feat maj fonctionnement

backend/main.py

@@ -3,15 +3,15 @@ from fastapi.responses import RedirectResponse, FileResponse
 from fastapi.staticfiles import StaticFiles
 from sqlalchemy.orm import Session
 from typing import List
-import os, json, asyncio, shutil, base64, io
+import os, json, asyncio, shutil, base64, io, csv, zipfile
 from datetime import datetime
 import pytz
 
 from .database import engine, Base, get_db, SessionLocal
 from .auth import get_password_hash, generate_totp_secret, get_totp_uri, verify_password, verify_totp, create_access_token, verify_token
-from .schemas import AdminCreate, LoginRequest, ProjectCreate, ProjectResponse, ProjectRename, MessageCreate, MessageResponse, PasswordChange
+from .schemas import AdminCreate, LoginRequest, ProjectCreate, ProjectResponse, ProjectRename, MessageCreate, MessageResponse, PasswordChange, ModelReplacementRequest, LogRequest
 from .models import User, Project, Message, SystemSettings, AIModel, FinancialLog
-from .orchestrator import run_orchestrator, sync_providers_models, sync_finances
+from .orchestrator import run_orchestrator, sync_providers_models, sync_finances, activity_logs, log_activity
 
 Base.metadata.create_all(bind=engine)
 app = FastAPI(title="AETHAS38")
@@ -73,7 +73,6 @@ 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.")
-    # 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"}
 
@@ -83,6 +82,52 @@ def dashboard(request: Request):
     if not token or not verify_token(token): return RedirectResponse(url="/login")
     return FileResponse(os.path.join(os.getcwd(), "frontend", "dashboard.html"))
 
+@app.get("/api/logs")
+def get_logs():
+    return {"logs": activity_logs}
+
+@app.post("/api/logs")
+def add_frontend_log(req: LogRequest):
+    log_activity(f"[Système UI] {req.message}")
+    return {"status": "ok"}
+
+@app.post("/api/models/suggest_replacement")
+async def suggest_replacement(req: ModelReplacementRequest, db: Session = Depends(get_db)):
+    log_activity(f"⚠️ Modèle indisponible: {req.missing_model}. Demande de suggestion à Gemini...")
+    settings = db.query(SystemSettings).first()
+    if not settings or not settings.gemini_api_key:
+        log_activity("Clé Gemini non trouvée. Fallback forcé sur gemini-3.5-flash-lite.")
+        return {"suggestion": "gemini-3.5-flash-lite", "reason": "Clé API Gemini non configurée dans le système."}
+
+    models = db.query(AIModel).all()
+    available = [m.model_id for m in models]
+    
+    prompt = f"Le modèle IA '{req.missing_model}' n'est plus disponible. Voici les modèles disponibles : {', '.join(available)}. Trouve le modèle le plus proche techniquement. Réponds UNIQUEMENT avec ce format strict : ID_DU_MODELE | Brève explication en français de 10 mots max. Si aucun ne correspond, renvoie gemini-3.5-flash-lite | Par défaut."
+    
+    try:
+        from openai import AsyncOpenAI
+        client = AsyncOpenAI(base_url="https://generativelanguage.googleapis.com/v1beta/openai/", api_key=settings.gemini_api_key)
+        resp = await client.chat.completions.create(model="gemini-3.5-flash-lite", messages=[{"role": "user", "content": prompt}], max_tokens=50)
+        res = resp.choices[0].message.content.strip()
+        
+        if "|" in res:
+            parts = res.split("|")
+            sugg = parts[0].strip()
+            reason = parts[1].strip()
+        else:
+            sugg = res.strip()
+            reason = "Sélectionné par Gemini."
+            
+        if sugg not in available and sugg != "gemini-3.5-flash-lite":
+            sugg = "gemini-3.5-flash-lite"
+            reason = "Gemini a suggéré un modèle invalide. Fallback par défaut."
+
+        log_activity(f"✅ Remplacement trouvé : {req.missing_model} -> {sugg}")
+        return {"suggestion": sugg, "reason": reason}
+    except Exception as e:
+        log_activity(f"Erreur d'interrogation Gemini: {str(e)}. Fallback par défaut.")
+        return {"suggestion": "gemini-3.5-flash-lite", "reason": f"Erreur API."}
+
 @app.get("/api/users/me")
 def get_me(current_user: User = Depends(get_current_user)):
     return {"username": current_user.username, "is_admin": current_user.is_admin, "is_superadmin": current_user.is_superadmin, "avatar_path": current_user.avatar_path}
@@ -202,7 +247,6 @@ async def create_message(project_id: int, message: MessageCreate, db: Session =
             else:
                 extracted_files_data.append({"name": f.name, "content": content})
 
-    # 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)}]"
@@ -214,7 +258,6 @@ async def create_message(project_id: int, message: MessageCreate, db: Session =
     settings = db.query(SystemSettings).first()
     conf = message.config.dict() if message.config else {"workers": ["gemini-3.5-flash-lite"]} 
     
-    # 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))
@@ -245,6 +288,37 @@ async def trigger_model_sync(db: Session = Depends(get_db), current_user: User =
 @app.get("/api/models/export")
 def export_models(db: Session = Depends(get_db), current_user: User = Depends(get_current_user)):
     if not current_user.is_admin: raise HTTPException(status_code=403, detail="Accès admin requis.")
-    models = db.query(AIModel).all()
-    data = [{"provider": m.provider, "model_id": m.model_id, "name": m.name, "description_fr": m.description_fr, "domain": m.domain, "is_free": m.is_free, "context_length": m.context_length, "pricing_prompt": m.pricing_prompt, "pricing_completion": m.pricing_completion} for m in models]
-    return Response(content=json.dumps(data), media_type="application/json", headers={"Content-Disposition": "attachment; filename=aethas38_models.json"})
+    models = db.query(AIModel).order_by(AIModel.provider.asc(), AIModel.name.asc()).all()
+
+    csv_io = io.StringIO()
+    writer = csv.writer(csv_io, delimiter=',')
+    writer.writerow(["Provider", "Model ID", "Name", "Domain", "Is Free", "Context Length", "Pricing Prompt", "Pricing Completion", "Description"])
+    for m in models:
+        writer.writerow([m.provider, m.model_id, m.name, m.domain, m.is_free, m.context_length, m.pricing_prompt, m.pricing_completion, m.description_fr])
+    
+    md_content = f"# Extraction des Modèles IA - AETHAS38\n\n**Date d'extraction :** {datetime.now().strftime('%d/%m/%Y à %H:%M:%S')}\n\n"
+    providers = sorted(list(set(m.provider for m in models)))
+    for prov in providers:
+        md_content += f"## Fournisseur : {prov.upper()}\n\n"
+        prov_models = [m for m in models if m.provider == prov]
+        for m in prov_models:
+            price_info = "**GRATUIT**" if m.is_free else f"In: ${m.pricing_prompt:.2f} / Out: ${m.pricing_completion:.2f}"
+            ctx_info = f"{int(m.context_length/1000)}k"
+            desc = m.description_fr.replace('\n', ' ') if m.description_fr else ""
+            md_content += f"- **{m.name or m.model_id}** (`{m.model_id}`)\n"
+            md_content += f"  - *Domaine :* {m.domain}\n"
+            md_content += f"  - *Prix (1M tokens) :* {price_info}\n"
+            md_content += f"  - *Contexte :* {ctx_info}\n"
+            md_content += f"  - *Description :* {desc}\n\n"
+
+    zip_io = io.BytesIO()
+    with zipfile.ZipFile(zip_io, mode='w', compression=zipfile.ZIP_DEFLATED) as zf:
+        zf.writestr("models_export.csv", csv_io.getvalue().encode('utf-8'))
+        zf.writestr(f"{datetime.now().strftime('%Y%m%d')}-extraction-modeles.md", md_content.encode('utf-8'))
+
+    zip_io.seek(0)
+    return Response(
+        content=zip_io.getvalue(), 
+        media_type="application/zip", 
+        headers={"Content-Disposition": f"attachment; filename=aethas38_models_{datetime.now().strftime('%Y%m%d')}.zip"}
+    )

backend/orchestrator.py

@@ -8,6 +8,16 @@ from sqlalchemy.exc import IntegrityError
 from .models import SystemSettings, AIModel, FinancialLog
 from datetime import datetime, timezone
 
+# --- LOGGER GLOBAL POUR LE TERMINAL ---
+activity_logs = []
+
+def log_activity(msg: str):
+    ts = datetime.now(timezone.utc).strftime('%H:%M:%S')
+    activity_logs.append(f"[{ts}] {msg}")
+    if len(activity_logs) > 100:
+        activity_logs.pop(0)
+# --------------------------------------
+
 def determine_domain(model_id: str) -> str:
     mid = model_id.lower()
     if "vision" in mid or "vl" in mid or "omni" in mid: return "Vision & Texte"
@@ -74,6 +84,7 @@ def update_finance_db(db, provider, balance, usage):
     except Exception as e: print(f"Finance DB Error: {e}")
 
 async def sync_providers_models(db: Session, settings: SystemSettings, sync_type: str = "Automatique"):
+    log_activity(f"Lancement de la synchronisation des modèles ({sync_type})...")
     added = 0
     models_to_process = {}
     async with httpx.AsyncClient(timeout=90.0) as client:
@@ -171,6 +182,7 @@ async def sync_providers_models(db: Session, settings: SystemSettings, sync_type
         db.rollback()
         
     await sync_finances(db, settings)
+    log_activity(f"Synchronisation terminée : {added} modèles analysés.")
     return {"status": "success", "models_processed": added}
 
 def get_client_for_model(db: Session, model_id: str, settings: SystemSettings):
@@ -194,7 +206,6 @@ async def ask_agent(client, model_id, messages, provider="openrouter"):
     kwargs = {"model": model_id, "messages": messages}
     if provider == "openrouter": 
         kwargs["extra_headers"] = {"HTTP-Referer": "https://aethas38.duckdns.org", "X-Title": "AETHAS38 Orchestrator"}
-        # Activation du plugin natif de compression d'OpenRouter pour éviter le dépassement de contexte
         kwargs["extra_body"] = {"plugins": [{"id": "context-compression"}]}
         
     resp = await client.chat.completions.create(**kwargs)
@@ -211,70 +222,80 @@ async def run_orchestrator(db: Session, history: list, settings: SystemSettings,
         p_mod = config.get("prompter")
         if not p_mod: p_mod = "gemini-3.5-flash-lite"
         
+        log_activity(f"Démarrage de l'orchestration. Modèle Prompteur: {p_mod}")
         p_client, p_prov = get_client_for_model(db, p_mod, settings)
 
         # --- WORKFLOW MAP-REDUCE : PRÉ-TRAITEMENT SÉQUENTIEL & CHUNKING ---
         files_context = ""
         if extracted_files:
+            log_activity(f"Traitement Map-Reduce de {len(extracted_files)} fichier(s) joint(s).")
             async def process_single_file(f):
                 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."
                 content = f['content']
-                chunk_size = 150000  # Environ 35k à 40k tokens par morceau pour rester très large par rapport aux limites
+                chunk_size = 150000 
                 
-                # CHUNKING : Découpage intelligent si le fichier est massif
                 if len(content) > chunk_size:
                     chunks = [content[i:i+chunk_size] for i in range(0, len(content), chunk_size)]
                     chunk_analyses = []
+                    log_activity(f"Fichier lourd ({f['name']}): Chunking en {len(chunks)} morceaux.")
                     for idx, chunk in enumerate(chunks):
                         file_prompt = f"Demande de l'utilisateur : '{original_user_text}'.\n\nPartie {idx+1}/{len(chunks)} du fichier '{f['name']}'. Analysez, extrayez et résumez le code, VBA, SQL ou les données pertinentes.\n\nContenu :\n```\n{chunk}\n```"
                         try:
                             analysis = await ask_agent(p_client, p_mod, [{"role": "system", "content": file_sys}, {"role": "user", "content": file_prompt}], p_prov)
                             chunk_analyses.append(analysis)
+                            log_activity(f"Analyse chunk {idx+1}/{len(chunks)} pour {f['name']} réussie.")
                         except Exception as e:
                             chunk_analyses.append(f"[Erreur sur la partie {idx+1}: {str(e)}]")
+                            log_activity(f"Erreur chunk {idx+1}/{len(chunks)} pour {f['name']}: {str(e)}")
                         
-                        await asyncio.sleep(1.5) # Pause anti-spam (429) entre les morceaux
+                        await asyncio.sleep(1.5) 
                     
                     return f"\n\n--- Extraction du fichier {f['name']} (en {len(chunks)} parties) ---\n" + "\n".join(chunk_analyses)
                 else:
                     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{content}\n```"
                     try:
                         analysis = await ask_agent(p_client, p_mod, [{"role": "system", "content": file_sys}, {"role": "user", "content": file_prompt}], p_prov)
+                        log_activity(f"Analyse intégrale de {f['name']} réussie.")
                         return f"\n\n--- Extraction du fichier {f['name']} ---\n{analysis}"
                     except Exception as e:
+                        log_activity(f"Erreur d'analyse sur {f['name']}: {str(e)}")
                         return f"\n\n--- Erreur sur {f['name']} ---\n{str(e)}"
 
             file_analyses = []
             for f in extracted_files:
                 analysis = await process_single_file(f)
                 file_analyses.append(analysis)
-                # SÉQUENÇAGE : Pause de 1.5 seconde entre les fichiers pour éviter l'erreur 429
                 await asyncio.sleep(1.5)
             
             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 ---
+        log_activity(f"Optimisation/Traduction de la requête via Prompteur...")
         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)
 
+        log_activity(f"Lancement de {len(workers)} travailleur(s) en parallèle...")
         if len(workers) == 1:
             w_mod = workers[0]
             client, provider = get_client_for_model(db, w_mod, settings)
             worker_response = await ask_agent(client, w_mod, formatted_history + [{"role": "user", "content": optimized}], provider)
             responses = [worker_response]
+            log_activity(f"Travailleur 1 ({w_mod}) a terminé.")
         else:
             w_tasks = []
             for w in workers:
                 w_client, w_prov = get_client_for_model(db, w, settings)
                 w_tasks.append(ask_agent(w_client, w, formatted_history + [{"role": "user", "content": optimized}], w_prov))
             responses = await asyncio.gather(*w_tasks, return_exceptions=True)
+            log_activity("Tous les travailleurs ont terminé leur analyse.")
 
         c_mod = config.get("concatenator")
         if not c_mod: c_mod = "gemini-3.5-flash-lite"
         
         c_client, c_prov = get_client_for_model(db, c_mod, settings)
         
+        log_activity(f"Synthèse et traduction finale via Concaténeur ({c_mod})...")
         concat_system = (
             "You are a master lead developer and technical synthesizer. "
             "Synthesize the provided expert responses into a single cohesive response. "
@@ -285,9 +306,12 @@ async def run_orchestrator(db: Session, history: list, settings: SystemSettings,
         
         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)
+        
+        log_activity("Orchestration terminée avec succès.")
 
     except Exception as e:
         final_response = f"L'IA a rencontré une erreur critique: {str(e)}"
+        log_activity(f"ERREUR CRITIQUE: {str(e)}")
     
     await sync_finances(db, settings)
     return final_response

backend/schemas.py

@@ -64,4 +64,10 @@ class MessageResponse(MessageBase):
 
 class PasswordChange(BaseModel):
     old_password: str
-    new_password: str
+    new_password: str
+
+class ModelReplacementRequest(BaseModel):
+    missing_model: str
+
+class LogRequest(BaseModel):
+    message: str