Déploiement de l'agent Kibali sur Hugging Face

.dockerignore

@@ -1,10 +1,14 @@
-__pycache__
+# --- À EXCLURE (Trop lourd ou inutile) ---
+kibali_env/
+model_cache/
+frontend/
+__pycache__/
 *.pyc
 *.pt
 *.pkl
-model_cache
-data
-tools
-kibali_env
-frontend
 *.jar
+.git/
+.gitignore
+
+# --- À NE PAS EXCLURE ---
+# Assure-toi que "tools" n'est PAS écrit ici !

.gitattributes

@@ -1 +1,3 @@
 model_cache/** filter=lfs diff=lfs merge=lfs -text
+data/** filter=lfs diff=lfs merge=lfs -text
+*.pyc filter=lfs diff=lfs merge=lfs -text

CARTABON1/server.js

@@ -1,3 +1,4 @@
+// server.js (ou index.js)
 import express from "express";
 import mongoose from "mongoose";
 import dotenv from "dotenv";
@@ -5,9 +6,11 @@ import cors from "cors";
 import http from "http";
 import { Server } from "socket.io";
 import bcrypt from "bcrypt";
+import jwt from "jsonwebtoken";
 import multer from "multer";
 import path from "path";
 import fs from "fs";
+import crypto from "crypto";
 
 dotenv.config();
 
@@ -16,17 +19,17 @@ const server = http.createServer(app);
 const io = new Server(server, {
   cors: {
     origin: "*",
-    methods: ["GET", "POST"]
+    methods: ["GET", "POST", "PATCH"]
   }
 });
 
-app.use(express.json());
+app.use(express.json({ limit: "50mb" }));
 app.use(cors());
+app.use("/uploads", express.static(path.join(process.cwd(), "uploads")));
 
 if (!fs.existsSync("uploads")) {
   fs.mkdirSync("uploads");
 }
-app.use("/uploads", express.static(path.join(process.cwd(), "uploads")));
 
 const storage = multer.diskStorage({
   destination: (req, file, cb) => cb(null, "uploads/"),
@@ -39,28 +42,25 @@ const upload = multer({ storage });
 // =========================
 mongoose
   .connect(process.env.MONGO_URI)
-  .then(() => console.log("MongoDB connecté ✅"))
-  .catch(err => console.error("Erreur MongoDB ❌:", err));
+  .then(() => console.log("MongoDB connecté avec succès ✅"))
+  .catch(err => console.error("Erreur connexion MongoDB ❌:", err));
 
 // =========================
-// User Schema (ajout photo de profil)
+// Modèles
 // =========================
 const userSchema = new mongoose.Schema({
   name: { type: String, required: true },
-  email: { type: String, required: true, unique: true },
-  password: { type: String }, // null pour Google login
-  googleId: { type: String }, // pour OAuth Google
-  photoUrl: { type: String }, // photo Google
+  email: { type: String, required: true, unique: true, lowercase: true },
+  password: { type: String }, // hashé ou null si OTP uniquement
   latitude: { type: Number, default: 0 },
   longitude: { type: Number, default: 0 },
+  otp: { type: String }, // OTP temporaire
+  otpExpires: { type: Date }, // expiration OTP
   createdAt: { type: Date, default: Date.now }
 });
 
 const User = mongoose.model("User", userSchema);
 
-// =========================
-// Marker Schema (inchangé)
-// =========================
 const markerSchema = new mongoose.Schema({
   latitude: { type: Number, required: true },
   longitude: { type: Number, required: true },
@@ -69,109 +69,147 @@ const markerSchema = new mongoose.Schema({
   color: { type: String, default: "#ff0000" },
   photos: { type: [String], default: [] },
   videos: { type: [String], default: [] },
-  createdBy: { type: mongoose.Schema.Types.ObjectId, ref: "User", default: null },
+  createdBy: { type: mongoose.Schema.Types.ObjectId, ref: "User" },
   createdAt: { type: Date, default: Date.now }
 });
 const Marker = mongoose.model("Marker", markerSchema);
 
 // =========================
-// Routes
+// Helpers
 // =========================
-app.get("/", (req, res) => res.send("API Temps Réel fonctionne ✅"));
+const generateOTP = () => Math.floor(100000 + Math.random() * 900000).toString();
+
+const sendOTPByEmail = async (email, otp) => {
+  // À intégrer avec Nodemailer, SMTP, ou service comme Brevo/SendGrid
+  console.log(`📧 OTP pour ${email} : ${otp}`); // Simulation en console
+  // Exemple avec Nodemailer (à décommenter et configurer)
+  /*
+  const transporter = nodemailer.createTransport({
+    host: "smtp.gmail.com",
+    port: 587,
+    auth: { user: "tonemail@gmail.com", pass: "app-password" }
+  });
+  await transporter.sendMail({
+    from: "Kibali <no-reply@kibali.ga>",
+    to: email,
+    subject: "Votre code OTP Kibali",
+    text: `Votre code de connexion est : ${otp}\nValable 10 minutes.`
+  });
+  */
+};
 
-// Lister tous les utilisateurs (sans mot de passe)
-app.get("/users", async (req, res) => {
+// =========================
+// Routes Auth avec OTP
+// =========================
+app.post("/auth/request-otp", async (req, res) => {
   try {
-    const users = await User.find({}, "-__v -password");
-    res.json(users);
-  } catch (err) {
-    res.status(500).json({ error: err.message });
-  }
-});
+    const { email, name } = req.body;
+    if (!email) return res.status(400).json({ message: "Email requis" });
 
-// Inscription classique
-app.post("/register", async (req, res) => {
-  try {
-    const { name, email, password } = req.body;
-    const existingUser = await User.findOne({ email });
-    if (existingUser) return res.status(400).json({ message: "Email déjà utilisé" });
+    let user = await User.findOne({ email });
+    if (!user) {
+      user = await User.create({ email, name: name || email.split("@")[0], password: null });
+    }
 
-    const hashedPassword = await bcrypt.hash(password, 10);
-    const newUser = await User.create({ name, email, password: hashedPassword });
+    const otp = generateOTP();
+    user.otp = otp;
+    user.otpExpires = Date.now() + 10 * 60 * 1000; // 10 minutes
+    await user.save();
 
-    res.status(201).json({
-      user: { _id: newUser._id, name: newUser.name, email: newUser.email, photoUrl: newUser.photoUrl }
-    });
+    await sendOTPByEmail(email, otp);
+
+    res.json({ message: "OTP envoyé à votre email" });
   } catch (err) {
-    res.status(500).json({ message: err.message });
+    console.error(err);
+    res.status(500).json({ message: "Erreur serveur" });
   }
 });
 
-// Connexion classique
-app.post("/login", async (req, res) => {
+app.post("/auth/verify-otp", async (req, res) => {
   try {
-    const { email, password } = req.body;
+    const { email, otp } = req.body;
     const user = await User.findOne({ email });
-    if (!user || !user.password) return res.status(400).json({ message: "Email ou mot de passe incorrect" });
 
-    const isMatch = await bcrypt.compare(password, user.password);
-    if (!isMatch) return res.status(400).json({ message: "Email ou mot de passe incorrect" });
+    if (!user || !user.otp || user.otp !== otp || user.otpExpires < Date.now()) {
+      return res.status(400).json({ message: "OTP invalide ou expiré" });
+    }
+
+    // OTP valide → générer JWT
+    const token = jwt.sign({ userId: user._id }, process.env.JWT_SECRET, { expiresIn: "7d" });
+
+    // Nettoyer OTP
+    user.otp = undefined;
+    user.otpExpires = undefined;
+    await user.save();
 
     res.json({
-      user: { _id: user._id, name: user.name, email: user.email, photoUrl: user.photoUrl }
+      token,
+      user: {
+        _id: user._id,
+        name: user.name,
+        email: user.email,
+        latitude: user.latitude,
+        longitude: user.longitude
+      }
     });
   } catch (err) {
-    res.status(500).json({ message: err.message });
+    console.error(err);
+    res.status(500).json({ message: "Erreur serveur" });
   }
 });
 
-// Connexion Google (à appeler après OAuth réussi côté frontend)
-app.post("/auth/google", async (req, res) => {
-  try {
-    const { googleId, name, email, photoUrl } = req.body;
-
-    let user = await User.findOne({ googleId });
-    if (!user) {
-      user = await User.findOne({ email });
-      if (user) {
-        // Compte existant avec email → lier Google
-        user.googleId = googleId;
-        user.photoUrl = photoUrl;
-        user.name = name;
-        await user.save();
-      } else {
-        // Nouveau compte Google
-        user = await User.create({
-          googleId,
-          name,
-          email,
-          photoUrl,
-          password: null // pas de mot de passe local
-        });
-      }
-    } else {
-      // Mise à jour photo/nom si changé
-      user.name = name;
-      user.photoUrl = photoUrl;
-      await user.save();
-    }
+// =========================
+// Routes protégées (vérification JWT)
+// =========================
+const authenticateToken = (req, res, next) => {
+  const authHeader = req.headers["authorization"];
+  const token = authHeader && authHeader.split(" ")[1];
+  if (!token) return res.status(401).json({ message: "Accès refusé" });
+
+  jwt.verify(token, process.env.JWT_SECRET, (err, payload) => {
+    if (err) return res.status(403).json({ message: "Token invalide" });
+    req.userId = payload.userId;
+    next();
+  });
+};
 
-    res.json({
-      user: { _id: user._id, name: user.name, email: user.email, photoUrl: user.photoUrl }
-    });
+// Mise à jour position utilisateur
+app.post("/update-position", authenticateToken, async (req, res) => {
+  try {
+    const { latitude, longitude } = req.body;
+    const user = await User.findByIdAndUpdate(
+      req.userId,
+      { latitude, longitude },
+      { new: true }
+    );
+    res.json({ latitude: user.latitude, longitude: user.longitude });
   } catch (err) {
     res.status(500).json({ message: err.message });
   }
 });
 
-// ... (routes markers inchangées)
-app.post("/markers", upload.fields([{ name: "photos", maxCount: 10 }, { name: "videos", maxCount: 10 }]), async (req, res) => {
+// =========================
+// Routes Markers
+// =========================
+app.get("/markers", async (req, res) => {
+  try {
+    const markers = await Marker.find().populate("createdBy", "name email");
+    res.json(markers);
+  } catch (err) {
+    res.status(500).json({ error: err.message });
+  }
+});
+
+app.post("/markers", authenticateToken, upload.fields([
+  { name: "photos", maxCount: 10 },
+  { name: "videos", maxCount: 10 }
+]), async (req, res) => {
   try {
-    const { latitude, longitude, title, comment, color, userId } = req.body;
+    const { latitude, longitude, title, comment, color } = req.body;
     const photos = req.files.photos ? req.files.photos.map(f => `/uploads/${f.filename}`) : [];
     const videos = req.files.videos ? req.files.videos.map(f => `/uploads/${f.filename}`) : [];
 
-    const newMarker = await Marker.create({
+    const marker = await Marker.create({
       latitude: parseFloat(latitude),
       longitude: parseFloat(longitude),
       title,
@@ -179,34 +217,38 @@ app.post("/markers", upload.fields([{ name: "photos", maxCount: 10 }, { name: "v
       color,
       photos,
       videos,
-      createdBy: userId || null
+      createdBy: req.userId
     });
 
-    io.emit("newMarker", newMarker);
-    res.status(201).json(newMarker);
+    const populated = await marker.populate("createdBy", "name email");
+    io.emit("newMarker", populated);
+    res.status(201).json(populated);
   } catch (err) {
-    console.error("Erreur addMarker:", err);
+    console.error(err);
     res.status(500).json({ message: err.message });
   }
 });
 
-app.patch("/markers/:id", upload.fields([{ name: "photos", maxCount: 10 }, { name: "videos", maxCount: 10 }]), async (req, res) => {
+app.patch("/markers/:id", authenticateToken, upload.fields([
+  { name: "photos", maxCount: 10 },
+  { name: "videos", maxCount: 10 }
+]), async (req, res) => {
   try {
-    const { title, comment, color } = req.body;
     const marker = await Marker.findById(req.params.id);
     if (!marker) return res.status(404).json({ message: "Marqueur non trouvé" });
 
-    if (title !== undefined) marker.title = title;
-    if (comment !== undefined) marker.comment = comment;
-    if (color !== undefined) marker.color = color;
-    if (req.files.photos) marker.photos = [...marker.photos, ...req.files.photos.map(f => `/uploads/${f.filename}`)];
-    if (req.files.videos) marker.videos = [...marker.videos, ...req.files.videos.map(f => `/uploads/${f.filename}`)];
+    const updates = req.body;
+    if (updates.title !== undefined) marker.title = updates.title;
+    if (updates.comment !== undefined) marker.comment = updates.comment;
+    if (updates.color !== undefined) marker.color = updates.color;
+    if (req.files.photos) marker.photos.push(...req.files.photos.map(f => `/uploads/${f.filename}`));
+    if (req.files.videos) marker.videos.push(...req.files.videos.map(f => `/uploads/${f.filename}`));
 
     await marker.save();
-    io.emit("updatedMarker", marker);
-    res.json(marker);
+    const populated = await marker.populate("createdBy", "name email");
+    io.emit("updatedMarker", populated);
+    res.json(populated);
   } catch (err) {
-    console.error("Erreur editMarker:", err);
     res.status(500).json({ message: err.message });
   }
 });
@@ -215,43 +257,39 @@ app.patch("/markers/:id", upload.fields([{ name: "photos", maxCount: 10 }, { nam
 // Socket.IO
 // =========================
 io.on("connection", (socket) => {
-  console.log("Nouvel utilisateur connecté:", socket.id);
+  console.log("Client connecté:", socket.id);
 
-  const sendAllMarkers = async () => {
-    try {
-      const markers = await Marker.find();
-      socket.emit("allMarkers", markers);
-    } catch (err) {
-      console.error("Erreur envoi marqueurs:", err);
-    }
-  };
-  sendAllMarkers();
+  // Envoyer tous les marqueurs au nouveau client
+  Marker.find().populate("createdBy", "name email").then(markers => {
+    socket.emit("allMarkers", markers);
+  });
 
+  // Mise à jour position en temps réel
   socket.on("updatePosition", async ({ userId, latitude, longitude }) => {
-    if (typeof latitude !== "number" || typeof longitude !== "number") return;
-
     try {
       const user = await User.findByIdAndUpdate(userId, { latitude, longitude }, { new: true });
       if (user) {
         io.emit("positionsUpdate", {
           userId: user._id,
           name: user.name,
-          latitude: user.latitude,
-          longitude: user.longitude
+          latitude,
+          longitude
         });
       }
     } catch (err) {
-      console.error("Erreur updatePosition:", err);
+      console.error("Erreur position:", err);
     }
   });
 
   socket.on("disconnect", () => {
-    console.log("Utilisateur déconnecté:", socket.id);
+    console.log("Client déconnecté:", socket.id);
   });
 });
 
 // =========================
-// Start Server
+// Démarrage
 // =========================
-const PORT = process.env.PORT || 5000;
-server.listen(PORT, () => console.log(`Server démarré sur le port ${PORT} 🚀`));
+const PORT = process.env.PORT || 8000;
+server.listen(PORT, () => {
+  console.log(`🚀 Serveur Kibali démarré sur http://localhost:${PORT}`);
+});

Dockerfile

@@ -1,15 +1,36 @@
-FROM python:3.11-slim
+# --- STAGE 1 : Build du Frontend (Vite) ---
+FROM node:18-alpine AS build-frontend
+WORKDIR /app/frontend
+COPY kibali-ui/package*.json ./
+RUN npm install
+COPY kibali-ui/ ./
+RUN npm run build
 
+# --- STAGE 2 : Backend + Serveur Statique ---
+FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04
 WORKDIR /app
 
-# Copier seulement ce qui est nécessaire
+# Installation de Python
+RUN apt-get update && apt-get install -y \
+    python3-pip \
+    python3-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Installation de PyTorch
+RUN pip3 install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+
+# Installation des dépendances
 COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# On récupère le dossier 'dist' de Vite et on le renomme 'static'
+COPY --from=build-frontend /app/frontend/dist ./static
 
-COPY main.py app.py llm.py agent.py memory_faiss.py kibali_logo.svg ./
+# Copie du code Python
+COPY . .
 
-# Exposer le port
+ENV PYTHONUNBUFFERED=1
 EXPOSE 8000
 
-# Lancer FastAPI
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
+# Commande corrigée pour Ubuntu (python3)
+CMD ["python3", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

docker-compose.yml

@@ -5,6 +5,7 @@ services:
     build: .
     image: kibali-engine:latest
     container_name: kibali-engine
+    # Configuration GPU vitale pour l'IA
     deploy:
       resources:
         reservations:
@@ -13,19 +14,12 @@ services:
               count: 1
               capabilities: [gpu]
     volumes:
-      - /home/belikan/geoscan/agent_kibali:/app
+      # On ne monte que les dossiers de données persistantes
+      - ./model_cache:/app/model_cache
+      - ./data:/app/data
+      # On évite de monter tout le dossier /app pour ne pas écraser 
+      # le build de Vite fait pendant le Dockerfile
     ports:
       - "8000:8000"
-    command: python main.py
-
-  kibali-ui:
-    build: .
-    image: kibali-ui:latest
-    container_name: kibali-ui
-    ports:
-      - "8501:8501"
-    depends_on:
-      - kibali-engine
-    volumes:
-      - /home/belikan/geoscan/agent_kibali:/app
-    command: streamlit run app.py --server.port 8501 --server.address 0.0.0.0
+    # Lancement de l'API avec uvicorn
+    command: python3 -m uvicorn main:app --host 0.0.0.0 --port 8000

kibali-ui/package-lock.json

@@ -9,13 +9,22 @@
       "version": "0.0.0",
       "dependencies": {
         "axios": "^1.13.2",
+        "bcrypt": "^6.0.0",
+        "cors": "^2.8.5",
+        "dotenv": "^17.2.3",
+        "express": "^5.2.1",
         "firebase": "^12.7.0",
+        "jsonwebtoken": "^9.0.3",
         "leaflet": "^1.9.4",
         "lucide-react": "^0.562.0",
+        "mongoose": "^9.1.0",
+        "multer": "^2.0.2",
         "react": "^19.2.0",
         "react-dom": "^19.2.0",
         "react-leaflet": "^5.0.0",
-        "react-markdown": "^10.1.0"
+        "react-markdown": "^10.1.0",
+        "socket.io": "^4.8.3",
+        "socket.io-client": "^4.8.3"
       },
       "devDependencies": {
         "@eslint/js": "^9.39.1",
@@ -1654,6 +1663,15 @@
         "@jridgewell/sourcemap-codec": "^1.4.14"
       }
     },
+    "node_modules/@mongodb-js/saslprep": {
+      "version": "1.4.4",
+      "resolved": "https://registry.npmjs.org/@mongodb-js/saslprep/-/saslprep-1.4.4.tgz",
+      "integrity": "sha512-p7X/ytJDIdwUfFL/CLOhKgdfJe1Fa8uw9seJYvdOmnP9JBWGWHW69HkOixXS6Wy9yvGf1MbhcS6lVmrhy4jm2g==",
+      "license": "MIT",
+      "dependencies": {
+        "sparse-bitfield": "^3.0.3"
+      }
+    },
     "node_modules/@protobufjs/aspromise": {
       "version": "1.1.2",
       "resolved": "https://registry.npmjs.org/@protobufjs/aspromise/-/aspromise-1.1.2.tgz",
@@ -2044,6 +2062,12 @@
         "win32"
       ]
     },
+    "node_modules/@socket.io/component-emitter": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/@socket.io/component-emitter/-/component-emitter-3.1.2.tgz",
+      "integrity": "sha512-9BCxFwvbGg/RsZK9tjXd8s4UcwR0MWeFQ1XEKIQVVvAGJyINdrqKMcTRyLoK8Rse1GjzLV9cwjWV1olXRWEXVA==",
+      "license": "MIT"
+    },
     "node_modules/@types/babel__core": {
       "version": "7.20.5",
       "resolved": "https://registry.npmjs.org/@types/babel__core/-/babel__core-7.20.5.tgz",
@@ -2089,6 +2113,15 @@
         "@babel/types": "^7.28.2"
       }
     },
+    "node_modules/@types/cors": {
+      "version": "2.8.19",
+      "resolved": "https://registry.npmjs.org/@types/cors/-/cors-2.8.19.tgz",
+      "integrity": "sha512-mFNylyeyqN93lfe/9CSxOGREz8cpzAhH+E93xJ4xWQf62V8sQ/24reV2nyzUWM6H6Xji+GGHpkbLe7pVoUEskg==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/node": "*"
+      }
+    },
     "node_modules/@types/debug": {
       "version": "4.1.12",
       "resolved": "https://registry.npmjs.org/@types/debug/-/debug-4.1.12.tgz",
@@ -2178,6 +2211,21 @@
       "integrity": "sha512-ko/gIFJRv177XgZsZcBwnqJN5x/Gien8qNOn0D5bQU/zAzVf9Zt3BlcUiLqhV9y4ARk0GbT3tnUiPNgnTXzc/Q==",
       "license": "MIT"
     },
+    "node_modules/@types/webidl-conversions": {
+      "version": "7.0.3",
+      "resolved": "https://registry.npmjs.org/@types/webidl-conversions/-/webidl-conversions-7.0.3.tgz",
+      "integrity": "sha512-CiJJvcRtIgzadHCYXw7dqEnMNRjhGZlYK05Mj9OyktqV8uVT8fD2BFOB7S1uwBE3Kj2Z+4UyPmFw/Ixgw/LAlA==",
+      "license": "MIT"
+    },
+    "node_modules/@types/whatwg-url": {
+      "version": "13.0.0",
+      "resolved": "https://registry.npmjs.org/@types/whatwg-url/-/whatwg-url-13.0.0.tgz",
+      "integrity": "sha512-N8WXpbE6Wgri7KUSvrmQcqrMllKZ9uxkYWMt+mCSGwNc0Hsw9VQTW7ApqI4XNrx6/SaM2QQJCzMPDEXE058s+Q==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/webidl-conversions": "*"
+      }
+    },
     "node_modules/@ungap/structured-clone": {
       "version": "1.3.0",
       "resolved": "https://registry.npmjs.org/@ungap/structured-clone/-/structured-clone-1.3.0.tgz",
@@ -2205,6 +2253,44 @@
         "vite": "^4.2.0 || ^5.0.0 || ^6.0.0 || ^7.0.0"
       }
     },
+    "node_modules/accepts": {
+      "version": "2.0.0",
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+        "object-inspect": "^1.13.3"
+      },
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+      },
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+      }
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+      "resolved": "https://registry.npmjs.org/side-channel-weakmap/-/side-channel-weakmap-1.0.2.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "call-bound": "^1.0.2",
+        "es-errors": "^1.3.0",
+        "get-intrinsic": "^1.2.5",
+        "object-inspect": "^1.13.3",
+        "side-channel-map": "^1.0.1"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
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+      "resolved": "https://registry.npmjs.org/sift/-/sift-17.1.3.tgz",
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+      "license": "MIT"
+    },
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+      "version": "4.8.3",
+      "resolved": "https://registry.npmjs.org/socket.io/-/socket.io-4.8.3.tgz",
+      "integrity": "sha512-2Dd78bqzzjE6KPkD5fHZmDAKRNe3J15q+YHDrIsy9WEkqttc7GY+kT9OBLSMaPbQaEd0x1BjcmtMtXkfpc+T5A==",
+      "license": "MIT",
+      "dependencies": {
+        "accepts": "~1.3.4",
+        "base64id": "~2.0.0",
+        "cors": "~2.8.5",
+        "debug": "~4.4.1",
+        "engine.io": "~6.6.0",
+        "socket.io-adapter": "~2.5.2",
+        "socket.io-parser": "~4.2.4"
+      },
+      "engines": {
+        "node": ">=10.2.0"
+      }
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+      "resolved": "https://registry.npmjs.org/socket.io-adapter/-/socket.io-adapter-2.5.6.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "debug": "~4.4.1",
+        "ws": "~8.18.3"
+      }
+    },
+    "node_modules/socket.io-client": {
+      "version": "4.8.3",
+      "resolved": "https://registry.npmjs.org/socket.io-client/-/socket.io-client-4.8.3.tgz",
+      "integrity": "sha512-uP0bpjWrjQmUt5DTHq9RuoCBdFJF10cdX9X+a368j/Ft0wmaVgxlrjvK3kjvgCODOMMOz9lcaRzxmso0bTWZ/g==",
+      "license": "MIT",
+      "dependencies": {
+        "@socket.io/component-emitter": "~3.1.0",
+        "debug": "~4.4.1",
+        "engine.io-client": "~6.6.1",
+        "socket.io-parser": "~4.2.4"
+      },
+      "engines": {
+        "node": ">=10.0.0"
+      }
+    },
+    "node_modules/socket.io-parser": {
+      "version": "4.2.5",
+      "resolved": "https://registry.npmjs.org/socket.io-parser/-/socket.io-parser-4.2.5.tgz",
+      "integrity": "sha512-bPMmpy/5WWKHea5Y/jYAP6k74A+hvmRCQaJuJB6I/ML5JZq/KfNieUVo/3Mh7SAqn7TyFdIo6wqYHInG1MU1bQ==",
+      "license": "MIT",
+      "dependencies": {
+        "@socket.io/component-emitter": "~3.1.0",
+        "debug": "~4.4.1"
+      },
+      "engines": {
+        "node": ">=10.0.0"
+      }
+    },
+    "node_modules/socket.io/node_modules/accepts": {
+      "version": "1.3.8",
+      "resolved": "https://registry.npmjs.org/accepts/-/accepts-1.3.8.tgz",
+      "integrity": "sha512-PYAthTa2m2VKxuvSD3DPC/Gy+U+sOA1LAuT8mkmRuvw+NACSaeXEQ+NHcVF7rONl6qcaxV3Uuemwawk+7+SJLw==",
+      "license": "MIT",
+      "dependencies": {
+        "mime-types": "~2.1.34",
+        "negotiator": "0.6.3"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/socket.io/node_modules/negotiator": {
+      "version": "0.6.3",
+      "resolved": "https://registry.npmjs.org/negotiator/-/negotiator-0.6.3.tgz",
+      "integrity": "sha512-+EUsqGPLsM+j/zdChZjsnX51g4XrHFOIXwfnCVPGlQk/k5giakcKsuxCObBRu6DSm9opw/O6slWbJdghQM4bBg==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
     "node_modules/source-map-js": {
       "version": "1.2.1",
       "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
@@ -4885,6 +6138,41 @@
         "url": "https://github.com/sponsors/wooorm"
       }
     },
+    "node_modules/sparse-bitfield": {
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/sparse-bitfield/-/sparse-bitfield-3.0.3.tgz",
+      "integrity": "sha512-kvzhi7vqKTfkh0PZU+2D2PIllw2ymqJKujUcyPMd9Y75Nv4nPbGJZXNhxsgdQab2BmlDct1YnfQCguEvHr7VsQ==",
+      "license": "MIT",
+      "dependencies": {
+        "memory-pager": "^1.0.2"
+      }
+    },
+    "node_modules/statuses": {
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/statuses/-/statuses-2.0.2.tgz",
+      "integrity": "sha512-DvEy55V3DB7uknRo+4iOGT5fP1slR8wQohVdknigZPMpMstaKJQWhwiYBACJE3Ul2pTnATihhBYnRhZQHGBiRw==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.8"
+      }
+    },
+    "node_modules/streamsearch": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/streamsearch/-/streamsearch-1.1.0.tgz",
+      "integrity": "sha512-Mcc5wHehp9aXz1ax6bZUyY5afg9u2rv5cqQI3mRrYkGC8rW2hM02jWuwjtL++LS5qinSyhj2QfLyNsuc+VsExg==",
+      "engines": {
+        "node": ">=10.0.0"
+      }
+    },
+    "node_modules/string_decoder": {
+      "version": "1.3.0",
+      "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz",
+      "integrity": "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==",
+      "license": "MIT",
+      "dependencies": {
+        "safe-buffer": "~5.2.0"
+      }
+    },
     "node_modules/string-width": {
       "version": "4.2.3",
       "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
@@ -4993,6 +6281,27 @@
         "url": "https://github.com/sponsors/SuperchupuDev"
       }
     },
+    "node_modules/toidentifier": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/toidentifier/-/toidentifier-1.0.1.tgz",
+      "integrity": "sha512-o5sSPKEkg/DIQNmH43V0/uerLrpzVedkUh8tGNvaeXpfpuwjKenlSox/2O/BTlZUtEe+JG7s5YhEz608PlAHRA==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.6"
+      }
+    },
+    "node_modules/tr46": {
+      "version": "5.1.1",
+      "resolved": "https://registry.npmjs.org/tr46/-/tr46-5.1.1.tgz",
+      "integrity": "sha512-hdF5ZgjTqgAntKkklYw0R03MG2x/bSzTtkxmIRw/sTNV8YXsCJ1tfLAX23lhxhHJlEf3CRCOCGGWw3vI3GaSPw==",
+      "license": "MIT",
+      "dependencies": {
+        "punycode": "^2.3.1"
+      },
+      "engines": {
+        "node": ">=18"
+      }
+    },
     "node_modules/trim-lines": {
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/trim-lines/-/trim-lines-3.0.1.tgz",
@@ -5032,6 +6341,51 @@
         "node": ">= 0.8.0"
       }
     },
+    "node_modules/type-is": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/type-is/-/type-is-2.0.1.tgz",
+      "integrity": "sha512-OZs6gsjF4vMp32qrCbiVSkrFmXtG/AZhY3t0iAMrMBiAZyV9oALtXO8hsrHbMXF9x6L3grlFuwW2oAz7cav+Gw==",
+      "license": "MIT",
+      "dependencies": {
+        "content-type": "^1.0.5",
+        "media-typer": "^1.1.0",
+        "mime-types": "^3.0.0"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/type-is/node_modules/mime-db": {
+      "version": "1.54.0",
+      "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.54.0.tgz",
+      "integrity": "sha512-aU5EJuIN2WDemCcAp2vFBfp/m4EAhWJnUNSSw0ixs7/kXbd6Pg64EmwJkNdFhB8aWt1sH2CTXrLxo/iAGV3oPQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/type-is/node_modules/mime-types": {
+      "version": "3.0.2",
+      "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-3.0.2.tgz",
+      "integrity": "sha512-Lbgzdk0h4juoQ9fCKXW4by0UJqj+nOOrI9MJ1sSj4nI8aI2eo1qmvQEie4VD1glsS250n15LsWsYtCugiStS5A==",
+      "license": "MIT",
+      "dependencies": {
+        "mime-db": "^1.54.0"
+      },
+      "engines": {
+        "node": ">=18"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/express"
+      }
+    },
+    "node_modules/typedarray": {
+      "version": "0.0.6",
+      "resolved": "https://registry.npmjs.org/typedarray/-/typedarray-0.0.6.tgz",
+      "integrity": "sha512-/aCDEGatGvZ2BIk+HmLf4ifCJFwvKFNb9/JeZPMulfgFracn9QFcAf5GO8B/mweUjSoblS5In0cWhqpfs/5PQA==",
+      "license": "MIT"
+    },
     "node_modules/undici-types": {
       "version": "7.16.0",
       "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.16.0.tgz",
@@ -5125,6 +6479,15 @@
         "url": "https://opencollective.com/unified"
       }
     },
+    "node_modules/unpipe": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz",
+      "integrity": "sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.8"
+      }
+    },
     "node_modules/update-browserslist-db": {
       "version": "1.2.3",
       "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz",
@@ -5166,6 +6529,21 @@
         "punycode": "^2.1.0"
       }
     },
+    "node_modules/util-deprecate": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
+      "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==",
+      "license": "MIT"
+    },
+    "node_modules/vary": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz",
+      "integrity": "sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.8"
+      }
+    },
     "node_modules/vfile": {
       "version": "6.0.3",
       "resolved": "https://registry.npmjs.org/vfile/-/vfile-6.0.3.tgz",
@@ -5275,6 +6653,15 @@
       "integrity": "sha512-r4DIlprAGwJ7YM11VZp4R884m0Vmgr6EAKe3P+kO0PPj3Unqyvv59rczf6UiGcb9Z8QxZVcqKNwv/g0WNdWwsw==",
       "license": "Apache-2.0"
     },
+    "node_modules/webidl-conversions": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-7.0.0.tgz",
+      "integrity": "sha512-VwddBukDzu71offAQR975unBIGqfKZpM+8ZX6ySk8nYhVoo5CYaZyzt3YBvYtRtO+aoGlqxPg/B87NGVZ/fu6g==",
+      "license": "BSD-2-Clause",
+      "engines": {
+        "node": ">=12"
+      }
+    },
     "node_modules/websocket-driver": {
       "version": "0.7.4",
       "resolved": "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz",
@@ -5298,6 +6685,19 @@
         "node": ">=0.8.0"
       }
     },
+    "node_modules/whatwg-url": {
+      "version": "14.2.0",
+      "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-14.2.0.tgz",
+      "integrity": "sha512-De72GdQZzNTUBBChsXueQUnPKDkg/5A5zp7pFDuQAj5UFoENpiACU0wlCvzpAGnTkj++ihpKwKyYewn/XNUbKw==",
+      "license": "MIT",
+      "dependencies": {
+        "tr46": "^5.1.0",
+        "webidl-conversions": "^7.0.0"
+      },
+      "engines": {
+        "node": ">=18"
+      }
+    },
     "node_modules/which": {
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
@@ -5341,6 +6741,50 @@
         "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
       }
     },
+    "node_modules/wrappy": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+      "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==",
+      "license": "ISC"
+    },
+    "node_modules/ws": {
+      "version": "8.18.3",
+      "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz",
+      "integrity": "sha512-PEIGCY5tSlUt50cqyMXfCzX+oOPqN0vuGqWzbcJ2xvnkzkq46oOpz7dQaTDBdfICb4N14+GARUDw2XV2N4tvzg==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=10.0.0"
+      },
+      "peerDependencies": {
+        "bufferutil": "^4.0.1",
+        "utf-8-validate": ">=5.0.2"
+      },
+      "peerDependenciesMeta": {
+        "bufferutil": {
+          "optional": true
+        },
+        "utf-8-validate": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/xmlhttprequest-ssl": {
+      "version": "2.1.2",
+      "resolved": "https://registry.npmjs.org/xmlhttprequest-ssl/-/xmlhttprequest-ssl-2.1.2.tgz",
+      "integrity": "sha512-TEU+nJVUUnA4CYJFLvK5X9AOeH4KvDvhIfm0vV1GaQRtchnG0hgK5p8hw/xjv8cunWYCsiPCSDzObPyhEwq3KQ==",
+      "engines": {
+        "node": ">=0.4.0"
+      }
+    },
+    "node_modules/xtend": {
+      "version": "4.0.2",
+      "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
+      "integrity": "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.4"
+      }
+    },
     "node_modules/y18n": {
       "version": "5.0.8",
       "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",

kibali-ui/package.json

@@ -11,13 +11,22 @@
   },
   "dependencies": {
     "axios": "^1.13.2",
+    "bcrypt": "^6.0.0",
+    "cors": "^2.8.5",
+    "dotenv": "^17.2.3",
+    "express": "^5.2.1",
     "firebase": "^12.7.0",
+    "jsonwebtoken": "^9.0.3",
     "leaflet": "^1.9.4",
     "lucide-react": "^0.562.0",
+    "mongoose": "^9.1.0",
+    "multer": "^2.0.2",
     "react": "^19.2.0",
     "react-dom": "^19.2.0",
     "react-leaflet": "^5.0.0",
-    "react-markdown": "^10.1.0"
+    "react-markdown": "^10.1.0",
+    "socket.io": "^4.8.3",
+    "socket.io-client": "^4.8.3"
   },
   "devDependencies": {
     "@eslint/js": "^9.39.1",

kibali-ui/src/App.jsx

@@ -2,12 +2,11 @@ import React, { useState, useEffect, useRef } from 'react';
 import axios from 'axios';
 import {
   Send, FileText, Upload, Globe, MapPin,
-  Loader2, Trash2, Image as ImageIcon, Search, Brain, ChevronDown, ChevronUp, User, LogOut
+  Loader2, Trash2, Image as ImageIcon, Search, Brain, ChevronDown, ChevronUp, User,
+  Database, Clock, TrendingUp, AlertCircle, CheckCircle, Zap
 } from 'lucide-react';
-import { auth, googleProvider } from './firebase'; // Import Firebase
-import { signInWithPopup, signOut, onAuthStateChanged } from "firebase/auth";
 
-const API_BASE = "http://localhost:8000"; // Ton backend Node
+const API_BASE = "http://localhost:8000";
 const LOGO_PATH = "/kibali_logo.svg";
 
 function App() {
@@ -15,129 +14,223 @@ function App() {
   const [input, setInput] = useState("");
   const [loading, setLoading] = useState(false);
   const [uploading, setUploading] = useState(false);
-  const [status, setStatus] = useState(null);
-  const [showThinking, setShowThinking] = useState(true);
-  const [currentUser, setCurrentUser] = useState(null); // Utilisateur Firebase
+  const [status, setStatus] = useState({
+    doc_chunks: 0,
+    memory_entries: 0,
+    current_subject: null,
+    subject_message_count: 0
+  });
+  const [showThinking, setShowThinking] = useState({});
+  const [uploadProgress, setUploadProgress] = useState(null);
   const scrollRef = useRef(null);
+  const pollingInterval = useRef(null);
 
-  // Écouter les changements d'authentification Firebase
+  // Récupération du status backend au démarrage et périodiquement
   useEffect(() => {
-    const unsubscribe = onAuthStateChanged(auth, async (firebaseUser) => {
-      if (firebaseUser) {
-        // Utilisateur connecté via Google
-        try {
-          // Envoyer les infos à ton backend pour créer/lier le compte
-          const res = await axios.post(`${API_BASE}/auth/google`, {
-            googleId: firebaseUser.uid,
-            name: firebaseUser.displayName,
-            email: firebaseUser.email,
-            photoUrl: firebaseUser.photoURL
-          });
-
-          const user = res.data.user;
-          setCurrentUser(user);
-          localStorage.setItem("user", JSON.stringify(user)); // backup local
-        } catch (err) {
-          console.error("Erreur synchronisation avec backend:", err);
-          // Option : déconnexion si échec critique
-        }
-      } else {
-        setCurrentUser(null);
-        localStorage.removeItem("user");
+    fetchStatus();
+    pollingInterval.current = setInterval(fetchStatus, 10000); // Toutes les 10 secondes
+    
+    return () => {
+      if (pollingInterval.current) {
+        clearInterval(pollingInterval.current);
       }
-    });
-
-    return () => unsubscribe();
+    };
   }, []);
 
-  // Charger l'utilisateur depuis localStorage au démarrage (si refresh)
-  useEffect(() => {
-    const saved = localStorage.getItem("user");
-    if (saved && !currentUser) {
-      setCurrentUser(JSON.parse(saved));
-    }
-  }, []);
-
-  useEffect(() => {
-    scrollRef.current?.scrollIntoView({ behavior: "smooth" });
-  }, [messages, loading]);
-
-  const handleGoogleLogin = async () => {
+  const fetchStatus = async () => {
     try {
-      await signInWithPopup(auth, googleProvider);
-      // onAuthStateChanged gérera la suite
+      const response = await axios.get(`${API_BASE}/status`, { timeout: 5000 });
+      setStatus(response.data);
     } catch (error) {
-      alert("Échec de la connexion Google : " + error.message);
+      console.error("Erreur récupération status:", error);
     }
   };
 
-  const handleLogout = async () => {
-    try {
-      await signOut(auth);
-      setMessages([]);
-      // Firebase effacera l'état → onAuthStateChanged mettra currentUser à null
-    } catch (error) {
-      alert("Erreur déconnexion");
-    }
-  };
+  // Auto-scroll vers le bas
+  useEffect(() => {
+    scrollRef.current?.scrollIntoView({ behavior: "smooth" });
+  }, [messages, loading]);
 
+  // Envoi du message
   const handleSend = async () => {
-    if (!currentUser) {
-      alert("Veuillez vous connecter avec Google pour discuter.");
-      return;
-    }
     if (!input.trim() || loading) return;
 
-    const userMsg = { role: "user", content: input };
+    const userMsg = { role: "user", content: input.trim() };
     setMessages(prev => [...prev, userMsg]);
     setInput("");
     setLoading(true);
 
     try {
-      // À adapter selon ton vrai endpoint chat
       const response = await axios.post(`${API_BASE}/chat`, {
         messages: [...messages, userMsg],
-        latitude: currentUser.latitude || 0.4061,
-        longitude: currentUser.longitude || 9.4673,
+        latitude: 0.4061,
+        longitude: 9.4673,
         city: "Libreville",
         thinking_mode: true
+      }, {
+        timeout: 120000 // 120 secondes pour les requêtes complexes
       });
 
-      const aiResponse = response.data.response || "Réponse simulée (à connecter)";
+      const aiResponse = response.data.response || "Réponse reçue du serveur.";
       const aiImages = response.data.images || [];
+      const contextInfo = response.data.context_info || {};
 
       setMessages(prev => [...prev, {
         role: "assistant",
         content: aiResponse,
         images: aiImages,
-        tools_used: ["Recherche Web", "Analyse Géo", "Base Documentaire"]
+        context_info: contextInfo,
+        timestamp: new Date().toISOString()
       }]);
+
+      // Mise à jour du status après la réponse
+      fetchStatus();
     } catch (error) {
+      console.error("Erreur lors de l'appel au backend:", error);
+      let errorMsg = "Erreur : impossible de contacter le serveur IA.";
+      
+      if (error.code === 'ERR_NETWORK') {
+        errorMsg = "⚠️ Serveur injoignable. Vérifiez que votre backend est lancé sur http://localhost:8000";
+      } else if (error.code === 'ECONNABORTED') {
+        errorMsg = "⏱️ Timeout : la requête a pris trop de temps. Le modèle est peut-être surchargé.";
+      } else if (error.response?.status === 400) {
+        errorMsg = `❌ Erreur de requête : ${error.response.data.detail || 'Format invalide'}`;
+      } else if (error.response?.status === 500) {
+        errorMsg = "🔥 Erreur serveur interne. Vérifiez les logs du backend.";
+      } else if (error.response?.data?.detail) {
+        errorMsg = `Erreur serveur : ${error.response.data.detail}`;
+      }
+
       setMessages(prev => [...prev, {
         role: "assistant",
-        content: "Erreur : impossible de contacter le serveur IA."
+        content: errorMsg,
+        error: true,
+        timestamp: new Date().toISOString()
       }]);
     } finally {
       setLoading(false);
     }
   };
 
+  // Upload de fichiers PDF avec feedback de progression
   const handleFileUpload = async (e) => {
-    // ... (inchangé)
+    const files = Array.from(e.target.files);
+    if (!files.length) return;
+
+    setUploading(true);
+    setUploadProgress({ current: 0, total: files.length });
+
+    const formData = new FormData();
+    files.forEach(file => formData.append('files', file));
+
+    try {
+      const res = await axios.post(`${API_BASE}/upload`, formData, {
+        headers: { 'Content-Type': 'multipart/form-data' },
+        timeout: 180000, // 3 minutes pour les gros PDFs
+        onUploadProgress: (progressEvent) => {
+          const percentCompleted = Math.round((progressEvent.loaded * 100) / progressEvent.total);
+          setUploadProgress(prev => ({ ...prev, percent: percentCompleted }));
+        }
+      });
+
+      const chunksAdded = res.data.chunks_added || 0;
+      const filesProcessed = res.data.files_processed || 0;
+      const totalChunks = res.data.total_doc_chunks || 0;
+
+      // Mise à jour du status immédiate
+      setStatus(prev => ({ ...prev, doc_chunks: totalChunks }));
+
+      setMessages(prev => [...prev, {
+        role: "assistant",
+        content: `✅ **Import réussi !**\n\n📄 ${filesProcessed} document(s) traité(s)\n📊 ${chunksAdded} nouveaux segments ajoutés\n💾 Total en base : **${totalChunks} chunks**\n\nVous pouvez maintenant poser des questions sur ces documents !`,
+        success: true,
+        timestamp: new Date().toISOString()
+      }]);
+
+      // Refresh complet du status
+      fetchStatus();
+    } catch (err) {
+      console.error("Erreur upload:", err);
+      let errorMsg = "❌ Échec de l'import des documents.";
+      
+      if (err.code === 'ECONNABORTED') {
+        errorMsg += " Timeout : les fichiers sont peut-être trop volumineux.";
+      } else if (err.response?.data?.detail) {
+        errorMsg += ` Détails : ${err.response.data.detail}`;
+      }
+
+      setMessages(prev => [...prev, {
+        role: "assistant",
+        content: errorMsg,
+        error: true,
+        timestamp: new Date().toISOString()
+      }]);
+    } finally {
+      setUploading(false);
+      setUploadProgress(null);
+      e.target.value = '';
+    }
   };
 
+  // Réinitialisation du chat ET de la mémoire
+  const handleReset = async () => {
+    if (!window.confirm("Voulez-vous réinitialiser la conversation ET effacer la mémoire du modèle ?")) {
+      return;
+    }
+
+    try {
+      // Appeler l'endpoint de clear memory
+      await axios.post(`${API_BASE}/clear-memory`, {}, { timeout: 5000 });
+      setMessages([]);
+      fetchStatus();
+      
+      setMessages([{
+        role: "assistant",
+        content: "✅ Conversation et mémoire réinitialisées avec succès.",
+        success: true,
+        timestamp: new Date().toISOString()
+      }]);
+    } catch (error) {
+      console.error("Erreur lors de la réinitialisation:", error);
+      setMessages([{
+        role: "assistant",
+        content: "⚠️ Chat réinitialisé, mais impossible de contacter le serveur pour effacer la mémoire.",
+        error: true
+      }]);
+    }
+  };
+
+  // Toggle thinking pour chaque message
+  const toggleThinking = (msgIndex) => {
+    setShowThinking(prev => ({ ...prev, [msgIndex]: !prev[msgIndex] }));
+  };
+
+  // Formatage du temps relatif
+  const formatTimeAgo = (timestamp) => {
+    if (!timestamp) return "";
+    const seconds = Math.floor((new Date() - new Date(timestamp)) / 1000);
+    if (seconds < 60) return "à l'instant";
+    if (seconds < 3600) return `il y a ${Math.floor(seconds / 60)}m`;
+    if (seconds < 86400) return `il y a ${Math.floor(seconds / 3600)}h`;
+    return `il y a ${Math.floor(seconds / 86400)}j`;
+  };
+
+  // Styles centralisés
   const styles = {
     container: { display: 'flex', height: '100vh', backgroundColor: '#020617', color: '#f8fafc', fontFamily: 'system-ui, -apple-system, sans-serif' },
-    sidebar: { width: '320px', backgroundColor: '#0f172a', borderRight: '1px solid #1e293b', display: 'flex', flexDirection: 'column', zIndex: 10 },
+    sidebar: { width: '340px', backgroundColor: '#0f172a', borderRight: '1px solid #1e293b', display: 'flex', flexDirection: 'column', overflowY: 'auto' },
     main: { flex: 1, display: 'flex', flexDirection: 'column', position: 'relative', background: 'radial-gradient(circle at 50% 0%, #1e293b 0%, #020617 100%)' },
     messageArea: { flex: 1, overflowY: 'auto', padding: '2rem 10% 2rem 10%' },
     userBubble: { backgroundColor: '#059669', color: 'white', padding: '1rem 1.25rem', borderRadius: '1.25rem 1.25rem 0.25rem 1.25rem', boxShadow: '0 4px 15px rgba(5, 150, 105, 0.2)' },
     aiBubble: { backgroundColor: '#1e293b', border: '1px solid #334155', color: '#f1f5f9', padding: '1rem 1.25rem', borderRadius: '1.25rem 1.25rem 1.25rem 0.25rem', boxShadow: '0 10px 30px rgba(0,0,0,0.5)' },
+    successBubble: { backgroundColor: '#064e3b', border: '1px solid #059669', color: '#d1fae5' },
+    errorBubble: { backgroundColor: '#7f1d1d', border: '1px solid #991b1b', color: '#fca5a5' },
     inputContainer: { padding: '2rem', background: 'linear-gradient(to top, #020617 70%, transparent)' },
     inputWrapper: { display: 'flex', alignItems: 'center', backgroundColor: 'rgba(30, 41, 59, 0.7)', backdropFilter: 'blur(20px)', border: '1px solid #334155', borderRadius: '1.5rem', padding: '0.6rem 1.2rem', maxWidth: '900px', margin: '0 auto' },
     inputField: { flex: 1, background: 'transparent', border: 'none', color: 'white', padding: '0.8rem', outline: 'none', fontSize: '1rem' },
     sendBtn: { backgroundColor: '#10b981', color: 'white', border: 'none', borderRadius: '1rem', width: '45px', height: '45px', display: 'flex', alignItems: 'center', justifyContent: 'center', cursor: 'pointer', transition: 'all 0.2s' },
-    toolBadge: { display: 'flex', alignItems: 'center', gap: '6px', fontSize: '11px', backgroundColor: '#020617', padding: '6px 12px', borderRadius: '10px', border: '1px solid #1e293b', color: '#94a3b8' }
+    toolBadge: { display: 'flex', alignItems: 'center', gap: '6px', fontSize: '11px', backgroundColor: '#020617', padding: '6px 12px', borderRadius: '10px', border: '1px solid #1e293b', color: '#94a3b8' },
+    statCard: { backgroundColor: 'rgba(2, 6, 23, 0.5)', borderRadius: '1rem', padding: '1rem', border: '1px solid #1e293b', marginBottom: '0.8rem' }
   };
 
   return (
@@ -146,141 +239,322 @@ function App() {
       <aside style={styles.sidebar}>
         <div style={{ padding: '2rem', borderBottom: '1px solid #1e293b', display: 'flex', alignItems: 'center', gap: '1rem' }}>
           <div style={{ width: '45px', height: '45px', backgroundColor: '#1e293b', borderRadius: '12px', display: 'flex', alignItems: 'center', justifyContent: 'center', border: '1px solid #334155' }}>
-            <img src={LOGO_PATH} alt="K" style={{ width: '30px', height: '30px', objectFit: 'contain' }} onError={(e) => e.target.style.display='none'} />
+            <img src={LOGO_PATH} alt="K" style={{ width: '30px', height: '30px', objectFit: 'contain' }} onError={(e) => e.target.style.display = 'none'} />
           </div>
           <div>
-            <h1 style={{ fontSize: '1.3rem', fontWeight: '900', margin: 0, letterSpacing: '-0.5px' }}>Kibali <span style={{ color: '#10b981' }}>AI</span></h1>
+            <h1 style={{ fontSize: '1.3rem', fontWeight: '900', margin: 0, letterSpacing: '-0.5px' }}>
+              Kibali <span style={{ color: '#10b981' }}>AI</span>
+            </h1>
+            <p style={{ fontSize: '10px', color: '#475569', margin: 0, marginTop: '4px', fontWeight: '700' }}>
+              v1.0 • {status.status === 'ready' ? '🟢 ONLINE' : '🔴 OFFLINE'}
+            </p>
           </div>
         </div>
 
-        <div style={{ padding: '2rem', flex: 1, display: 'flex', flexDirection: 'column', gap: '2.5rem' }}>
-          {/* Profil ou bouton connexion */}
-          {!currentUser ? (
-            <div style={{ display: 'flex', flexDirection: 'column', alignItems: 'center', gap: '1rem' }}>
-              <p style={{ color: '#64748b', fontSize: '14px', textAlign: 'center' }}>
-                Connectez-vous avec Google pour utiliser Kibali
-              </p>
-              <button
-                onClick={handleGoogleLogin}
-                style={{
-                  backgroundColor: '#ffffff',
-                  color: '#000',
-                  padding: '12px 24px',
-                  borderRadius: '12px',
-                  border: 'none',
-                  cursor: 'pointer',
-                  fontWeight: '600',
-                  display: 'flex',
-                  alignItems: 'center',
-                  gap: '10px',
-                  boxShadow: '0 4px 15px rgba(0,0,0,0.3)'
-                }}
-              >
-                <img src="https://upload.wikimedia.org/wikipedia/commons/5/53/Google_%22G%22_Logo.svg" alt="G" width="20" />
-                Se connecter avec Google
-              </button>
+        <div style={{ padding: '2rem', flex: 1, display: 'flex', flexDirection: 'column', gap: '2rem' }}>
+          {/* Upload de documents */}
+          <div>
+            <h2 style={{ fontSize: '11px', color: '#475569', textTransform: 'uppercase', letterSpacing: '1.5px', marginBottom: '1rem', fontWeight: '800' }}>
+              📚 Documents
+            </h2>
+            <label style={{
+              display: 'flex',
+              flexDirection: 'column',
+              alignItems: 'center',
+              justifyContent: 'center',
+              height: '130px',
+              border: uploading ? '2px solid #10b981' : '2px dashed #1e293b',
+              borderRadius: '1.5rem',
+              cursor: uploading ? 'not-allowed' : 'pointer',
+              transition: 'all 0.3s',
+              backgroundColor: uploading ? 'rgba(16, 185, 129, 0.05)' : '#020617',
+              position: 'relative',
+              overflow: 'hidden'
+            }}>
+              {uploading ? (
+                <>
+                  <Loader2 className="animate-spin" color="#10b981" size={32} />
+                  <span style={{ fontSize: '12px', marginTop: '12px', color: '#10b981', fontWeight: '600' }}>
+                    {uploadProgress?.percent ? `${uploadProgress.percent}%` : 'Traitement...'}
+                  </span>
+                  {uploadProgress?.total && (
+                    <span style={{ fontSize: '10px', color: '#64748b', marginTop: '4px' }}>
+                      {uploadProgress.current}/{uploadProgress.total} fichiers
+                    </span>
+                  )}
+                </>
+              ) : (
+                <>
+                  <Upload color="#10b981" size={32} />
+                  <span style={{ fontSize: '12px', marginTop: '12px', color: '#64748b', fontWeight: '500' }}>
+                    Importer rapports PDF
+                  </span>
+                  <span style={{ fontSize: '10px', color: '#475569', marginTop: '4px' }}>
+                    Plusieurs fichiers acceptés
+                  </span>
+                </>
+              )}
+              <input type="file" hidden multiple accept=".pdf" onChange={handleFileUpload} disabled={uploading} />
+            </label>
+          </div>
+
+          {/* Statistiques enrichies */}
+          <div>
+            <h3 style={{ fontSize: '11px', color: '#475569', textTransform: 'uppercase', marginBottom: '1rem', fontWeight: '800', letterSpacing: '1.5px' }}>
+              📊 Statistiques
+            </h3>
+            
+            {/* Base de connaissances */}
+            <div style={styles.statCard}>
+              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+                <span style={{ color: '#64748b', fontSize: '12px', display: 'flex', alignItems: 'center', gap: '6px' }}>
+                  <Database size={14} color="#10b981" /> Base documentaire
+                </span>
+                <span style={{ color: '#10b981', fontWeight: 'bold', fontSize: '15px' }}>
+                  {status.doc_chunks || 0}
+                </span>
+              </div>
+              <div style={{ fontSize: '10px', color: '#475569', marginTop: '6px' }}>
+                segments vectorisés
+              </div>
             </div>
-          ) : (
-            <div style={{ display: 'flex', alignItems: 'center', gap: '12px', backgroundColor: '#020617', padding: '12px', borderRadius: '12px', border: '1px solid #1e293b' }}>
-              <img
-                src={currentUser.photoUrl || '/default-avatar.png'}
-                alt="Profil"
-                style={{ width: '40px', height: '40px', borderRadius: '50%', objectFit: 'cover' }}
-              />
-              <div style={{ flex: 1 }}>
-                <p style={{ margin: 0, fontWeight: '600', fontSize: '14px' }}>{currentUser.name}</p>
-                <p style={{ margin: 0, fontSize: '11px', color: '#64748b' }}>{currentUser.email}</p>
+
+            {/* Mémoire conversationnelle */}
+            <div style={styles.statCard}>
+              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+                <span style={{ color: '#64748b', fontSize: '12px', display: 'flex', alignItems: 'center', gap: '6px' }}>
+                  <Brain size={14} color="#3b82f6" /> Mémoire active
+                </span>
+                <span style={{ color: '#3b82f6', fontWeight: 'bold', fontSize: '15px' }}>
+                  {status.memory_entries || 0}
+                </span>
+              </div>
+              <div style={{ fontSize: '10px', color: '#475569', marginTop: '6px' }}>
+                échanges mémorisés
               </div>
-              <button onClick={handleLogout} style={{ background: 'none', border: 'none', cursor: 'pointer', color: '#ef4444' }}>
-                <LogOut size={18} />
-              </button>
             </div>
-          )}
 
-          {/* Le reste de ta sidebar (documents, stats, etc.) */}
-          <div>
-            <h2 style={{ fontSize: '11px', color: '#475569', textTransform: 'uppercase', letterSpacing: '1.5px', marginBottom: '1rem', fontWeight: '800' }}>Documents</h2>
-            <label style={{ display: 'flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', height: '140px', border: '2px dashed #1e293b', borderRadius: '1.5rem', cursor: 'pointer', transition: 'all 0.3s', backgroundColor: '#020617' }}>
-              {uploading ? <Loader2 className="animate-spin" color="#10b981" size={32} /> : <Upload color="#10b981" size={32} />}
-              <span style={{ fontSize: '12px', marginTop: '12px', color: '#64748b', fontWeight: '500' }}>Importer rapports PDF</span>
-              <input type="file" hidden multiple accept=".pdf" onChange={handleFileUpload} />
-            </label>
-          </div>
+            {/* Contexte conversationnel */}
+            {status.current_subject && (
+              <div style={styles.statCard}>
+                <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+                  <span style={{ color: '#64748b', fontSize: '12px', display: 'flex', alignItems: 'center', gap: '6px' }}>
+                    <TrendingUp size={14} color="#f59e0b" /> Sujet actuel
+                  </span>
+                  <span style={{ color: '#f59e0b', fontWeight: 'bold', fontSize: '15px' }}>
+                    {status.subject_message_count}
+                  </span>
+                </div>
+                <div style={{ fontSize: '10px', color: '#475569', marginTop: '6px', overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap' }}>
+                  {status.current_subject}
+                </div>
+              </div>
+            )}
 
-          {/* Statistiques */}
-          <div style={{ backgroundColor: 'rgba(2, 6, 23, 0.5)', borderRadius: '1.5rem', padding: '1.5rem', border: '1px solid #1e293b' }}>
-            <h3 style={{ fontSize: '11px', color: '#475569', textTransform: 'uppercase', marginBottom: '1.2rem', fontWeight: '800' }}>Statistiques</h3>
-            <div style={{ display: 'flex', justifyContent: 'space-between', fontSize: '13px', marginBottom: '1rem' }}>
-              <span style={{ color: '#64748b' }}>Connaissances</span>
-              <span style={{ color: '#10b981', fontWeight: 'bold' }}>{status?.doc_chunks || 0} segments</span>
+            {/* Localisation */}
+            <div style={styles.statCard}>
+              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+                <span style={{ color: '#64748b', fontSize: '12px' }}>Position</span>
+                <span style={{ color: '#f1f5f9', display: 'flex', alignItems: 'center', gap: '6px', fontWeight: '600', fontSize: '13px' }}>
+                  <MapPin size={14} color="#ef4444" /> Libreville
+                </span>
+              </div>
             </div>
-            <div style={{ display: 'flex', justifyContent: 'space-between', fontSize: '13px' }}>
-              <span style={{ color: '#64748b' }}>Position</span>
-              <span style={{ color: '#f1f5f9', display: 'flex', alignItems: 'center', gap: '6px', fontWeight: '600' }}>
-                <MapPin size={14} color="#ef4444"/> Libreville
-              </span>
+
+            {/* Santé du système */}
+            <div style={styles.statCard}>
+              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+                <span style={{ color: '#64748b', fontSize: '12px', display: 'flex', alignItems: 'center', gap: '6px' }}>
+                  <Zap size={14} color={status.torch_cuda_available ? '#10b981' : '#f59e0b'} /> GPU
+                </span>
+                <span style={{ color: status.torch_cuda_available ? '#10b981' : '#f59e0b', fontWeight: 'bold', fontSize: '11px' }}>
+                  {status.torch_cuda_available ? 'ACTIVÉ' : 'CPU ONLY'}
+                </span>
+              </div>
             </div>
           </div>
         </div>
 
-        <button onClick={() => setMessages([])} style={{ margin: '2rem', background: '#020617', border: '1px solid #1e293b', color: '#475569', padding: '14px', borderRadius: '14px', cursor: 'pointer', display: 'flex', alignItems: 'center', justifyContent: 'center', gap: '10px', fontWeight: '700', fontSize: '12px' }}>
-          <Trash2 size={16} /> RÉINITIALISER LE CHAT
+        <button
+          onClick={handleReset}
+          style={{
+            margin: '2rem',
+            background: '#020617',
+            border: '1px solid #1e293b',
+            color: '#475569',
+            padding: '14px',
+            borderRadius: '14px',
+            cursor: 'pointer',
+            display: 'flex',
+            alignItems: 'center',
+            justifyContent: 'center',
+            gap: '10px',
+            fontWeight: '700',
+            fontSize: '12px',
+            transition: 'all 0.2s'
+          }}
+          onMouseEnter={(e) => {
+            e.target.style.borderColor = '#ef4444';
+            e.target.style.color = '#ef4444';
+          }}
+          onMouseLeave={(e) => {
+            e.target.style.borderColor = '#1e293b';
+            e.target.style.color = '#475569';
+          }}
+        >
+          <Trash2 size={16} /> RÉINITIALISER TOUT
         </button>
       </aside>
 
-      {/* MAIN CONTENT - reste identique, avec photo utilisateur dans les bulles */}
+      {/* MAIN CONTENT */}
       <main style={styles.main}>
         <div style={styles.messageArea}>
-          {messages.length === 0 && !currentUser && (
+          {messages.length === 0 && (
             <div style={{ height: '80%', display: 'flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', textAlign: 'center' }}>
-              <div style={{ width: '100px', height: '100px', backgroundColor: '#0f172a', borderRadius: '30px', display: 'flex', alignItems: 'center', justifyContent: 'center', marginBottom: '2rem', border: '1px solid #334155', boxShadow: '0 20px 50px rgba(0,0,0,0.5)' }}>
-                <img src={LOGO_PATH} alt="Kibali" style={{ width: '60px' }} />
+              <div style={{
+                width: '100px',
+                height: '100px',
+                backgroundColor: '#0f172a',
+                borderRadius: '30px',
+                display: 'flex',
+                alignItems: 'center',
+                justifyContent: 'center',
+                marginBottom: '2rem',
+                border: '1px solid #334155',
+                boxShadow: '0 20px 50px rgba(0,0,0,0.5)'
+              }}>
+                <img src={LOGO_PATH} alt="Kibali" style={{ width: '60px' }} onError={(e) => e.target.style.display = 'none'} />
               </div>
-              <h2 style={{ fontSize: '2.5rem', fontWeight: '900', marginBottom: '0.8rem', letterSpacing: '-1px' }}>Connectez-vous pour commencer</h2>
+              <h2 style={{ fontSize: '2.5rem', fontWeight: '900', marginBottom: '0.8rem', letterSpacing: '-1px' }}>
+                Bienvenue sur Kibali AI
+              </h2>
               <p style={{ color: '#64748b', maxWidth: '450px', lineHeight: '1.7', fontSize: '1.1rem' }}>
-                Utilisez votre compte Google pour accéder à Kibali AI.
+                Assistant IA expert du Gabon avec mémoire contextuelle et analyse documentaire avancée.
               </p>
+              <div style={{ display: 'flex', gap: '1rem', marginTop: '2rem' }}>
+                <div style={{ textAlign: 'center' }}>
+                  <div style={{ fontSize: '2rem', fontWeight: 'bold', color: '#10b981' }}>{status.doc_chunks}</div>
+                  <div style={{ fontSize: '12px', color: '#64748b' }}>Chunks PDF</div>
+                </div>
+                <div style={{ textAlign: 'center' }}>
+                  <div style={{ fontSize: '2rem', fontWeight: 'bold', color: '#3b82f6' }}>{status.memory_entries}</div>
+                  <div style={{ fontSize: '12px', color: '#64748b' }}>Mémoires</div>
+                </div>
+              </div>
             </div>
           )}
 
           {messages.map((m, i) => (
             <div key={i} style={{ display: 'flex', justifyContent: m.role === 'user' ? 'flex-end' : 'flex-start', marginBottom: '3rem' }}>
               <div style={{ display: 'flex', gap: '1.2rem', maxWidth: '85%', flexDirection: m.role === 'user' ? 'row-reverse' : 'row' }}>
-                <div style={{ width: '45px', height: '45px', borderRadius: '14px', display: 'flex', alignItems: 'center', justifyContent: 'center', backgroundColor: m.role === 'user' ? '#059669' : '#1e293b', flexShrink: 0, border: '1px solid #334155', overflow: 'hidden' }}>
+                <div style={{
+                  width: '45px',
+                  height: '45px',
+                  borderRadius: '14px',
+                  display: 'flex',
+                  alignItems: 'center',
+                  justifyContent: 'center',
+                  backgroundColor: m.role === 'user' ? '#059669' : m.error ? '#7f1d1d' : m.success ? '#064e3b' : '#1e293b',
+                  flexShrink: 0,
+                  border: '1px solid #334155'
+                }}>
                   {m.role === 'user' ? (
-                    currentUser?.photoUrl ? (
-                      <img src={currentUser.photoUrl} alt="Vous" style={{ width: '100%', height: '100%', objectFit: 'cover' }} />
-                    ) : (
-                      <User size={24} color="white" />
-                    )
+                    <User size={24} color="white" />
+                  ) : m.error ? (
+                    <AlertCircle size={24} color="#ef4444" />
+                  ) : m.success ? (
+                    <CheckCircle size={24} color="#10b981" />
                   ) : (
-                    <img src={LOGO_PATH} style={{ width: '28px' }} alt="K" />
+                    <img src={LOGO_PATH} style={{ width: '28px' }} alt="K" onError={(e) => e.target.style.display = 'none'} />
                   )}
                 </div>
 
-                <div style={{ display: 'flex', flexDirection: 'column', gap: '0.8rem' }}>
-                  {m.role === 'assistant' && (
-                    <div style={{ backgroundColor: 'rgba(30, 41, 59, 0.3)', borderRadius: '12px', padding: '8px 12px', border: '1px solid #1e293b' }}>
-                      <button onClick={() => setShowThinking(!showThinking)} style={{ background: 'none', border: 'none', color: '#64748b', fontSize: '11px', display: 'flex', alignItems: 'center', gap: '6px', cursor: 'pointer', fontWeight: '700', textTransform: 'uppercase' }}>
-                        <Brain size={14} color="#10b981"/> Renseignements utilisés {showThinking ? <ChevronUp size={14}/> : <ChevronDown size={14}/>}
+                <div style={{ display: 'flex', flexDirection: 'column', gap: '0.8rem', width: '100%' }}>
+                  {/* Informations contextuelles */}
+                  {m.role === 'assistant' && !m.error && !m.success && m.context_info && (
+                    <div style={{ backgroundColor: 'rgba(30, 41, 59, 0.3)', borderRadius: '12px', padding: '10px 14px', border: '1px solid #1e293b' }}>
+                      <button
+                        onClick={() => toggleThinking(i)}
+                        style={{ background: 'none', border: 'none', color: '#64748b', fontSize: '11px', display: 'flex', alignItems: 'center', gap: '6px', cursor: 'pointer', fontWeight: '700', textTransform: 'uppercase', width: '100%', justifyContent: 'space-between' }}
+                      >
+                        <span style={{ display: 'flex', alignItems: 'center', gap: '6px' }}>
+                          <Brain size={14} color="#10b981" /> Intelligence contextuelle
+                        </span>
+                        {showThinking[i] ? <ChevronUp size={14} /> : <ChevronDown size={14} />}
                       </button>
-                      {showThinking && (
-                        <div style={{ display: 'flex', gap: '8px', marginTop: '10px', flexWrap: 'wrap' }}>
-                          <div style={styles.toolBadge}><Globe size={13} color="#3b82f6"/> Web Search</div>
-                          <div style={styles.toolBadge}><FileText size={13} color="#10b981"/> PDF Vault</div>
-                          <div style={styles.toolBadge}><MapPin size={13} color="#ef4444"/> Geo-Context</div>
-                        </div>
+                      
+                      {showThinking[i] && (
+                        <>
+                          <div style={{ display: 'flex', gap: '8px', marginTop: '12px', flexWrap: 'wrap' }}>
+                            <div style={styles.toolBadge}>
+                              <Globe size={13} color="#3b82f6" /> Web Search
+                              {m.context_info.web_results > 0 && <span style={{ color: '#10b981' }}>• {m.context_info.web_results}</span>}
+                            </div>
+                            <div style={styles.toolBadge}>
+                              <FileText size={13} color="#10b981" /> PDF Vault
+                              {m.context_info.rag_sources?.length > 0 && <span style={{ color: '#10b981' }}>• {m.context_info.rag_sources.length}</span>}
+                            </div>
+                            <div style={styles.toolBadge}>
+                              <Brain size={13} color="#f59e0b" /> Mémoire
+                              {m.context_info.memory_used > 0 && <span style={{ color: '#10b981' }}>• {m.context_info.memory_used}</span>}
+                            </div>
+                            <div style={styles.toolBadge}>
+                              <MapPin size={13} color="#ef4444" /> Geo-Context
+                            </div>
+                          </div>
+
+                          {/* Détails du contexte */}
+                          <div style={{ marginTop: '12px', fontSize: '11px', color: '#64748b', padding: '10px', backgroundColor: '#020617', borderRadius: '8px' }}>
+                            {m.context_info.subject_keywords?.length > 0 && (
+                              <div style={{ marginBottom: '8px' }}>
+                                <strong style={{ color: '#94a3b8' }}>Mots-clés du sujet :</strong> {m.context_info.subject_keywords.join(', ')}
+                              </div>
+                            )}
+                            {m.context_info.message_count > 0 && (
+                              <div style={{ marginBottom: '8px' }}>
+                                <strong style={{ color: '#94a3b8' }}>Messages sur ce sujet :</strong> {m.context_info.message_count}
+                              </div>
+                            )}
+                            {m.context_info.rag_sources?.length > 0 && (
+                              <div>
+                                <strong style={{ color: '#94a3b8' }}>Sources documentaires :</strong> {m.context_info.rag_sources.join(', ')}
+                              </div>
+                            )}
+                          </div>
+                        </>
                       )}
                     </div>
                   )}
-                  <div style={m.role === 'user' ? styles.userBubble : styles.aiBubble}>
-                    <p style={{ margin: 0, fontSize: '1.05rem', lineHeight: '1.6', fontWeight: '400' }}>{m.content}</p>
+
+                  {/* Bulle de message */}
+                  <div style={{
+                    ...(m.role === 'user' ? styles.userBubble : m.error ? styles.errorBubble : m.success ? styles.successBubble : styles.aiBubble),
+                    position: 'relative'
+                  }}>
+                    <p style={{ margin: 0, fontSize: '1.05rem', lineHeight: '1.6', fontWeight: '400', whiteSpace: 'pre-wrap' }}>
+                      {m.content}
+                    </p>
+                    {m.timestamp && (
+                      <div style={{ fontSize: '10px', color: 'rgba(255,255,255,0.4)', marginTop: '8px', display: 'flex', alignItems: 'center', gap: '4px' }}>
+                        <Clock size={10} /> {formatTimeAgo(m.timestamp)}
+                      </div>
+                    )}
                   </div>
+
+                  {/* Images */}
                   {m.images && m.images.length > 0 && (
-                    <div style={{ display: 'flex', gap: '12px', marginTop: '12px', overflowX: 'auto', paddingBottom: '10px' }}>
+                    <div style={{ display: 'flex', gap: '12px', marginTop: '8px', overflowX: 'auto', paddingBottom: '10px' }}>
                       {m.images.map((img, idx) => (
-                        <div key={idx} style={{ flexShrink: 0, position: 'relative', borderRadius: '1.2rem', overflow: 'hidden', border: '2px solid #334155' }}>
-                          <img src={img} alt="contexte" style={{ height: '180px', width: '280px', objectFit: 'cover' }} />
+                        <div key={idx} style={{ flexShrink: 0, position: 'relative', borderRadius: '1.2rem', overflow: 'hidden', border: '2px solid #334155', backgroundColor: '#0f172a' }}>
+                          <img 
+                            src={img} 
+                            alt={`Image ${idx + 1}`} 
+                            style={{ height: '180px', width: '280px', objectFit: 'cover' }}
+                            onError={(e) => {
+                              e.target.style.display = 'none';
+                              e.target.parentElement.innerHTML = '<div style="height:180px;width:280px;display:flex;align-items:center;justify-content:center;color:#64748b;font-size:12px;"><ImageIcon size={24} /> Image indisponible</div>';
+                            }}
+                          />
                         </div>
                       ))}
                     </div>
@@ -292,12 +566,29 @@ function App() {
 
           {loading && (
             <div style={{ display: 'flex', gap: '1.2rem', alignItems: 'center' }}>
-              <div style={{ width: '45px', height: '45px', borderRadius: '14px', backgroundColor: '#1e293b', display: 'flex', alignItems: 'center', justifyContent: 'center', border: '1px solid #334155' }}>
+              <div style={{
+                width: '45px',
+                height: '45px',
+                borderRadius: '14px',
+                backgroundColor: '#1e293b',
+                display: 'flex',
+                alignItems: 'center',
+                justifyContent: 'center',
+                border: '1px solid #334155'
+              }}>
                 <Loader2 className="animate-spin" color="#10b981" size={24} />
               </div>
-              <div style={{ color: '#64748b', fontSize: '15px', fontWeight: '600' }}>Kibali analyse la situation...</div>
+              <div style={{ display: 'flex', flexDirection: 'column', gap: '6px' }}>
+                <div style={{ color: '#f1f5f9', fontSize: '15px', fontWeight: '600' }}>
+                  Kibali analyse votre demande...
+                </div>
+                <div style={{ color: '#64748b', fontSize: '12px' }}>
+                  Recherche multi-sources • Traitement vectoriel • Génération contextuelle
+                </div>
+              </div>
             </div>
           )}
+
           <div ref={scrollRef} />
         </div>
 
@@ -306,23 +597,61 @@ function App() {
             <Search size={22} color="#475569" style={{ marginRight: '10px' }} />
             <input
               style={styles.inputField}
-              placeholder={currentUser ? "Posez votre question..." : "Connectez-vous pour discuter"}
+              placeholder="Posez votre question sur le Gabon, vos documents ou l'actualité..."
               value={input}
               onChange={(e) => setInput(e.target.value)}
-              onKeyDown={(e) => e.key === 'Enter' && handleSend()}
-              disabled={!currentUser}
+              onKeyDown={(e) => {
+                if (e.key === 'Enter' && !e.shiftKey) {
+                  e.preventDefault();
+                  handleSend();
+                }
+              }}
+              disabled={loading}
             />
             <button
-              style={{ ...styles.sendBtn, opacity: currentUser && input.trim() ? 1 : 0.4 }}
+              style={{ 
+                ...styles.sendBtn, 
+                opacity: input.trim() && !loading ? 1 : 0.4,
+                cursor: input.trim() && !loading ? 'pointer' : 'not-allowed',
+                transform: input.trim() && !loading ? 'scale(1)' : 'scale(0.95)'
+              }}
               onClick={handleSend}
-              disabled={!currentUser || loading || !input.trim()}
+              disabled={loading || !input.trim()}
+              onMouseEnter={(e) => {
+                if (input.trim() && !loading) {
+                  e.target.style.backgroundColor = '#059669';
+                  e.target.style.transform = 'scale(1.05)';
+                }
+              }}
+              onMouseLeave={(e) => {
+                e.target.style.backgroundColor = '#10b981';
+                e.target.style.transform = 'scale(1)';
+              }}
             >
-              <Send size={20} />
+              {loading ? <Loader2 className="animate-spin" size={20} /> : <Send size={20} />}
             </button>
           </div>
-          <p style={{ textAlign: 'center', color: '#1e293b', fontSize: '10px', marginTop: '1.2rem', fontWeight: '800', letterSpacing: '2px' }}>
-            GABONESE SOVEREIGN ARTIFICIAL INTELLIGENCE • SYSTEM KIBALI-1 powered by SETRAF-GABON
-          </p>
+
+          <div style={{ textAlign: 'center', marginTop: '1.5rem', display: 'flex', flexDirection: 'column', gap: '0.5rem', alignItems: 'center' }}>
+            <p style={{
+              color: '#334155',
+              fontSize: '10px',
+              fontWeight: '800',
+              letterSpacing: '2px',
+              margin: 0
+            }}>
+              GABONESE SOVEREIGN ARTIFICIAL INTELLIGENCE
+            </p>
+            <div style={{ display: 'flex', gap: '1rem', fontSize: '9px', color: '#1e293b', fontWeight: '700' }}>
+              <span>SYSTEM KIBALI-1</span>
+              <span>•</span>
+              <span>SETRAF-GABON</span>
+              <span>•</span>
+              <span style={{ color: status.torch_cuda_available ? '#10b981' : '#f59e0b' }}>
+                {status.torch_cuda_available ? '⚡ GPU ACCELERATED' : '⚙️ CPU MODE'}
+              </span>
+            </div>
+          </div>
         </div>
       </main>
     </div>

main.py

@@ -12,6 +12,9 @@ from threading import Thread
 import os
 from io import BytesIO
 import logging
+from datetime import datetime
+import json
+import hashlib
 
 # --- CONFIGURATION LOGGING ---
 logging.basicConfig(level=logging.INFO)
@@ -30,30 +33,13 @@ except ImportError:
         raise ImportError("Installe pypdf ou PyPDF2 : pip install pypdf")
 
 # --- OUTILS PERSONNALISÉS ---
-try:
-    from tools.web import web_search
-except ImportError:
-    logger.warning("Module tools.web non trouvé, utilisation d'une fonction stub")
-    def web_search(query):
-        return {"results": [], "images": []}
-
-try:
-    from tools.todo import execute_reflection_plan
-except ImportError:
-    logger.warning("Module tools.todo non trouvé")
-    def execute_reflection_plan(*args, **kwargs):
-        return None
-
-try:
-    from tools.geo import get_geo_context
-except ImportError:
-    logger.warning("Module tools.geo non trouvé")
-    def get_geo_context(*args, **kwargs):
-        return {}
+from tools.web import web_search
+from tools.todo import execute_reflection_plan
+from tools.geo import get_geo_context
 
 app = FastAPI(title="Kibali AI API", version="1.0")
 
-# --- SERVEUR STATIQUE (logo, etc.) ---
+# --- SERVEUR STATIQUE ---
 script_dir = os.path.dirname(os.path.abspath(__file__))
 static_dir = os.path.join(script_dir, "static")
 os.makedirs(static_dir, exist_ok=True)
@@ -68,58 +54,99 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# --- CHARGEMENT DES MODÈLES DEPUIS HUGGING FACE HUB ---
-MODEL_REPO = "BelikanM/kibali-final-merged"  # Ton modèle sur HF Hub
-
+# --- CHARGEMENT DES MODÈLES ---
+MODEL_PATH = "/home/belikan/geoscan/agent_kibali/model_cache"
 logger.info("Chargement du modèle d'embedding...")
-try:
-    embed_model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
-    logger.info("Modèle d'embedding chargé avec succès")
-except Exception as e:
-    logger.error(f"Erreur chargement embedding model : {e}")
-    raise
+embed_model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
+logger.info("Chargement du tokenizer et du modèle LLM...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, local_files_only=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16
+)
 
-logger.info(f"Chargement du tokenizer depuis {MODEL_REPO}...")
-try:
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO, trust_remote_code=True)
-    if tokenizer.pad_token is None:
-        tokenizer.pad_token = tokenizer.eos_token
-    logger.info("Tokenizer chargé avec succès")
-except Exception as e:
-    logger.error(f"Erreur chargement tokenizer : {e}")
-    raise
-
-logger.info(f"Chargement du modèle LLM depuis {MODEL_REPO}...")
-try:
-    bnb_config = BitsAndBytesConfig(
-        load_in_4bit=True,
-        bnb_4bit_use_double_quant=True,
-        bnb_4bit_quant_type="nf4",
-        bnb_4bit_compute_dtype=torch.float16
-    )
-
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_REPO,
-        quantization_config=bnb_config,
-        device_map="auto",
-        torch_dtype=torch.float16,
-        trust_remote_code=True,
-        low_cpu_mem_usage=True
-    )
-    logger.info(f"Modèle LLM chargé avec succès sur {model.device}")
-except Exception as e:
-    logger.error(f"Erreur chargement modèle LLM : {e}")
-    raise
-
-# --- BASES VECTORIELLES (séparées) ---
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    quantization_config=bnb_config,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+    low_cpu_mem_usage=True
+)
+logger.info(f"Modèle chargé sur {model.device}")
+
+# --- BASES VECTORIELLES GLOBALES ---
 dimension = 384
-# Index pour les documents PDF
 doc_index = faiss.IndexFlatL2(dimension)
 doc_chunks: List[str] = []
+doc_metadata: List[dict] = []  # Métadonnées des chunks (source, timestamp, etc.)
 
-# Index séparé pour la mémoire conversationnelle
 memory_index = faiss.IndexFlatL2(dimension)
 memory_texts: List[str] = []
+memory_metadata: List[dict] = []  # Métadonnées des mémoires (timestamp, sujet, score)
+
+# --- GESTION DU CONTEXTE CONVERSATIONNEL ---
+class ConversationContext:
+    def __init__(self):
+        self.current_subject = None
+        self.subject_embedding = None
+        self.subject_start_time = None
+        self.message_count = 0
+        self.subject_keywords = []
+        
+    def update_subject(self, message: str, embedding: np.ndarray):
+        """Détecte et met à jour le sujet actuel de la conversation"""
+        keywords = self._extract_keywords(message)
+        
+        # Détection de changement de sujet
+        if self.subject_embedding is not None:
+            similarity = np.dot(embedding.flatten(), self.subject_embedding.flatten())
+            if similarity < 0.6:  # Seuil de changement de sujet
+                logger.info(f"Changement de sujet détecté (similarité: {similarity:.2f})")
+                self._archive_current_subject()
+                self.current_subject = message
+                self.subject_embedding = embedding
+                self.subject_start_time = datetime.now()
+                self.message_count = 1
+                self.subject_keywords = keywords
+            else:
+                self.message_count += 1
+                self.subject_keywords.extend(keywords)
+                self.subject_keywords = list(set(self.subject_keywords))[:10]  # Top 10
+        else:
+            self.current_subject = message
+            self.subject_embedding = embedding
+            self.subject_start_time = datetime.now()
+            self.message_count = 1
+            self.subject_keywords = keywords
+    
+    def _extract_keywords(self, text: str) -> List[str]:
+        """Extrait les mots-clés importants du texte"""
+        stopwords = {'le', 'la', 'les', 'un', 'une', 'des', 'de', 'du', 'et', 'ou', 
+                     'est', 'sont', 'à', 'au', 'en', 'pour', 'dans', 'sur', 'avec'}
+        words = text.lower().split()
+        keywords = [w for w in words if len(w) > 3 and w not in stopwords]
+        return keywords[:5]
+    
+    def _archive_current_subject(self):
+        """Archive le sujet actuel avant de passer au suivant"""
+        if self.current_subject and memory_index.ntotal > 0:
+            # Créer un résumé du sujet archivé
+            summary = {
+                "subject": self.current_subject[:200],
+                "keywords": self.subject_keywords,
+                "message_count": self.message_count,
+                "duration": (datetime.now() - self.subject_start_time).seconds,
+                "archived_at": datetime.now().isoformat()
+            }
+            logger.info(f"Sujet archivé: {summary['keywords']}")
+
+conversation_ctx = ConversationContext()
 
 # --- MODÈLES PYDANTIC ---
 class Message(BaseModel):
@@ -136,10 +163,10 @@ class ChatRequest(BaseModel):
 class ChatResponse(BaseModel):
     response: str
     images: List[str] = []
+    context_info: Optional[dict] = None
 
 # --- UTILITAIRES ---
 def extract_text_from_pdf(pdf_bytes: bytes) -> str:
-    """Extrait le texte d'un PDF depuis des bytes"""
     text = ""
     try:
         pdf_file = BytesIO(pdf_bytes)
@@ -148,41 +175,105 @@ def extract_text_from_pdf(pdf_bytes: bytes) -> str:
             page_text = page.extract_text()
             if page_text:
                 text += page_text + "\n"
+        return text.strip()
     except Exception as e:
         logger.error(f"Erreur extraction PDF : {e}")
-    return text
+        return ""
 
 def chunk_text(text: str, chunk_size: int = 400, overlap: int = 50) -> List[str]:
-    """Découpe un texte en chunks avec overlap"""
+    if not text.strip():
+        return []
     words = text.split()
     chunks = []
     i = 0
     while i < len(words):
-        chunk = " ".join(words[i:i + chunk_size])
-        chunks.append(chunk)
+        chunk_words = words[i:i + chunk_size]
+        chunk = " ".join(chunk_words)
+        if chunk.strip():
+            chunks.append(chunk.strip())
         i += chunk_size - overlap
-        if i >= len(words) and len(chunks) > 0:
+        if i >= len(words) and len(chunk_words) < overlap:
             break
     return chunks
 
-# --- ROUTES ---
-@app.get("/")
-async def root():
-    """Route racine - information API"""
-    return {
-        "message": "Kibali AI API - Prêt",
-        "version": "1.0",
-        "endpoints": {
-            "status": "/status",
-            "chat": "/chat",
-            "upload": "/upload-pdfs",
-            "docs": "/docs"
-        }
+def add_to_memory_realtime(user_msg: str, ai_response: str, subject_keywords: List[str]):
+    """Ajoute une entrée mémoire en temps réel avec métadonnées enrichies"""
+    timestamp = datetime.now().isoformat()
+    
+    # Créer une entrée mémoire enrichie
+    memory_entry = f"""[{timestamp}]
+Sujet: {', '.join(subject_keywords)}
+Utilisateur: {user_msg}
+Kibali: {ai_response}"""
+    
+    # Métadonnées
+    metadata = {
+        "timestamp": timestamp,
+        "subject_keywords": subject_keywords,
+        "user_length": len(user_msg),
+        "ai_length": len(ai_response),
+        "hash": hashlib.md5(memory_entry.encode()).hexdigest()
     }
+    
+    # Éviter les doublons
+    if metadata["hash"] not in [m.get("hash") for m in memory_metadata]:
+        memory_texts.append(memory_entry)
+        memory_metadata.append(metadata)
+        
+        # Ajout vectoriel
+        mem_emb = embed_model.encode([memory_entry], normalize_embeddings=True).astype('float32')
+        memory_index.add(mem_emb)
+        
+        logger.info(f"Mémoire ajoutée en temps réel: {subject_keywords} (total: {len(memory_texts)})")
+        return True
+    return False
+
+def retrieve_adaptive_memory(query: str, k: int = 5) -> tuple:
+    """Récupère la mémoire de façon adaptative selon le contexte"""
+    if memory_index.ntotal == 0:
+        return [], []
+    
+    query_emb = embed_model.encode([query], normalize_embeddings=True).astype('float32')
+    
+    # Recherche de base
+    k_search = min(k * 2, memory_index.ntotal)  # Chercher plus pour filtrer ensuite
+    D, I = memory_index.search(query_emb, k=k_search)
+    
+    # Filtrage intelligent avec scoring
+    results = []
+    for dist, idx in zip(D[0], I[0]):
+        if 0 <= idx < len(memory_texts):
+            metadata = memory_metadata[idx] if idx < len(memory_metadata) else {}
+            
+            # Score de pertinence
+            recency_score = 1.0 / (1 + (datetime.now() - datetime.fromisoformat(metadata.get("timestamp", datetime.now().isoformat()))).seconds / 3600)
+            similarity_score = 1.0 / (1 + dist)
+            
+            # Bonus si les mots-clés du sujet actuel correspondent
+            keyword_bonus = 0
+            if conversation_ctx.subject_keywords:
+                text_lower = memory_texts[idx].lower()
+                keyword_bonus = sum(1 for kw in conversation_ctx.subject_keywords if kw in text_lower) * 0.1
+            
+            total_score = similarity_score * 0.6 + recency_score * 0.3 + keyword_bonus
+            
+            results.append({
+                "text": memory_texts[idx],
+                "score": total_score,
+                "metadata": metadata
+            })
+    
+    # Trier par score et prendre les top k
+    results = sorted(results, key=lambda x: x["score"], reverse=True)[:k]
+    
+    texts = [r["text"] for r in results]
+    scores = [r["score"] for r in results]
+    
+    return texts, scores
 
+# --- ROUTES ---
 @app.get("/status")
 async def status():
-    """Statut de l'API et des ressources chargées"""
     return {
         "status": "ready",
         "doc_chunks": len(doc_chunks),
@@ -190,188 +281,215 @@ async def status():
         "pdf_library": PDF_READER,
         "model_device": str(model.device),
         "torch_cuda_available": torch.cuda.is_available(),
-        "model_repo": MODEL_REPO
+        "current_subject": conversation_ctx.current_subject[:100] if conversation_ctx.current_subject else None,
+        "subject_message_count": conversation_ctx.message_count
     }
 
 @app.post("/chat", response_model=ChatResponse)
 async def chat(request: ChatRequest):
-    """Endpoint principal pour le chat avec Kibali"""
-    try:
-        user_message = request.messages[-1].content.strip()
-        if not user_message:
-            raise HTTPException(status_code=400, detail="Message vide")
-
-        geo = {
-            "latitude": request.latitude,
-            "longitude": request.longitude,
-            "city": request.city or "Libreville"
-        }
-
-        # 1. RAG Documents (PDF uploadés)
-        rag_context = ""
-        if doc_index.ntotal > 0 and len(doc_chunks) > 0:
-            try:
-                query_emb = embed_model.encode([user_message], normalize_embeddings=True).astype('float32')
-                D, I = doc_index.search(query_emb, k=5)
-                relevant_chunks = [doc_chunks[i] for i in I[0] if 0 <= i < len(doc_chunks)]
-                rag_context = "\n\n".join([f"Document : {chunk[:1000]}" for chunk in relevant_chunks])
-            except Exception as e:
-                logger.error(f"Erreur RAG documents : {e}")
-
-        # 2. Mémoire conversationnelle (historique du chat indexé vectoriellement)
-        memory_context = ""
-        if memory_index.ntotal > 0 and len(memory_texts) > 0:
-            try:
-                query_emb = embed_model.encode([user_message], normalize_embeddings=True).astype('float32')
-                D, I = memory_index.search(query_emb, k=5)
-                relevant_mem = [memory_texts[i] for i in I[0] if 0 <= i < len(memory_texts)]
-                memory_context = "\n\n".join(relevant_mem)
-            except Exception as e:
-                logger.error(f"Erreur RAG mémoire : {e}")
-
-        # 3. Recherche Web (pour éviter les hallucinations avec infos récentes)
-        web_context = ""
-        web_images = []
-        try:
-            search_results = web_search(user_message + " Gabon")
-            web_context = "\n".join([f"- {r['content'][:500]}" for r in search_results.get("results", [])[:6]])
-            web_images = search_results.get("images", [])[:4]
-        except Exception as e:
-            logger.error(f"Erreur recherche web : {e}")
+    user_message = request.messages[-1].content.strip()
+    if not user_message:
+        raise HTTPException(status_code=400, detail="Message vide")
+
+    geo = {
+        "latitude": request.latitude,
+        "longitude": request.longitude,
+        "city": request.city or "Libreville"
+    }
+    
+    # Mise à jour du contexte conversationnel en temps réel
+    user_emb = embed_model.encode([user_message], normalize_embeddings=True).astype('float32')
+    conversation_ctx.update_subject(user_message, user_emb)
+
+    # 1. RAG Documents PDF
+    rag_context = ""
+    rag_sources = []
+    if doc_index.ntotal > 0 and len(doc_chunks) > 0:
+        D, I = doc_index.search(user_emb, k=5)
+        relevant_chunks = []
+        for idx in I[0]:
+            if 0 <= idx < len(doc_chunks):
+                relevant_chunks.append(doc_chunks[idx][:1000])
+                if idx < len(doc_metadata):
+                    rag_sources.append(doc_metadata[idx].get("source", "PDF"))
+        if relevant_chunks:
+            rag_context = "\n\n".join([f"Document : {chunk}" for chunk in relevant_chunks])
+
+    # 2. Mémoire conversationnelle adaptative
+    memory_context = ""
+    memory_texts_filtered, memory_scores = retrieve_adaptive_memory(user_message, k=5)
+    if memory_texts_filtered:
+        memory_context = "\n\n".join([f"Mémoire (score: {score:.2f}): {text}" 
+                                      for text, score in zip(memory_texts_filtered, memory_scores)])
+        logger.info(f"Mémoire récupérée: {len(memory_texts_filtered)} entrées (scores: {[f'{s:.2f}' for s in memory_scores]})")
+
+    # 3. Réflexion stratégique
+    if request.thinking_mode:
+        execute_reflection_plan(
+            user_message, 
+            geo_info=geo,
+            messages=request.messages,
+            current_subject=conversation_ctx.current_subject,
+            subject_keywords=conversation_ctx.subject_keywords
+        )
+
+    # 4. Recherche Web enrichie
+    search_query = user_message
+    if conversation_ctx.subject_keywords:
+        search_query = f"{user_message} {' '.join(conversation_ctx.subject_keywords[:3])} Gabon"
+    
+    search_results = web_search(search_query)
+    web_context = "\n".join([f"- {r['content'][:500]}" for r in search_results.get("results", [])[:6]])
+    web_images = search_results.get("images", [])[:4]
 
-        # 4. Prompt final renforcé pour précision, cohérence et réduction des hallucinations
-        system_prompt = f"""Tu es Kibali, un assistant IA chaleureux, précis et expert du Gabon, basé à {geo['city']}.
+    # 5. Prompt enrichi avec verrouillage contextuel
+    system_prompt = f"""Tu es Kibali, un assistant IA chaleureux, précis et expert du Gabon, basé à {geo['city']}.
 Réponds toujours en français, de façon naturelle, concise et factuelle.
-Utilise uniquement les informations fournies dans les contextes ci-dessous ou ta connaissance vérifiée.
-Si tu n'es pas sûr d'une information, dis-le clairement plutôt que d'inventer.
-Ne répète jamais une réponse précédente et n'ajoute pas d'informations inutiles."""
 
-        full_prompt = f"""### INSTRUCTIONS STRICTES :
+CONTEXTE CONVERSATIONNEL ACTUEL:
+- Sujet en cours: {', '.join(conversation_ctx.subject_keywords) if conversation_ctx.subject_keywords else 'Nouveau sujet'}
+- Nombre de messages sur ce sujet: {conversation_ctx.message_count}
+
+PRIORITÉ DES SOURCES:
+1. Documents uploadés (PDF Vault) - Source la plus fiable
+2. Mémoire conversationnelle récente et pertinente
+3. Informations Web actualisées
+
+Si une information vient d'un document uploadé, mentionne-le brièvement.
+Adapte-toi aux changements brusques de sujet en restant cohérent."""
+
+    full_prompt = f"""### INSTRUCTIONS STRICTES :
 {system_prompt}
 
-### CONTEXTE DOCUMENTS (sources fiables uploadées) :
-{rag_context}
+### CONTEXTE DOCUMENTS (PDF Vault) :
+{rag_context if rag_context else "Aucun document pertinent trouvé."}
 
-### HISTORIQUE DE LA CONVERSATION (contexte du fil du chat) :
-{memory_context}
+### HISTORIQUE PERTINENT (Mémoire adaptative) :
+{memory_context if memory_context else "Pas d'historique pertinent."}
 
-### INFORMATIONS RÉCENTES DU WEB (vérifiées) :
-{web_context}
+### INFORMATIONS WEB RÉCENTES :
+{web_context if web_context else "Pas d'informations web disponibles."}
 
-### QUESTION DE L'UTILISATEUR :
+### QUESTION :
 {user_message}
 
-### RÉPONSE (en français uniquement, directe, précise, sans répétition ni hallucination) :
+### RÉPONSE (en français uniquement) :
 """
 
-        inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True, max_length=8192).to(model.device)
-
-        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=120.0)
-
-        def generate_stream():
-            try:
-                model.generate(
-                    **inputs,
-                    streamer=streamer,
-                    max_new_tokens=1024,
-                    temperature=0.6,
-                    do_sample=True,
-                    top_p=0.85,
-                    top_k=50,
-                    repetition_penalty=1.2,
-                    length_penalty=0.8
-                )
-            except Exception as e:
-                logger.error(f"Erreur génération : {e}")
-
-        thread = Thread(target=generate_stream)
-        thread.start()
-
-        response_text = ""
-        try:
-            for new_text in streamer:
-                if new_text is None:
-                    break
-                response_text += new_text
-        except Exception as e:
-            logger.error(f"Erreur streaming : {e}")
-            response_text = "Désolé, une erreur est survenue pendant la génération."
+    inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True, max_length=8192).to(model.device)
 
-        response_text = response_text.strip()
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=120.0)
 
-        # 5. Mise à jour automatique de la base vectorielle de mémoire
-        if response_text and response_text != "Désolé, une erreur est survenue pendant la génération.":
-            try:
-                memory_entry = f"Utilisateur : {user_message}\nKibali : {response_text}"
-                memory_texts.append(memory_entry)
-                mem_emb = embed_model.encode([memory_entry], normalize_embeddings=True).astype('float32')
-                memory_index.add(mem_emb)
-            except Exception as e:
-                logger.error(f"Erreur mise à jour mémoire : {e}")
+    def generate_stream():
+        try:
+            model.generate(
+                **inputs,
+                streamer=streamer,
+                max_new_tokens=1024,
+                temperature=0.6,
+                do_sample=True,
+                top_p=0.85,
+                top_k=50,
+                repetition_penalty=1.2,
+                length_penalty=0.8
+            )
+        except Exception as e:
+            logger.error(f"Erreur génération : {e}")
+
+    thread = Thread(target=generate_stream)
+    thread.start()
+
+    response_text = ""
+    for new_text in streamer:
+        if new_text is not None:
+            response_text += new_text
+    response_text = response_text.strip()
+
+    # Ajout en temps réel à la mémoire
+    if response_text:
+        add_to_memory_realtime(
+            user_message, 
+            response_text, 
+            conversation_ctx.subject_keywords
+        )
+
+    # Informations contextuelles
+    context_info = {
+        "subject_keywords": conversation_ctx.subject_keywords,
+        "message_count": conversation_ctx.message_count,
+        "memory_used": len(memory_texts_filtered),
+        "rag_sources": list(set(rag_sources)),
+        "web_results": len(search_results.get("results", []))
+    }
 
-        return ChatResponse(response=response_text, images=web_images)
+    return ChatResponse(response=response_text, images=web_images, context_info=context_info)
 
-    except HTTPException as he:
-        raise he
-    except Exception as e:
-        logger.error(f"Erreur inattendue dans /chat : {e}")
-        raise HTTPException(status_code=500, detail=f"Erreur serveur : {str(e)}")
+@app.post("/upload")
+async def upload(files: List[UploadFile] = File(...)):
+    total_added = 0
+    processed_files = 0
 
-@app.post("/upload-pdfs")
-async def upload_pdfs(files: List[UploadFile] = File(...)):
-    """Upload et indexation de fichiers PDF"""
-    added = 0
-    errors = []
-    
     for file in files:
         if not file.filename.lower().endswith(".pdf"):
-            errors.append(f"{file.filename} : n'est pas un PDF")
             continue
-        
+
         try:
             content = await file.read()
             text = extract_text_from_pdf(content)
-            
-            if text.strip():
-                chunks = chunk_text(text)
-                if chunks:
-                    embeddings = embed_model.encode(chunks, normalize_embeddings=True).astype('float32')
-                    doc_index.add(embeddings)
-                    doc_chunks.extend(chunks)
-                    added += len(chunks)
-                    logger.info(f"PDF {file.filename} indexé : {len(chunks)} chunks")
-            else:
-                errors.append(f"{file.filename} : aucun texte extrait")
-                
+
+            if not text:
+                logger.warning(f"Aucun texte extrait de {file.filename}")
+                continue
+
+            chunks = chunk_text(text)
+            if not chunks:
+                continue
+
+            # Métadonnées pour chaque chunk
+            timestamp = datetime.now().isoformat()
+            for chunk in chunks:
+                doc_metadata.append({
+                    "source": file.filename,
+                    "timestamp": timestamp,
+                    "length": len(chunk)
+                })
+
+            embeddings = embed_model.encode(chunks, normalize_embeddings=True).astype('float32')
+            doc_index.add(embeddings)
+            doc_chunks.extend(chunks)
+
+            total_added += len(chunks)
+            processed_files += 1
+
+            logger.info(f"Upload réussi : {file.filename} → {len(chunks)} chunks ajoutés")
+
         except Exception as e:
-            logger.error(f"Erreur upload {file.filename} : {e}")
-            errors.append(f"{file.filename} : {str(e)}")
-    
-    return {
-        "status": "success" if added > 0 else "partial" if errors else "failed",
-        "chunks_added": added,
-        "total_doc_chunks": len(doc_chunks),
-        "errors": errors if errors else None
-    }
+            logger.error(f"Erreur lors du traitement de {file.filename} : {e}")
 
-@app.get("/health")
-async def health():
-    """Health check pour monitoring"""
     return {
-        "status": "healthy",
-        "model_loaded": model is not None,
-        "embed_model_loaded": embed_model is not None
+        "status": "success",
+        "files_processed": processed_files,
+        "chunks_added": total_added,
+        "total_doc_chunks": len(doc_chunks)
     }
 
-# --- MESSAGE AU DEMARRAGE ---
+@app.post("/upload-pdfs")
+async def upload_pdfs(files: List[UploadFile] = File(...)):
+    return await upload(files)
+
+@app.post("/clear-memory")
+async def clear_memory():
+    """Efface la mémoire conversationnelle"""
+    global memory_index, memory_texts, memory_metadata
+    memory_index = faiss.IndexFlatL2(dimension)
+    memory_texts = []
+    memory_metadata = []
+    conversation_ctx.__init__()
+    return {"status": "memory_cleared", "message": "Mémoire conversationnelle effacée"}
+
+# --- DEMARRAGE ---
 @app.on_event("startup")
 async def startup_event():
     logger.info("🚀 Kibali AI API démarrée avec succès !")
-    logger.info(f"Modèle : {MODEL_REPO}")
-    logger.info(f"Device : {model.device}")
-    logger.info(f"CUDA disponible : {torch.cuda.is_available()}")
-    logger.info("Accès : http://localhost:8000 | Docs : http://localhost:8000/docs")
-
-@app.on_event("shutdown")
-async def shutdown_event():
-    logger.info("👋 Arrêt de Kibali AI API")
+    logger.info(f"Accès : http://localhost:8000 | Docs : http://localhost:8000/docs")
+    logger.info(f"Mémoire adaptative et réflexion contextuelle activées ✓")

requirements.txt

@@ -1,21 +1,22 @@
-torch==2.11.0.dev20251229+cu130
-torchvision==0.25.0.dev20251229+cu130
-torchaudio==2.10.0.dev20251229+cu130
-triton==3.6.0+git9844da95
+# --- Core IA (Versions Stables) ---
+# Note: On ne met pas de version figée pour torch ici, 
+# car on l'installe via l'URL spécifique dans le Dockerfile.
 transformers==4.41.2
 bitsandbytes>=0.41.0
 accelerate
 sentence-transformers
 faiss-gpu
+
+# --- Serveur & API ---
 fastapi
 uvicorn[standard]
 pydantic
 python-multipart
-fastapi.middleware.cors
-fastapi.staticfiles
+
+# --- Outils & Data ---
 pypdf>=3.0.0
-numpy<2
+numpy<2.0.0
 folium
 duckduckgo-search
 huggingface_hub==0.23.4
-spaces
+spaces

tools/todo.py

@@ -1,41 +1,253 @@
-# tools/todo.py
 import streamlit as st
 import time
+from typing import List, Optional
+import re
 
-def execute_reflection_plan(prompt, geo_info=None, messages=[]):
-    """
-    Phase de réflexion structurée avec verrouillage du sujet conversationnel.
-    """
-    if geo_info is None: geo_info = {}
+def analyze_query_type(prompt: str) -> dict:
+    """Analyse le type de requête pour adapter la stratégie de réflexion"""
+    prompt_lower = prompt.lower()
+    
+    analysis = {
+        "type": "general",
+        "needs_web": False,
+        "needs_memory": False,
+        "needs_docs": False,
+        "complexity": "simple",
+        "temporal": False,
+        "geographical": False
+    }
+    
+    # Détection de questions temporelles
+    temporal_keywords = ["aujourd'hui", "maintenant", "récent", "actuel", "dernier", "2024", "2025"]
+    if any(kw in prompt_lower for kw in temporal_keywords):
+        analysis["temporal"] = True
+        analysis["needs_web"] = True
+    
+    # Détection géographique
+    geo_keywords = ["gabon", "libreville", "port-gentil", "franceville", "oyem", "où", "localisation"]
+    if any(kw in prompt_lower for kw in geo_keywords):
+        analysis["geographical"] = True
+    
+    # Détection de questions sur documents
+    doc_keywords = ["selon le document", "d'après le pdf", "dans le fichier", "uploadé"]
+    if any(kw in prompt_lower for kw in doc_keywords):
+        analysis["needs_docs"] = True
+        analysis["type"] = "document_query"
+    
+    # Détection de continuation de conversation
+    continuation_keywords = ["ils", "elles", "lui", "leur", "donc", "alors", "ensuite", "aussi", "également"]
+    if any(kw in prompt_lower for kw in continuation_keywords) or len(prompt.split()) < 5:
+        analysis["needs_memory"] = True
+        analysis["type"] = "continuation"
+    
+    # Détection de complexité
+    if len(prompt.split()) > 15 or "?" in prompt and prompt.count("?") > 1:
+        analysis["complexity"] = "complex"
+    elif "pourquoi" in prompt_lower or "comment" in prompt_lower or "expliquer" in prompt_lower:
+        analysis["complexity"] = "medium"
+    
+    # Questions nécessitant le web
+    web_keywords = ["actualité", "news", "prix", "cours", "météo", "horaire"]
+    if any(kw in prompt_lower for kw in web_keywords):
+        analysis["needs_web"] = True
+        analysis["type"] = "real_time"
+    
+    return analysis
+
+def detect_subject_shift(prompt: str, current_subject: str, subject_keywords: List[str]) -> dict:
+    """Détecte un changement de sujet et évalue la force du changement"""
+    if not current_subject or not subject_keywords:
+        return {
+            "shift_detected": False,
+            "shift_strength": 0.0,
+            "new_subject_detected": True,
+            "reason": "Premier message ou pas de sujet actuel"
+        }
     
-    # 1. ANALYSE DU SUJET (Contextualisation)
-    # Si le prompt est court, on récupère le sujet du dernier message
-    subject = prompt
-    is_continuation = len(prompt.split()) < 5 or any(x in prompt.lower() for x in ["ils", "elles", "donc", "alors", "ceux-là"])
+    prompt_lower = prompt.lower()
     
-    if is_continuation and len(messages) > 0:
-        # On extrait le sujet principal du dernier échange pour "nourrir" la réflexion
-        subject = f"{prompt} (contexte: {messages[-1]['content'][:50]}...)"
+    # Calcul de l'overlap des mots-clés
+    prompt_words = set(re.findall(r'\b\w{4,}\b', prompt_lower))
+    keyword_overlap = len(prompt_words.intersection(set(subject_keywords)))
+    overlap_ratio = keyword_overlap / max(len(subject_keywords), 1)
+    
+    # Détection de marqueurs de changement de sujet
+    shift_markers = ["maintenant", "sinon", "autre chose", "parlons de", "passons à", "nouveau sujet"]
+    has_shift_marker = any(marker in prompt_lower for marker in shift_markers)
+    
+    # Calcul de la force du changement
+    shift_strength = 0.0
+    if overlap_ratio < 0.2:
+        shift_strength += 0.5
+    if has_shift_marker:
+        shift_strength += 0.3
+    if len(prompt_words) > 5 and keyword_overlap == 0:
+        shift_strength += 0.2
+    
+    shift_detected = shift_strength > 0.4
+    
+    return {
+        "shift_detected": shift_detected,
+        "shift_strength": shift_strength,
+        "new_subject_detected": shift_strength > 0.6,
+        "keyword_overlap": keyword_overlap,
+        "overlap_ratio": overlap_ratio,
+        "reason": f"Overlap: {overlap_ratio:.1%}, Marqueurs: {has_shift_marker}"
+    }
 
-    location = f"{geo_info.get('city', 'Libreville')}, {geo_info.get('country', 'Gabon')}"
-    method = geo_info.get('method', 'Inconnue')
+def generate_search_strategy(analysis: dict, subject_keywords: List[str], geo_info: dict) -> dict:
+    """Génère une stratégie de recherche optimisée"""
+    strategy = {
+        "use_rag": analysis["needs_docs"],
+        "use_memory": analysis["needs_memory"] or analysis["type"] == "continuation",
+        "use_web": analysis["needs_web"] or analysis["temporal"],
+        "memory_k": 5,
+        "rag_k": 3,
+        "web_enhanced": False,
+        "search_query_suffix": ""
+    }
+    
+    # Ajustement selon la complexité
+    if analysis["complexity"] == "complex":
+        strategy["memory_k"] = 8
+        strategy["rag_k"] = 5
+    elif analysis["complexity"] == "simple":
+        strategy["memory_k"] = 3
+        strategy["rag_k"] = 2
+    
+    # Enrichissement de la recherche web
+    if analysis["needs_web"]:
+        strategy["web_enhanced"] = True
+        if subject_keywords:
+            strategy["search_query_suffix"] = f" {' '.join(subject_keywords[:3])}"
+        if analysis["geographical"]:
+            strategy["search_query_suffix"] += f" {geo_info.get('city', 'Gabon')}"
+    
+    return strategy
 
-    with st.status(f"🧠 Kibali Thinking Engine", expanded=True) as status:
-        st.write(f"🌍 **Localisation active :** {location}")
-        
-        if is_continuation:
-            st.write(f"🔗 **Liaison contextuelle :** Analyse du sujet précédent détectée.")
-        
-        steps = [
-            f"Identification de l'entité : Recherche d'informations sur '{subject}'.",
-            "Extraction de la mémoire sémantique FAISS pour éviter les répétitions.",
-            "Requête Web enrichie : Combinaison du sujet récent + question actuelle pour les images.",
-            f"Vérification de la pertinence culturelle et temporelle pour le Gabon."
-        ]
+def execute_reflection_plan(
+    prompt: str, 
+    geo_info: Optional[dict] = None, 
+    messages: Optional[List] = None,
+    current_subject: Optional[str] = None,
+    subject_keywords: Optional[List[str]] = None
+):
+    """
+    Phase de réflexion structurée avec analyse contextuelle avancée et adaptation dynamique.
+    """
+    if geo_info is None:
+        geo_info = {}
+    if messages is None:
+        messages = []
+    if subject_keywords is None:
+        subject_keywords = []
+    
+    # ÉTAPE 1: Analyse du type de requête
+    query_analysis = analyze_query_type(prompt)
+    
+    # ÉTAPE 2: Détection de changement de sujet
+    subject_shift = detect_subject_shift(prompt, current_subject, subject_keywords)
+    
+    # ÉTAPE 3: Génération de la stratégie de recherche
+    search_strategy = generate_search_strategy(query_analysis, subject_keywords, geo_info)
+    
+    # ÉTAPE 4: Affichage de la réflexion (si Streamlit disponible)
+    try:
+        location = f"{geo_info.get('city', 'Libreville')}, {geo_info.get('country', 'Gabon')}"
         
-        for i, step in enumerate(steps):
-            st.write(f"{i+1}. {step}")
-            time.sleep(0.15)
+        with st.status("🧠 Kibali Thinking Engine", expanded=True) as status:
+            st.write(f"🌍 **Localisation active :** {location}")
+            st.write("")
+            
+            # Analyse du type de requête
+            st.write("### 📊 Analyse de la requête")
+            st.write(f"- **Type :** {query_analysis['type'].replace('_', ' ').title()}")
+            st.write(f"- **Complexité :** {query_analysis['complexity'].title()}")
+            
+            if query_analysis['temporal']:
+                st.write("- ⏰ **Dimension temporelle détectée** → Recherche web activée")
+            if query_analysis['geographical']:
+                st.write(f"- 🗺️ **Contexte géographique :** {location}")
+            
+            time.sleep(0.2)
+            st.write("")
+            
+            # Détection de changement de sujet
+            st.write("### 🔄 Analyse du contexte conversationnel")
+            if subject_shift['shift_detected']:
+                if subject_shift['new_subject_detected']:
+                    st.write("- 🆕 **Nouveau sujet détecté** → Rafraîchissement du contexte")
+                else:
+                    st.write(f"- ⚠️ **Changement partiel** (force: {subject_shift['shift_strength']:.0%})")
+                st.write(f"  *Raison : {subject_shift['reason']}*")
+            else:
+                st.write("- ✅ **Continuité du sujet actuel**")
+                if subject_keywords:
+                    st.write(f"  *Mots-clés actifs : {', '.join(subject_keywords[:5])}*")
+                st.write(f"  *Overlap : {subject_shift['keyword_overlap']}/{len(subject_keywords)} mots-clés*")
+            
+            time.sleep(0.2)
+            st.write("")
+            
+            # Stratégie de recherche
+            st.write("### 🎯 Stratégie de réponse")
+            sources = []
+            if search_strategy['use_rag']:
+                sources.append(f"📚 Documents PDF (top {search_strategy['rag_k']})")
+            if search_strategy['use_memory']:
+                sources.append(f"🧠 Mémoire conversationnelle (top {search_strategy['memory_k']})")
+            if search_strategy['use_web']:
+                web_label = "🌐 Recherche web"
+                if search_strategy['web_enhanced']:
+                    web_label += " (enrichie avec contexte)"
+                sources.append(web_label)
+            
+            if not sources:
+                sources.append("💭 Connaissance générale du modèle")
             
-        status.update(label="✅ Stratégie de réponse validée", state="complete", expanded=False)
-    return True
+            for i, source in enumerate(sources, 1):
+                st.write(f"{i}. {source}")
+                time.sleep(0.15)
+            
+            st.write("")
+            
+            # Plan d'action détaillé
+            st.write("### ⚙️ Plan d'exécution")
+            steps = []
+            
+            if search_strategy['use_rag']:
+                steps.append("Extraction des chunks pertinents depuis la base vectorielle PDF")
+            
+            if search_strategy['use_memory']:
+                steps.append("Récupération des échanges similaires avec scoring de pertinence")
+            
+            if search_strategy['use_web']:
+                query_suffix = search_strategy['search_query_suffix']
+                steps.append(f"Requête web : '{prompt[:50]}...{query_suffix}'")
+            
+            steps.append("Synthèse des sources avec priorisation hiérarchique")
+            steps.append("Génération de la réponse avec verrouillage contextuel")
+            
+            for i, step in enumerate(steps, 1):
+                st.write(f"{i}. {step}")
+                time.sleep(0.15)
+            
+            time.sleep(0.3)
+            status.update(
+                label="✅ Stratégie validée - Génération en cours", 
+                state="complete", 
+                expanded=False
+            )
+    
+    except Exception as e:
+        # Fallback si Streamlit n'est pas disponible
+        print(f"[Kibali Thinking] Type: {query_analysis['type']}, Complexité: {query_analysis['complexity']}")
+        print(f"[Kibali Thinking] Changement de sujet: {subject_shift['shift_detected']} (force: {subject_shift['shift_strength']:.0%})")
+        print(f"[Kibali Thinking] Sources: RAG={search_strategy['use_rag']}, Memory={search_strategy['use_memory']}, Web={search_strategy['use_web']}")
+    
+    return {
+        "analysis": query_analysis,
+        "subject_shift": subject_shift,
+        "strategy": search_strategy,
+        "execution_plan_ready": True
+    }