feat: intégration du workflow map-reduce parallèle pour le pré-traitement des fichiers joints, correction de session (7 jours) et redirection auto sur 401

backend/main.py

@@ -73,7 +73,8 @@ def login(login_data: LoginRequest, response: Response, db: Session = Depends(ge
     user = db.query(User).filter(User.username == login_data.username).first()
     if not user or not verify_password(login_data.password, user.hashed_password): raise HTTPException(status_code=401, detail="Identifiants incorrects.")
     if not verify_totp(user.totp_secret, login_data.totp_code): raise HTTPException(status_code=401, detail="2FA invalide.")
-    response.set_cookie(key="session_token", value=create_access_token(data={"sub": user.username}), httponly=True, max_age=3600, samesite="lax")
+    # Correction : Extension de la durée de session à 7 jours (604800 secondes) pour éviter les erreurs 401 intempestives
+    response.set_cookie(key="session_token", value=create_access_token(data={"sub": user.username}), httponly=True, max_age=604800, samesite="lax")
     return {"message": "Connexion réussie"}
 
 @app.get("/dashboard")
@@ -143,10 +144,12 @@ def get_messages(project_id: int, db: Session = Depends(get_db), current_user: U
 
 @app.post("/api/projects/{project_id}/messages", response_model=List[MessageResponse])
 async def create_message(project_id: int, message: MessageCreate, db: Session = Depends(get_db), current_user: User = Depends(get_current_user)):
-    final_content = message.content
+    extracted_files_data = []
+    files_names = []
+    
     if message.files:
-        files_text = ""
         for f in message.files:
+            files_names.append(f.name)
             content = f.content
             if content.startswith("data:"):
                 try:
@@ -175,7 +178,6 @@ async def create_message(project_id: int, message: MessageCreate, db: Session =
                                     if any(row_vals):
                                         extracted_text += "\t".join(row_vals) + "\n"
                             
-                            # Extraction des Macros VBA
                             if ext in ['xlsm', 'xlsb', 'xls']:
                                 try:
                                     from oletools.olevba import VBA_Parser
@@ -192,24 +194,28 @@ async def create_message(project_id: int, message: MessageCreate, db: Session =
                         except ImportError:
                             extracted_text = "[Erreur: L'administrateur doit exécuter 'pip install openpyxl' sur le serveur pour lire Excel.]"
                     else:
-                        extracted_text = f"[Fichier binaire non supporté textuellement : {f.name}]"
+                        extracted_text = file_bytes.decode('utf-8', errors='replace')
                     
-                    files_text += f"\n\n[Fichier attaché : {f.name}]\n```text\n{extracted_text}\n```"
+                    extracted_files_data.append({"name": f.name, "content": extracted_text})
                 except Exception as e:
-                    files_text += f"\n\n[Fichier attaché : {f.name} - ERREUR DE DECODAGE: {str(e)}]"
+                    extracted_files_data.append({"name": f.name, "content": f"[ERREUR DE DECODAGE: {str(e)}]"})
             else:
-                files_text += f"\n\n[Fichier attaché : {f.name}]\n```\n{content}\n```"
+                extracted_files_data.append({"name": f.name, "content": content})
 
-        final_content = message.content + files_text
+    # On ne stocke plus le contenu brut des fichiers en DB pour éviter d'exploser le contexte des requêtes suivantes
+    db_content = message.content
+    if files_names:
+        db_content += f"\n\n[Fichiers joints pour analyse : {', '.join(files_names)}]"
 
-    db.add(Message(role=message.role, content=final_content, project_id=project_id))
+    db.add(Message(role=message.role, content=db_content, project_id=project_id))
     db.commit()
     
     history = db.query(Message).filter(Message.project_id == project_id).order_by(Message.created_at.asc()).all()
     settings = db.query(SystemSettings).first()
     conf = message.config.dict() if message.config else {"workers": ["gemini-3.5-flash-lite"]} 
     
-    ai_resp = await run_orchestrator(db, history, settings, conf)
+    # Transmission des données de fichiers en mémoire vive à l'orchestrateur (Map-Reduce)
+    ai_resp = await run_orchestrator(db, history, settings, conf, extracted_files_data)
     
     db.add(Message(role="assistant", content=ai_resp, project_id=project_id))
     db.commit()

backend/orchestrator.py

@@ -197,19 +197,39 @@ async def ask_agent(client, model_id, messages, provider="openrouter"):
     resp = await client.chat.completions.create(**kwargs)
     return resp.choices[0].message.content
 
-async def run_orchestrator(db: Session, history: list, settings: SystemSettings, config: dict) -> str:
+async def run_orchestrator(db: Session, history: list, settings: SystemSettings, config: dict, extracted_files: list = None) -> str:
     workers = config.get("workers", ["gemini-3.5-flash-lite"])
     user_prompt = history[-1].content
+    original_user_text = user_prompt.split("\n\n[Fichiers joints")[0] if "[Fichiers joints" in user_prompt else user_prompt
     formatted_history = [{"role": msg.role, "content": msg.content} for msg in history[:-1]]
 
     final_response = ""
     try:
-        # Fallback sécurisé en cas de champ vide
         p_mod = config.get("prompter")
         if not p_mod: p_mod = "gemini-3.5-flash-lite"
         
         p_client, p_prov = get_client_for_model(db, p_mod, settings)
-        prompt_system = "You are an expert prompt engineer. Translate and optimize the user request into clear, precise English tailored for AI execution."
+
+        # --- WORKFLOW MAP-REDUCE : PRÉ-TRAITEMENT PARALLÈLE DES FICHIERS ---
+        files_context = ""
+        if extracted_files:
+            async def process_single_file(f):
+                file_prompt = f"Demande de l'utilisateur : '{original_user_text}'.\n\nAnalysez le fichier ci-dessous. Extrayez, résumez et conservez méticuleusement tout le code, les macros VBA, les requêtes SQL, ou les données métier pertinentes pour répondre à la demande.\n\nFichier : {f['name']}\nContenu :\n```\n{f['content']}\n```"
+                file_sys = "You are an expert data analyst and senior developer. Extract the most important technical information from the file without losing critical code syntax."
+                try:
+                    analysis = await ask_agent(p_client, p_mod, [{"role": "system", "content": file_sys}, {"role": "user", "content": file_prompt}], p_prov)
+                    return f"\n\n--- Extraction du fichier {f['name']} ---\n{analysis}"
+                except Exception as e:
+                    return f"\n\n--- Erreur sur {f['name']} ---\n{str(e)}"
+
+            file_tasks = [process_single_file(f) for f in extracted_files]
+            file_analyses = await asyncio.gather(*file_tasks)
+            files_context = "".join(file_analyses)
+            
+            user_prompt = f"{original_user_text}\n\nVoici les données pré-traitées des fichiers joints :\n{files_context}"
+
+        # --- OPTIMISATION & TRADUCTION ---
+        prompt_system = "You are an expert prompt engineer. Translate and optimize the user request and any file context into clear, precise English tailored for AI execution. Keep all code blocks intact."
         optimized = await ask_agent(p_client, p_mod, [{"role": "system", "content": prompt_system}, {"role": "user", "content": user_prompt}], p_prov)
 
         if len(workers) == 1:
@@ -237,7 +257,7 @@ async def run_orchestrator(db: Session, history: list, settings: SystemSettings,
             "You may translate code comments into French if appropriate, but leave code syntax strictly intact."
         )
         
-        synth = f"User Request: {user_prompt}\n\n" + "\n".join([f"--- EXPERT {i+1} ---\n{str(r)}" for i, r in enumerate(responses)])
+        synth = f"User Request: {original_user_text}\n\n" + "\n".join([f"--- EXPERT {i+1} ---\n{str(r)}" for i, r in enumerate(responses)])
         final_response = await ask_agent(c_client, c_mod, [{"role": "system", "content": concat_system}, {"role": "user", "content": synth}], c_prov)
 
     except Exception as e:

frontend/dashboard.html

@@ -631,7 +631,7 @@
                     attachedFiles.value.push({ name: file.name, content: e.target.result });
                 };
 
-                // Les fichiers binaires (PDF, Excel, etc.) sont lus en Base64 pour que Python s'en charge.
+                // Les fichiers binaires sont lus en Base64.
                 if (isText) {
                     reader.readAsText(file);
                 } else {
@@ -687,6 +687,10 @@
                         const data = await response.json(); 
                         if (Array.isArray(data)) messages.value = data; 
                         await fetchFinances(); 
+                    } else if (response.status === 401) {
+                        alert("Votre session a expiré. Veuillez vous reconnecter.");
+                        window.location.href = '/login';
+                        return;
                     } else { 
                         const err = await response.json(); alert("Erreur API IA : " + err.detail); 
                     }