor_2d(&tensors_map, &format!("{}.mlp.down_proj.weight", prefix), device, [config.hidden_size, config.intermediate_size])?;
-    }
-    
-    Ok(model)
-}

network-poc/node/src/burn_smollm/mod.rs

@@ -1,6 +0,0 @@
-pub mod attention;
-pub mod config;
-pub mod loader;
-pub mod model;
-pub mod modules;
-pub mod rope;

network-poc/node/src/burn_smollm/model.rs

@@ -1,96 +0,0 @@
-use burn::module::{Module, Param};
-use burn::tensor::{backend::Backend, Tensor, Int};
-use super::modules::{RmsNorm, Mlp};
-use super::attention::{Attention, KVCache};
-use super::config::SmolLMConfig;
-
-#[derive(Module, Debug)]
-pub struct LlamaBlock<B: Backend> {
-    pub self_attn: Attention<B>,
-    pub mlp: Mlp<B>,
-    pub input_layernorm: RmsNorm<B>,
-    pub post_attention_layernorm: RmsNorm<B>,
-}
-
-impl<B: Backend> LlamaBlock<B> {
-    pub fn new(config: &SmolLMConfig, device: &B::Device) -> Self {
-        Self {
-            self_attn: Attention::new(config, device),
-            mlp: Mlp::new(config.hidden_size, config.intermediate_size, device),
-            input_layernorm: RmsNorm::new(config.hidden_size, config.rms_norm_eps, device),
-            post_attention_layernorm: RmsNorm::new(config.hidden_size, config.rms_norm_eps, device),
-        }
-    }
-
-    pub fn forward(
-        &self,
-        x: Tensor<B, 3>,
-        offset: usize,
-        cache: Option<KVCache<B>>
-    ) -> (Tensor<B, 3>, KVCache<B>) {
-        let residual = x.clone();
-        let x_norm = self.input_layernorm.forward(x);
-        
-        let (attn_out, new_cache) = self.self_attn.forward(x_norm, offset, cache);
-        
-        let x = residual + attn_out;
-        
-        let residual = x.clone();
-        let x_norm = self.post_attention_layernorm.forward(x);
-        let mlp_out = self.mlp.forward(x_norm);
-        
-        let x = residual + mlp_out;
-        (x, new_cache)
-    }
-}
-
-#[derive(Module, Debug)]
-pub struct LlamaModel<B: Backend> {
-    pub embed_tokens: Param<Tensor<B, 2>>,
-    pub layers: Vec<LlamaBlock<B>>,
-    pub norm: RmsNorm<B>,
-    pub lm_head: Param<Tensor<B, 2>>, // For tie_word_embeddings this can point to embed_tokens
-}
-
-impl<B: Backend> LlamaModel<B> {
-    pub fn new(config: &SmolLMConfig, device: &B::Device) -> Self {
-        let embed = Tensor::zeros([config.vocab_size, config.hidden_size], device);
-        let lm_head = Tensor::zeros([config.vocab_size, config.hidden_size], device);
-        
-        let mut layers = Vec::new();
-        for _ in 0..config.num_hidden_layers {
-            layers.push(LlamaBlock::new(config, device));
-        }
-
-        Self {
-            embed_tokens: Param::from_tensor(embed),
-            layers,
-            norm: RmsNorm::new(config.hidden_size, config.rms_norm_eps, device),
-            lm_head: Param::from_tensor(lm_head),
-        }
-    }
-
-    pub fn forward(
-        &self,
-        input_ids: Tensor<B, 2, Int>,
-        offset: usize,
-        caches: &mut Vec<Option<KVCache<B>>>
-    ) -> Tensor<B, 3> {
-        let [_batch, _seq_len] = input_ids.dims();
-        
-        let mut x = burn::tensor::module::embedding(self.embed_tokens.val(), input_ids);
-        
-        for (i, layer) in self.layers.iter().enumerate() {
-            let cache = caches[i].take();
-            let (out, new_cache) = layer.forward(x, offset, cache);
-            x = out;
-            caches[i] = Some(new_cache);
-        }
-        
-        x = self.norm.forward(x);
-        
-        // Matmul with lm_head (or embed_tokens if tied) to get logits
-        // Notice: lm_head is typically [vocab_size, hidden_size] in HF, so we swap dims
-        x.matmul(self.lm_head.val().swap_dims(0, 1).unsqueeze())
-    }
-}

network-poc/node/src/burn_smollm/modules.rs

@@ -1,59 +0,0 @@
-use burn::module::{Module, Param};
-use burn::tensor::{backend::Backend, Tensor};
-
-#[derive(Module, Debug)]
-pub struct RmsNorm<B: Backend> {
-    pub weight: Param<Tensor<B, 1>>,
-    epsilon: f64,
-}
-
-impl<B: Backend> RmsNorm<B> {
-    pub fn new(size: usize, epsilon: f64, device: &B::Device) -> Self {
-        let weight = Param::from_tensor(Tensor::ones([size], device));
-        Self { weight, epsilon }
-    }
-
-    pub fn forward(&self, x: Tensor<B, 3>) -> Tensor<B, 3> {
-        // x: [batch, seq_len, dim]
-        // RMSNorm: x * weight / sqrt(mean(x^2) + eps)
-        let x_sq = x.clone().powf_scalar(2.0);
-        // mean over last dim, keeping dims for broadcast
-        let [b, s, d] = x_sq.dims();
-        let variance = x_sq.sum_dim(2).div_scalar(d as f32);
-        let norm = x.div(variance.add_scalar(self.epsilon).sqrt());
-
-        let w = self.weight.val().unsqueeze::<2>().unsqueeze::<3>().reshape([1, 1, d]);
-        norm * w
-    }
-}
-
-#[derive(Module, Debug)]
-pub struct Mlp<B: Backend> {
-    pub gate_proj: Param<Tensor<B, 2>>, // [in, intermediate]
-    pub up_proj: Param<Tensor<B, 2>>,   // [in, intermediate]
-    pub down_proj: Param<Tensor<B, 2>>, // [intermediate, out]
-}
-
-impl<B: Backend> Mlp<B> {
-    pub fn new(hidden_size: usize, intermediate_size: usize, device: &B::Device) -> Self {
-        Self {
-            gate_proj: Param::from_tensor(Tensor::zeros([hidden_size, intermediate_size], device)),
-            up_proj: Param::from_tensor(Tensor::zeros([hidden_size, intermediate_size], device)),
-            down_proj: Param::from_tensor(Tensor::zeros([intermediate_size, hidden_size], device)),
-        }
-    }
-
-    pub fn forward(&self, x: Tensor<B, 3>) -> Tensor<B, 3> {
-        // x: [batch, seq, hidden]
-        // gate = x @ gate_proj  -> [batch, seq, intermediate]
-        let gate = x.clone().matmul(self.gate_proj.val().unsqueeze());
-        let up = x.matmul(self.up_proj.val().unsqueeze());
-
-        // SiLU(gate) * up
-        let silu = gate.clone() * burn::tensor::activation::sigmoid(gate);
-        let intermediate = silu * up;
-
-        // intermediate @ down_proj -> [batch, seq, hidden]
-        intermediate.matmul(self.down_proj.val().unsqueeze())
-    }
-}

network-poc/node/src/burn_smollm/rope.rs

@@ -1,59 +0,0 @@
-use burn::module::Module;
-use burn::tensor::{backend::Backend, Tensor};
-
-#[derive(Module, Debug)]
-pub struct RoPE<B: Backend> {
-    cos_cache: Tensor<B, 2>,
-    sin_cache: Tensor<B, 2>,
-}
-
-impl<B: Backend> RoPE<B> {
-    pub fn new(head_dim: usize, max_seq_len: usize, theta: f32, device: &B::Device) -> Self {
-        // (head_dim / 2) values
-        let half_dim = head_dim / 2;
-        let inv_freq: Vec<f32> = (0..half_dim)
-            .map(|i| 1.0 / theta.powf((2 * i) as f32 / head_dim as f32))
-            .collect();
-        
-        let inv_freq = Tensor::<B, 1>::from_floats(inv_freq.as_slice(), device).unsqueeze::<2>();
-        let t_floats: Vec<f32> = (0..max_seq_len).map(|v| v as f32).collect();
-        let t = Tensor::<B, 1>::from_floats(t_floats.as_slice(), device).unsqueeze::<2>().transpose();
-        // t shape: [max_seq_len, 1]
-        // inv_freq shape: [1, half_dim]
-        
-        // freqs shape: [max_seq_len, half_dim]
-        let freqs = t.matmul(inv_freq);
-        
-        let cos_cache = freqs.clone().cos();
-        let sin_cache = freqs.sin();
-
-        Self {
-            cos_cache,
-            sin_cache,
-        }
-    }
-
-    pub fn forward(&self, x: Tensor<B, 4>, offset: usize) -> Tensor<B, 4> {
-        let [batch, heads, seq_len, head_dim] = x.dims();
-        let half_dim = head_dim / 2;
-        
-        // x shape: [batch, heads, seq_len, head_dim]
-        // valitaan viipaleet (x1 ja x2) jotta saadaan pyöritettyä rotaatiot
-        let x1 = x.clone().slice([0..batch, 0..heads, 0..seq_len, 0..half_dim]);
-        let x2 = x.clone().slice([0..batch, 0..heads, 0..seq_len, half_dim..head_dim]);
-        
-        // haetaan vastaava seq offsetista alkaen
-        let cos = self.cos_cache.clone().slice([offset..offset+seq_len, 0..half_dim])
-            .unsqueeze::<4>() // [seq, half_dim, 1]
-            .reshape([1, 1, seq_len, half_dim]);
-        let sin = self.sin_cache.clone().slice([offset..offset+seq_len, 0..half_dim])
-            .reshape([1, 1, seq_len, half_dim]);
-
-        // x1 * cos - x2 * sin
-        let o1 = x1.clone().mul(cos.clone()) - x2.clone().mul(sin.clone());
-        // x2 * cos + x1 * sin
-        let o2 = x2.mul(cos) + x1.mul(sin);
-
-        Tensor::cat(vec![o1, o2], 3)
-    }
-}

network-poc/node/src/lib.rs

@@ -3,16 +3,11 @@ use web_sys::{WebSocket, MessageEvent};
 use std::cell::RefCell;
 use std::rc::Rc;
 use std::sync::atomic::{AtomicU32, AtomicBool, Ordering};
-use burn::tensor::Tensor;
-use burn::backend::{Wgpu, NdArray};
 
 pub mod storage;
 pub mod sampling;
-pub mod smollm;
 pub mod qwen;
 pub mod qwen_coder;
-pub mod phi3;
-pub mod burn_smollm;
 
 #[macro_export]
 macro_rules! console_log {
@@ -82,41 +77,6 @@ pub async fn worker_fetch(url: &str) -> Result<web_sys::Response, String> {
         .map_err(|_| "ei Response".to_string())
 }
 
-// Geneerinen tensorilaskenta — toimii millä tahansa Burn-backendillä
-fn run_matmul<B: burn::tensor::backend::Backend>(size: usize) -> String {
-    let device = Default::default();
-    let dist = burn::tensor::Distribution::Default;
-    let t1: Tensor<B, 2> = Tensor::random([size, size], dist, &device);
-    let t2: Tensor<B, 2> = Tensor::random([size, size], dist, &device);
-    let sum = t1.matmul(t2).sum();
-    format!("{:?}", sum)
-}
-
-// Päättelyfunktio — valitsee backendin automaattisesti
-async fn run_ai_tensor_inference(difficulty: usize) -> String {
-    let load_pct = GPU_LOAD_PERCENT.load(Ordering::SeqCst);
-
-    if load_pct == 0 {
-        sleep_ms(2000).await;
-        return format!("Paused (0%). Lepäillään zZz..");
-    }
-
-    let active_workload_size = (difficulty as f32 * (load_pct as f32 / 100.0)) as usize;
-
-    let sleep_delay = (100 - load_pct) * 10;
-    if sleep_delay > 0 {
-        sleep_ms(sleep_delay as i32).await;
-    }
-
-    let use_gpu = HAS_WEBGPU.load(Ordering::SeqCst);
-    let (backend_name, result) = if use_gpu {
-        ("WebGPU", run_matmul::<Wgpu>(active_workload_size))
-    } else {
-        ("CPU/NdArray", run_matmul::<NdArray>(active_workload_size))
-    };
-
-    format!("PoC {} Matmul ({}x{}) >> {}", backend_name, active_workload_size, active_workload_size, result)
-}
 
 /// JS-exportti: tokenisoi tekstin ja palauttaa JSON-merkkijonon
 /// Tokenizer ladataan IndexedDB:stä (täytyy olla ladattu aiemmin)
@@ -246,7 +206,7 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
     HAS_WEBGPU.store(has_webgpu, Ordering::SeqCst);
     SELECTED_TASK.store(task_id, Ordering::SeqCst);
     let backend_name = if has_webgpu { "WebGPU" } else { "CPU (NdArray)" };
-    let task_names = ["tokenize", "smollm-135m", "qwen-05b", "phi3-mini", "qwen-coder-05b", "qwen-coder-3b"];
+    let task_names = ["tokenize", "qwen-05b", "qwen-coder-05b", "qwen-coder-3b"];
     let task_name = task_names.get(task_id as usize).unwrap_or(&"tokenize");
     console_log!("Kipinä Agent Node käynnistyy — backend: {} | tehtävä: {}", backend_name, task_name);
 
@@ -303,22 +263,6 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
                     }
                 }
             } else if msg.contains("llm_prompt") && current_task == 1 && auto_on {
-                // Vain SmolLM-solmut, ja vain yksi inferenssi kerrallaan
-                if LLM_BUSY.load(Ordering::SeqCst) {
-                    // Ohitetaan — edellinen inferenssi vielä käynnissä
-                } else if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
-                    let prompt = task.get("prompt").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    let model = task.get("model").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    if !prompt.is_empty() && model == "smollm-135m" {
-                        LLM_BUSY.store(true, Ordering::SeqCst);
-                        let ws_for_async = ws_clone.clone();
-                        wasm_bindgen_futures::spawn_local(async move {
-                            smollm::run_smollm_inference(prompt, ws_for_async).await;
-                            LLM_BUSY.store(false, Ordering::SeqCst);
-                        });
-                    }
-                }
-            } else if msg.contains("llm_prompt") && current_task == 2 && auto_on {
                 // Qwen2.5-0.5B
                 if LLM_BUSY.load(Ordering::SeqCst) {
                 } else if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
@@ -333,21 +277,6 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
                         });
                     }
                 }
-            } else if msg.contains("llm_prompt") && current_task == 3 && auto_on {
-                // Phi-3 Mini
-                if LLM_BUSY.load(Ordering::SeqCst) {
-                } else if let Ok(task) = serde_json::from_str::<serde_json::Value>(&msg) {
-                    let prompt = task.get("prompt").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    let model = task.get("model").and_then(|v| v.as_str()).unwrap_or("").to_string();
-                    if !prompt.is_empty() && model.starts_with("phi3-mini") {
-                        LLM_BUSY.store(true, Ordering::SeqCst);
-                        let ws_for_async = ws_clone.clone();
-                        wasm_bindgen_futures::spawn_local(async move {
-                            phi3::run_phi3_inference(prompt, ws_for_async).await;
-                            LLM_BUSY.store(false, Ordering::SeqCst);
-                        });
-                    }
-                }
             } else if msg.contains("llm_prompt") {
                 console_log!("[DEBUG] llm_prompt vastaanotettu! current_task={}, busy={}", current_task, LLM_BUSY.load(Ordering::SeqCst));
                 if current_task == 4 || current_task == 5 {
@@ -368,28 +297,23 @@ pub async fn start_agent_node(hub_url: String, has_webgpu: bool, device_info_jso
                                 let _ = ws_clone.borrow().send_with_str(&err_msg.to_string());
                             }
                         } else {
+                            // Välitetään parametrit JSON-promptina coderille
+                            let coder_prompt = serde_json::json!({
+                                "prompt": prompt,
+                                "system": task.get("system_prompt").and_then(|v| v.as_str()).unwrap_or(""),
+                                "max_tokens": task.get("max_tokens").and_then(|v| v.as_u64()).unwrap_or(512),
+                            }).to_string();
                             let use_3b = current_task == 5;
                             LLM_BUSY.store(true, Ordering::SeqCst);
                             let ws_for_async = ws_clone.clone();
                             wasm_bindgen_futures::spawn_local(async move {
-                                qwen_coder::run_coder_inference(prompt, ws_for_async, use_3b, task_id).await;
+                                qwen_coder::run_coder_inference(coder_prompt, ws_for_async, use_3b, task_id).await;
                                 LLM_BUSY.store(false, Ordering::SeqCst);
                             });
                         }
                     }
                 }
                 } // current_task == 4 || 5
-            } else if msg.contains("ai_task") {
-                console_log!("Hub task vastaanotettu, ajetaan GPU:lla...");
-                let ws_for_async = ws_clone.clone();
-                let diff = if msg.contains(r#""difficulty":1024"#) { 1024 } else { 512 };
-                
-                // Suoritetaan inference asynkronisesti erillisessä taaskissa välttääksemme UI-jäätymisen kokonaan
-                wasm_bindgen_futures::spawn_local(async move {
-                    let result = run_ai_tensor_inference(diff).await;
-                    let reply = format!("{{\"type\":\"result\", \"status\":\"success\", \"data\":\"{}\"}}", result);
-                    let _ = ws_for_async.borrow().send_with_str(&reply);
-                });
             } else if msg.contains("stats") {
                 // Sivuutetaan statsit täällä, UI hallitsee ne aivan itse HTML:n puolella
             }

network-poc/node/src/phi3.rs

@@ -1,36 +0,0 @@
-use candle_core::{Device, Tensor, DType};
-use candle_nn::VarBuilder;
-use candle_transformers::models::phi3::{Config as Phi3Config, Model as Phi3Model};
-use wasm_bindgen::JsCast;
-use std::cell::RefCell;
-use std::rc::Rc;
-use web_sys::WebSocket;
-
-use crate::storage;
-
-macro_rules! console_log {
-    ($($t:tt)*) => (web_sys::console::log_1(&format_args!($($t)*).to_string().into()))
-}
-
-const MODEL_URL: &str = "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/model.safetensors.index.json";
-const TOKENIZER_URL: &str = "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/tokenizer.json";
-
-// Phi-3 Mini on iso (7.6 GB) — käytetään kvantisoidumpaa versiota myöhemmin
-// Tällä hetkellä: placeholder joka raportoi koon ja jättää inferenssin väliin
-pub async fn run_phi3_inference(prompt: String, ws: Rc<RefCell<WebSocket>>) {
-    console_log!("[Phi-3] Phi-3 Mini 3.8B on liian suuri selaimessa ajettavaksi (~7.6 GB).");
-    console_log!("[Phi-3] Käytä SmolLM 135M tai Qwen2.5 0.5B selaininferenssiin.");
-    console_log!("[Phi-3] Phi-3 tuetaan native-node:lla (Docker + GPU).");
-
-    let done = serde_json::json!({
-        "type": "llm_done",
-        "prompt": prompt,
-        "model": "Phi-3-Mini (ei tuettu selaimessa)",
-        "response": "Phi-3 Mini 3.8B on liian suuri selaimessa ajettavaksi. Käytä SmolLM 135M tai Qwen2.5 0.5B.",
-        "tokens_generated": 0,
-        "duration_ms": 0,
-        "tokens_per_sec": 0,
-        "load_time_ms": 0,
-    });
-    let _ = ws.borrow().send_with_str(&done.to_string());
-}

network-poc/node/src/smollm.rs

@@ -1,232 +0,0 @@
-use candle_core::{Device, Tensor, DType};
-use candle_nn::VarBuilder;
-use candle_transformers::models::llama::{Llama, LlamaConfig, LlamaEosToks, Cache};
-// LogitsProcessor poistettu — käytetään greedy samplingia (argmax) Wasm-yhteensopivuuden vuoksi
-use wasm_bindgen::JsCast;
-use std::cell::RefCell;
-use std::rc::Rc;
-use web_sys::WebSocket;
-
-use crate::storage;
-
-macro_rules! console_log {
-    ($($t:tt)*) => (web_sys::console::log_1(&format_args!($($t)*).to_string().into()))
-}
-
-const MODEL_URL: &str = "https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct/resolve/main/model.safetensors";
-const TOKENIZER_URL: &str = "https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct/resolve/main/tokenizer.json";
-
-/// Lataa tiedosto HuggingFacesta streaming-latauksella (progress-ilmoitukset) ja tallentaa IndexedDB:hen
-async fn ensure_cached(key: &str, url: &str, ws: &Rc<RefCell<WebSocket>>) -> Result<Vec<u8>, String> {
-    if let Ok(Some(bytes)) = storage::load_from_idb(key).await {
-        console_log!("[SmolLM] {} löytyi välimuistista ({} MB)", key, bytes.len() / 1024 / 1024);
-        send_progress(ws, key, 100, bytes.len(), bytes.len());
-        return Ok(bytes);
-    }
-
-    console_log!("[SmolLM] Ladataan {}...", key);
-    send_progress(ws, key, 0, 0, 0);
-
-    // Fetch API:lla saadaan Content-Length ja streaming-luku
-    let resp = crate::worker_fetch(url).await?;
-
-    if !resp.ok() {
-        return Err(format!("HTTP {}", resp.status()));
-    }
-
-    // Kokonaiskoko Content-Length-headerista
-    let total_size: usize = resp.headers()
-        .get("content-length").ok().flatten()
-        .and_then(|s| s.parse().ok())
-        .unwrap_or(0);
-
-    let body = resp.body().ok_or("Ei bodyä")?;
-    let reader = body.get_reader();
-    let reader: web_sys::ReadableStreamDefaultReader = reader.dyn_into().map_err(|_| "Ei ReadableStreamDefaultReader".to_string())?;
-
-    let mut data: Vec<u8> = Vec::with_capacity(total_size);
-    let mut last_pct: u32 = 0;
-
-    loop {
-        let chunk = wasm_bindgen_futures::JsFuture::from(reader.read())
-            .await.map_err(|e| format!("Luku epäonnistui: {:?}", e))?;
-
-        let done = js_sys::Reflect::get(&chunk, &"done".into())
-            .map_err(|_| "done-kenttä puuttuu".to_string())?
-            .as_bool().unwrap_or(true);
-
-        if done { break; }
-
-        let value = js_sys::Reflect::get(&chunk, &"value".into())
-            .map_err(|_| "value-kenttä puuttuu".to_string())?;
-        let array = js_sys::Uint8Array::new(&value);
-        let mut buf = vec![0u8; array.length() as usize];
-        array.copy_to(&mut buf);
-        data.extend_from_slice(&buf);
-
-        // Progress-päivitys (joka 5%)
-        if total_size > 0 {
-            let pct = ((data.len() as f64 / total_size as f64) * 100.0) as u32;
-            if pct >= last_pct + 5 || pct == 100 {
-                last_pct = pct;
-                console_log!("[SmolLM] {} lataus: {}% ({}/{} MB)", key, pct, data.len() / 1024 / 1024, total_size / 1024 / 1024);
-                send_progress(ws, key, pct, data.len(), total_size);
-            }
-        }
-    }
-
-    console_log!("[SmolLM] Tallennetaan {} ({} MB) IndexedDB:hen...", key, data.len() / 1024 / 1024);
-    let _ = storage::save_to_idb(key, &data).await;
-    console_log!("[SmolLM] {} tallennettu!", key);
-    send_progress(ws, key, 100, data.len(), data.len());
-
-    Ok(data)
-}
-
-fn send_progress(ws: &Rc<RefCell<WebSocket>>, file: &str, pct: u32, loaded: usize, total: usize) {
-    let msg = serde_json::json!({
-        "type": "download_progress",
-        "file": file,
-        "pct": pct,
-        "loaded_mb": loaded / 1024 / 1024,
-        "total_mb": total / 1024 / 1024,
-    });
-    let _ = ws.borrow().send_with_str(&msg.to_string());
-}
-
-/// Lataa malli ja tokenizer, suorita inferenssi ja streamaa tokenit hubille
-pub async fn run_smollm_inference(prompt: String, ws: Rc<RefCell<WebSocket>>) {
-    // performance via crate::perf_now()
-
-    // 1. Lataa tokenizer
-    let tok_bytes = match ensure_cached("smollm-tokenizer.json", TOKENIZER_URL, &ws).await {
-        Ok(b) => b,
-        Err(e) => { console_log!("[SmolLM] Tokenizer-virhe: {}", e); return; }
-    };
-
-    let tokenizer = match tokenizers::Tokenizer::from_bytes(&tok_bytes) {
-        Ok(t) => t,
-        Err(e) => { console_log!("[SmolLM] Tokenizer-parsinta epäonnistui: {}", e); return; }
-    };
-
-    // 2. Lataa mallin painot
-    let model_bytes = match ensure_cached("smollm-model.safetensors", MODEL_URL, &ws).await {
-        Ok(b) => b,
-        Err(e) => { console_log!("[SmolLM] Malli-virhe: {}", e); return; }
-    };
-
-    // Burn 0.14 wgpu ei yhteensopiva nykyisten selainten kanssa (maxInterStageShaderComponents)
-    // Burn 0.21-pre.2 cubecl-runtime ei käänny Wasmille (println! puuttuu)
-    // → NdArray kunnes Burn 0.21 stable + Wasm-tuki
-    console_log!("[SmolLM] Burn NdArray (CPU) inferenssi...");
-    run_burn_inference::<burn::backend::NdArray>(prompt, model_bytes, tokenizer, ws).await;
-}
-
-async fn run_burn_inference<B: burn::tensor::backend::Backend>(
-    prompt: String,
-    model_bytes: Vec<u8>,
-    tokenizer: tokenizers::Tokenizer,
-    ws: Rc<RefCell<WebSocket>>,
-) {
-    let start_load = crate::perf_now();
-
-    let device = Default::default();
-    let config = crate::burn_smollm::config::SmolLMConfig::default();
-    
-    console_log!("[SmolLM] Injektoidaan Safetensors -> Burn Params...");
-    let model = match crate::burn_smollm::loader::load_safetensors_to_model::<B>(&model_bytes, &config, &device) {
-        Ok(m) => m,
-        Err(e) => { console_log!("[SmolLM] Lataus epäonnistui: {}", e); return; }
-    };
-
-    let load_time = crate::perf_now() - start_load;
-    console_log!("[SmolLM] Burn-malli ladattu ({:.0}ms). Generoidaan...", load_time);
-
-    let formatted_prompt = format!("<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n", prompt);
-    let encoding = match tokenizer.encode(formatted_prompt.as_str(), true) {
-        Ok(e) => e,
-        Err(e) => { console_log!("[SmolLM] Tokenisointivirhe: {}", e); return; }
-    };
-
-    let mut input_ids: Vec<u32> = encoding.get_ids().to_vec();
-    let input_len = input_ids.len();
-    console_log!("[SmolLM] Syöte: {} tokenia", input_len);
-
-    let start_gen = crate::perf_now();
-    let max_new_tokens = 32;
-    let mut generated_text = String::new();
-    let mut tokens_generated: usize = 0;
-    
-    // KV-välimuistin taulukko kerroksittain
-    let mut caches: Vec<Option<crate::burn_smollm::attention::KVCache<B>>> = vec![None; config.num_hidden_layers];
-    let mut current_offset = 0;
-
-    // Prefill: yksitellen, vältetään future token leakage koska ei causal maskia
-    let input_ids_i32: Vec<i32> = input_ids.iter().map(|&x| x as i32).collect();
-    let mut last_logits = None;
-    
-    for &id in &input_ids_i32 {
-        let input_tensor = burn::tensor::Tensor::<B, 1, burn::tensor::Int>::from_data(
-            burn::tensor::TensorData::from([id]),
-            &device
-        ).unsqueeze::<2>(); // [1, 1]
-        
-        last_logits = Some(model.forward(input_tensor, current_offset, &mut caches));
-        current_offset += 1;
-    }
-
-    let mut logits = last_logits.unwrap();
-
-    // Argmax sämpläys
-    let next_token_tensor = logits.clone().argmax(2);
-    let mut next_token: u32 = next_token_tensor.into_scalar().to_string().parse().unwrap_or(2); // Yksinkertainen cast koska int scalar
-
-    if next_token != 2 {
-        if let Ok(text) = tokenizer.decode(&[next_token], true) {
-            generated_text.push_str(&text);
-            let chunk = serde_json::json!({ "type": "llm_chunk", "token": text, "prompt": prompt, "model": "SmolLM-135M (WebGPU)" });
-            let _ = ws.borrow().send_with_str(&chunk.to_string());
-        }
-        tokens_generated += 1;
-    }
-
-    // Autoregressiivinen luuppi
-    for _ in 1..max_new_tokens {
-        if next_token == 2 { break; }
-        
-        let mut input_tensor = burn::tensor::Tensor::<B, 1, burn::tensor::Int>::from_data(
-            burn::tensor::TensorData::from([next_token as i32]),
-            &device
-        ).unsqueeze::<2>();
-        
-        logits = model.forward(input_tensor, current_offset, &mut caches);
-        current_offset += 1;
-        
-        let next_token_tensor = logits.argmax(2);
-        next_token = next_token_tensor.into_scalar().to_string().parse().unwrap_or(2);
-
-        if next_token == 2 { break; }
-
-        if let Ok(text) = tokenizer.decode(&[next_token], true) {
-            generated_text.push_str(&text);
-            let chunk = serde_json::json!({ "type": "llm_chunk", "token": text, "prompt": prompt, "model": "SmolLM-135M (WebGPU)" });
-            let _ = ws.borrow().send_with_str(&chunk.to_string());
-        }
-        tokens_generated += 1;
-    }
-
-    let gen_time = crate::perf_now() - start_gen;
-    let tokens_per_sec = if gen_time > 0.0 { (tokens_generated as f64 / gen_time) * 1000.0 } else { 0.0 };
-
-    let done = serde_json::json!({
-        "type": "llm_done",
-        "prompt": prompt,
-        "model": "SmolLM-135M-Instruct (WebGPU)",
-        "response": generated_text,
-        "tokens_generated": tokens_generated,
-        "duration_ms": (gen_time * 100.0).round() / 100.0,
-        "tokens_per_sec": (tokens_per_sec * 10.0).round() / 10.0,
-        "load_time_ms": (load_time * 100.0).round() / 100.0,
-    });
-    let _ = ws.borrow().send_with_str(&done.to_string());
-}

projektit/projekti1.md

@@ -0,0 +1,83 @@
+---
+
+title: UWB-paikannus sisätiloihin (2024)
+tags: project
+slideOptions:
+  text-align: left,
+  transition: slide,
+  theme: white,
+  hideAddressBar: true,
+  touch: true,
+  slideNumber: true,
+  controls: true,
+  controlsLayout: 'bottom-right'
+  spotlight:
+    enabled: true 
+
+---
+
+# UWB-paikannus sisätiloihin (2026)
+
+---
+
+![](https://gitlab.dclabra.fi/wiki/uploads/upload_dbe5eca9539b590febdb95942eaa16a7.jpg)
+
+
+----
+
+Ennakkotietona annettakoon pisteiden kohdistaminen yllä olevaan kuvatiedostoon:
+
+- y=0 yläreunan seinän sisäpinta, x=0 kassakoneiden keskilinja
+- y=5220 alareunan seinän sisäpinta, x=10406 oikealla seinän sisäpinta
+
+Paikassa n. 100, 2500 on yksi latausasema kärryille, siinä on sisäänkäynti kauppaan (turvaportit)
+Paikassa n. 900, 3600 on varsinainen latausasema joka on alakerrassa. Sieltä on liukuportaat vasemmalle kuvassa. Siellä ei ole kunnollista paikannusta joka aiheuttaa paikan hyppimistä. Nämä latausasemat eivät ole kiinnostavia tietoja.
+
+Eli yhden yksikön muutos koordinaateissa vastaa noin yhden senttimetrin muutosta "kartalla"?
+
+---
+
+# Dataformaatti 
+
+Datan formaatti on esitetty alla. Data itsessään on taltioituna csv-tiedostoihin. CSV-tiedostoja on paljon ja niissä on miljoonia rivejä, joten raakadatan käsittely voi olla raskasta. 
+
+
+![](https://gitlab.dclabra.fi/wiki/uploads/upload_b7a59c88b50fda806ec103ad2bbeeb6b.png)
+
+Varsinaisen ETL-/ELT-prosessin jälkeen data pitäisi olla esikäsitelty ja siivottu. Tämä prosessi on kuitenkin syytä tehdä heti alkuun, jotta myöhemmät dataan liittyvät operaatiot olisivat nopeampia.
+
+---
+
+Tehtävälistaa:
+
+ - Data platform
+     - MariaDB-tietokantakontti
+     - Jupyterlab-kontti ETL-prosessia ja data-analyysia varten
+     
+ - Visualisointi historiadatan perusteella
+     - Kärryjen liikkeet kaupan layoutissa
+    
+ - Outlierit pois datasta (x,y-pisteet, jotka ylittävät rajat)
+ - Läpimenoaika, "kuumat alueet" (eli missä on vietetty aikaa)
+ - Tilastoja
+     - Datan ajallinen täsmällisyys (näytevälin dt keskiarvo ja keskihajonta)
+     - Datan paikannustäsmällisyys (outlayreiden esiintymistaajuus, paikannuksen kohina eli x,y-koordinaatin heittelehtiminen luonnottomasti)
+     - Raportteja päivä-, viikko-, kuukausitason "liikennöinnistä"
+     - Läpimenoaikojen tilastointi (eri aukioloajan tunteina, eri päivinä, ruuhkahuippujen / hiljaisimpien aikojen löytäminen)
+     - Kuinka monta kassaa on käytössä eri aukioloajan tunteina, eri päivinä
+     - Kassajonojen kertyminen (kuinka monta asiakasta jonottaa kuinka monessa jonossa)
+ - (x,y)-koordinaattien skaalaus mittayksikköön [m]
+     - Keskimääräinen kärryjen kulkema matka
+     - Kärryjen nopeus [km/h], nopeuden liukuva keskiarvoistus (valon nopeudella / mach-nopeuksilla tapahtuvien liikkeiden karsiminen pois)
+     - Ostoskärryjen tasainen kierto, onko kärryjä, jotka ovat erittäin paljon/vähän käytössä
+ - Visualisointeja ja tilastoja
+     - kuumat alueet visualisoituna pohjakuvaan 
+         - eri aikaväleinä: 9.00-11.00; 11.00-13.00; 13.00-15.00; 15.00-17.00; 17.00-19.00; 19.00-21.00 
+         - eri viikonpäivinä
+     - Tilastot ja kuvaajat yllä mainitusta (esim histogrammit)
+ - Useamman datalähteen yhdistäminen
+     - Esim. avoimen säätietohistorian yhdistäminen eri tuntien kävijämääriin
+     - Jotain muuta, asiakkaalle mahdollisesti lisäarvoa tuottavaa - keksikää jotain jännää!
+ 
+---
+