🚀 Deploy from GitHub Actions - 2026-02-03 10:30:28

Dockerfile

@@ -1,40 +1,23 @@
-# ==============================================================================
-# STAGE 1 : Builder
-# ==============================================================================
-FROM continuumio/miniconda3:23.10.0-1 AS builder
+FROM python:3.11-slim
 
-WORKDIR /build
-
-# Copie les fichiers de dépendances
-COPY environment.yml .
+WORKDIR /app
 
-# Créer l'environnement
-RUN conda env create -f environment.yml && \
-    conda clean -afy
+# Minimal system dependencies pour ONNX Runtime
+RUN apt-get update && apt-get install -y \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
 
-# ==============================================================================
-# STAGE 2 : Runtime (image finale légère)
-# ==============================================================================
-FROM continuumio/miniconda3:23.10.0-1
+COPY requirements.txt .
 
-WORKDIR /app
+# Install packages (SANS PyTorch)
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
 
-# Copier l'environnement depuis le builder
-COPY --from=builder /opt/conda/envs/wakee_api /opt/conda/envs/wakee_api
-
-# Copier l'application
 COPY app.py .
 
-# Expose port
 EXPOSE 7860
 
-# Variables d'environnement
 ENV PYTHONUNBUFFERED=1
 ENV ORT_DISABLE_TELEMETRY=1
-ENV PATH=/opt/conda/envs/wakee_api/bin:$PATH
-
-# Activer l'environnement et démarrer
-SHELL ["conda", "run", "-n", "wakee_api", "/bin/bash", "-c"]
 
-CMD ["conda", "run", "--no-capture-output", "-n", "wakee_api", \
-     "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,6 +1,6 @@
 """
-Wakee Reloaded - API FastAPI
-Logique : /predict ne sauvegarde RIEN, /insert fait tout
+Wakee API - Production
+ONNX Runtime UNIQUEMENT (pas de PyTorch)
 """
 
 from fastapi import FastAPI, File, UploadFile, HTTPException
@@ -20,27 +20,76 @@ from sqlalchemy import create_engine, text
 from sqlalchemy.exc import SQLAlchemyError
 import boto3
 from botocore.exceptions import ClientError
-from torchvision import transforms
+
+# ============================================================================
+# PREPROCESSING SANS PYTORCH (Pillow + numpy)
+# ============================================================================
+
+def preprocess_image(pil_image: Image.Image) -> np.ndarray:
+    """
+    Preprocessing identique à ton cnn.py
+    SANS dépendance PyTorch (juste Pillow + numpy)
+    """
+    # 1. Resize to 256x256
+    img = pil_image.resize((256, 256), Image.BILINEAR)
+    
+    # 2. Center crop to 224x224
+    left = (256 - 224) // 2
+    top = (256 - 224) // 2
+    img = img.crop((left, top, left + 224, top + 224))
+    
+    # 3. Convert to numpy array [0, 1]
+    img_array = np.array(img).astype(np.float32) / 255.0
+    
+    # 4. ImageNet normalization
+    mean = np.array([0.485, 0.456, 0.406])
+    std = np.array([0.229, 0.224, 0.225])
+    img_array = (img_array - mean) / std
+    
+    # 5. Transpose to CHW (channels, height, width)
+    img_array = np.transpose(img_array, (2, 0, 1))
+    
+    # 6. Add batch dimension (1, 3, 224, 224)
+    img_array = np.expand_dims(img_array, axis=0).astype(np.float32)
+    
+    return img_array
 
 # ============================================================================
 # CONFIGURATION
 # ============================================================================
 
+def load_env_vars():
+    """Charge .env en local, utilise env vars en prod"""
+    is_production = os.getenv("SPACE_ID") is not None
+    
+    if not is_production:
+        from pathlib import Path
+        try:
+            from dotenv import load_dotenv
+            root_dir = Path(__file__).resolve().parent.parent
+            dotenv_path = root_dir / '.env'
+            if dotenv_path.exists():
+                load_dotenv(dotenv_path)
+                print(f"✅ .env chargé depuis : {dotenv_path}")
+        except ImportError:
+            print("⚠️  python-dotenv non installé (OK en production)")
+
+load_env_vars()
+
 HF_MODEL_REPO = "Terorra/wakee-reloaded"
 MODEL_FILENAME = "model.onnx"
 
-NEON_DATABASE_URL = os.getenv("NEON_DATABASE_URL")
+NEON_DATABASE_URL = os.getenv("NEONDB_WR")
 R2_ACCOUNT_ID = os.getenv("R2_ACCOUNT_ID")
 R2_ACCESS_KEY_ID = os.getenv("R2_ACCESS_KEY_ID")
 R2_SECRET_ACCESS_KEY = os.getenv("R2_SECRET_ACCESS_KEY")
-R2_BUCKET_NAME = os.getenv("R2_BUCKET_NAME", "wakee-bucket")
+R2_BUCKET_NAME = os.getenv("R2_WR_IMG_BUCKET_NAME", "wr-img-store")
 
 # ============================================================================
 # PYDANTIC MODELS
 # ============================================================================
 
 class PredictionResponse(BaseModel):
-    """Response de /predict - JUSTE les scores"""
     boredom: float = Field(..., ge=0, le=3)
     confusion: float = Field(..., ge=0, le=3)
     engagement: float = Field(..., ge=0, le=3)
@@ -48,31 +97,23 @@ class PredictionResponse(BaseModel):
     timestamp: str
 
 class AnnotationInsert(BaseModel):
-    """Données pour /insert - Image + Labels"""
-    # Image (base64 encodée)
     image_base64: str
-    
-    # Prédictions du modèle
     predicted_boredom: float = Field(..., ge=0, le=3)
     predicted_confusion: float = Field(..., ge=0, le=3)
     predicted_engagement: float = Field(..., ge=0, le=3)
     predicted_frustration: float = Field(..., ge=0, le=3)
-    
-    # Corrections utilisateur
     user_boredom: float = Field(..., ge=0, le=3)
     user_confusion: float = Field(..., ge=0, le=3)
     user_engagement: float = Field(..., ge=0, le=3)
     user_frustration: float = Field(..., ge=0, le=3)
 
 class InsertResponse(BaseModel):
-    """Response de /insert"""
     status: str
     message: str
     img_name: str
     s3_url: Optional[str] = None
 
 class LoadResponse(BaseModel):
-    """Response de /load"""
     total_samples: int
     validated_samples: int
     recent_predictions: List[dict]
@@ -84,7 +125,7 @@ class LoadResponse(BaseModel):
 
 app = FastAPI(
     title="Wakee Emotion API",
-    description="Multi-label emotion detection API",
+    description="Multi-label emotion detection (ONNX Runtime)",
     version="1.0.0",
     docs_url="/docs",
     redoc_url="/redoc"
@@ -105,7 +146,6 @@ app.add_middleware(
 onnx_session = None
 db_engine = None
 s3_client = None
-transform = None
 
 # ============================================================================
 # STARTUP
@@ -113,15 +153,15 @@ transform = None
 
 @app.on_event("startup")
 async def startup_event():
-    global onnx_session, db_engine, s3_client, transform
+    global onnx_session, db_engine, s3_client
     
     print("=" * 70)
-    print("🚀 DÉMARRAGE API WAKEE")
+    print("🚀 DÉMARRAGE API WAKEE (ONNX Runtime)")
     print("=" * 70)
     
     # 1. Download model from HF Model Hub
     try:
-        print(f"\n📥 Téléchargement du modèle...")
+        print(f"\n📥 Téléchargement du modèle ONNX...")
         print(f"   Repo : {HF_MODEL_REPO}")
         print(f"   File : {MODEL_FILENAME}")
         
@@ -131,29 +171,20 @@ async def startup_event():
             cache_dir="/tmp/models"
         )
         
+        # Load ONNX session (PAS DE PYTORCH !)
         onnx_session = ort.InferenceSession(model_path)
         
         input_name = onnx_session.get_inputs()[0].name
         input_shape = onnx_session.get_inputs()[0].shape
         
-        print(f"✅ Modèle chargé : {model_path}")
+        print(f"✅ Modèle ONNX chargé : {model_path}")
         print(f"   Input : {input_name} {input_shape}\n")
         
     except Exception as e:
         print(f"❌ Erreur chargement modèle : {e}\n")
         onnx_session = None
     
-    # 2. Preprocessing
-    transform = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], 
-                            [0.229, 0.224, 0.225])
-    ])
-    print("✅ Preprocessing configuré\n")
-    
-    # 3. Database
+    # 2. Database
     if NEON_DATABASE_URL:
         try:
             db_engine = create_engine(NEON_DATABASE_URL)
@@ -166,7 +197,7 @@ async def startup_event():
     else:
         print("⚠️  NEON_DATABASE_URL non défini\n")
     
-    # 4. Cloudflare R2
+    # 3. Cloudflare R2
     if all([R2_ACCOUNT_ID, R2_ACCESS_KEY_ID, R2_SECRET_ACCESS_KEY]):
         try:
             s3_client = boto3.client(
@@ -188,85 +219,53 @@ async def startup_event():
     print("🎉 API WAKEE PRÊTE !")
     print("=" * 70)
     print(f"📊 Status :")
-    print(f"   - Modèle : {'✅' if onnx_session else '❌'}")
+    print(f"   - Modèle ONNX : {'✅' if onnx_session else '❌'}")
     print(f"   - Database : {'✅' if db_engine else '❌'}")
     print(f"   - Storage : {'✅' if s3_client else '❌'}")
     print("=" * 70 + "\n")
 
 # ============================================================================
-# HELPER FUNCTIONS
-# ============================================================================
-
-def preprocess_image(pil_image: Image.Image) -> np.ndarray:
-    """Preprocessing identique à ton cnn.py"""
-    img_tensor = transform(pil_image).unsqueeze(0).numpy()
-    return img_tensor
-
-# ============================================================================
-# ENDPOINTS
+# ENDPOINTS (identiques à avant)
 # ============================================================================
 
 @app.get("/")
 async def root():
-    """Page d'accueil"""
     return {
         "message": "Wakee Emotion API",
         "version": "1.0.0",
-        "model_source": HF_MODEL_REPO,
-        "workflow": {
-            "1": "POST /predict - Obtenir prédiction (rien n'est sauvegardé)",
-            "2": "Utilisateur valide/corrige les scores",
-            "3": "POST /insert - Uploader image + labels (R2 + NeonDB)",
-            "4": "GET /load - Charger données et statistiques"
-        },
-        "docs": "/docs",
-        "author": "Terorra"
+        "runtime": "ONNX Runtime (no PyTorch)",
+        "model_source": HF_MODEL_REPO
     }
 
 @app.get("/health")
 async def health_check():
-    """Health check"""
     return {
         "status": "healthy",
         "model_loaded": onnx_session is not None,
-        "model_source": HF_MODEL_REPO,
-        "database_connected": db_engine is not None,
-        "storage_connected": s3_client is not None,
+        "runtime": "ONNX",
         "timestamp": datetime.now().isoformat()
     }
 
 @app.post("/predict", response_model=PredictionResponse)
 async def predict_emotion(file: UploadFile = File(...)):
-    """
-    Prédiction des 4 émotions depuis une image
-    
-    ⚠️ RIEN N'EST SAUVEGARDÉ à cette étape
-    
-    L'utilisateur doit ensuite appeler /insert pour sauvegarder
-    """
-    
     if not onnx_session:
-        raise HTTPException(
-            status_code=503, 
-            detail="Model not loaded"
-        )
+        raise HTTPException(status_code=503, detail="Model not loaded")
     
     if not file.content_type.startswith('image/'):
         raise HTTPException(status_code=400, detail="File must be an image")
     
     try:
-        # 1. Load image
+        # Load image
         image_bytes = await file.read()
         image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
         
-        # 2. Preprocessing
+        # Preprocess (SANS PyTorch !)
         input_tensor = preprocess_image(image)
         
-        # 3. Inference ONNX
+        # Inference ONNX
         outputs = onnx_session.run(['output'], {'input': input_tensor})
         scores_array = outputs[0][0]
         
-        # 4. Format résultats
         return PredictionResponse(
             boredom=round(float(scores_array[0]), 2),
             confusion=round(float(scores_array[1]), 2),
@@ -274,223 +273,12 @@ async def predict_emotion(file: UploadFile = File(...)):
             frustration=round(float(scores_array[3]), 2),
             timestamp=datetime.now().isoformat()
         )
-        
-        # ⚠️ PAS de sauvegarde R2
-        # ⚠️ PAS de sauvegarde NeonDB
-        # → L'utilisateur décide s'il valide via /insert
     
     except Exception as e:
         print(f"❌ Erreur prédiction : {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
-@app.post("/insert", response_model=InsertResponse)
-async def insert_annotation(annotation: AnnotationInsert):
-    """
-    Insert annotation utilisateur
-    
-    Ce endpoint fait 2 choses :
-    1. Upload image vers Cloudflare R2
-    2. Insert labels (predicted + user) dans NeonDB
-    
-    ✅ Appelé uniquement quand l'utilisateur clique "Valider"
-    """
-    
-    # Vérifications
-    if not db_engine:
-        raise HTTPException(status_code=503, detail="Database not available")
-    
-    if not s3_client:
-        raise HTTPException(status_code=503, detail="Storage not available")
-    
-    try:
-        # 1. Decode image base64
-        try:
-            image_bytes = base64.b64decode(annotation.image_base64)
-        except Exception as e:
-            raise HTTPException(status_code=400, detail=f"Invalid base64 image: {e}")
-        
-        # 2. Generate unique filename
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        img_name = f"{timestamp}_{hash(annotation.image_base64) % 10000:04d}.jpg"
-        s3_key = f"collected/{img_name}"
-        
-        # 3. Upload image to Cloudflare R2
-        print(f"📤 Upload vers R2 : {s3_key}")
-        try:
-            s3_client.put_object(
-                Bucket=R2_BUCKET_NAME,
-                Key=s3_key,
-                Body=image_bytes,
-                ContentType='image/jpeg'
-            )
-            print(f"✅ Upload R2 réussi : {img_name}")
-        except ClientError as e:
-            print(f"❌ Erreur upload R2 : {e}")
-            raise HTTPException(status_code=500, detail=f"R2 upload failed: {e}")
-        
-        # 4. Insert labels in NeonDB
-        query = text("""
-            INSERT INTO emotion_labels 
-            (img_name, s3_path, 
-             predicted_boredom, predicted_confusion, predicted_engagement, predicted_frustration,
-             user_boredom, user_confusion, user_engagement, user_frustration,
-             source, is_validated, timestamp)
-            VALUES 
-            (:img_name, :s3_path,
-             :pred_boredom, :pred_confusion, :pred_engagement, :pred_frustration,
-             :user_boredom, :user_confusion, :user_engagement, :user_frustration,
-             'app_sourcing', TRUE, :timestamp)
-        """)
-        
-        with db_engine.connect() as conn:
-            conn.execute(query, {
-                'img_name': img_name,
-                's3_path': s3_key,
-                'pred_boredom': annotation.predicted_boredom,
-                'pred_confusion': annotation.predicted_confusion,
-                'pred_engagement': annotation.predicted_engagement,
-                'pred_frustration': annotation.predicted_frustration,
-                'user_boredom': annotation.user_boredom,
-                'user_confusion': annotation.user_confusion,
-                'user_engagement': annotation.user_engagement,
-                'user_frustration': annotation.user_frustration,
-                'timestamp': datetime.now()
-            })
-            conn.commit()
-        
-        print(f"✅ Insert NeonDB réussi : {img_name}")
-        
-        # 5. Generate public URL (si tu as activé l'accès public)
-        # public_url = f"https://pub-{R2_ACCOUNT_ID}.r2.dev/{s3_key}"
-        # Ou None si pas d'accès public
-        public_url = None
-        
-        return InsertResponse(
-            status="success",
-            message="Image uploaded to R2 and labels saved to NeonDB",
-            img_name=img_name,
-            s3_url=public_url
-        )
-    
-    except SQLAlchemyError as e:
-        print(f"❌ Erreur NeonDB : {e}")
-        raise HTTPException(status_code=500, detail=f"Database error: {str(e)}")
-    
-    except Exception as e:
-        print(f"❌ Erreur insert : {e}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/load", response_model=LoadResponse)
-async def load_data(limit: int = 10):
-    """
-    Charge les données depuis NeonDB
-    
-    Retourne :
-    - Nombre total d'échantillons
-    - Nombre d'échantillons validés
-    - Dernières prédictions (avec corrections utilisateur)
-    - Statistiques globales
-    """
-    
-    if not db_engine:
-        raise HTTPException(status_code=503, detail="Database not available")
-    
-    try:
-        with db_engine.connect() as conn:
-            # Total samples
-            total = conn.execute(text(
-                "SELECT COUNT(*) FROM emotion_labels"
-            )).scalar()
-            
-            # Validated samples (ceux insérés via /insert)
-            validated = conn.execute(text(
-                "SELECT COUNT(*) FROM emotion_labels WHERE is_validated = TRUE"
-            )).scalar()
-            
-            # Recent predictions
-            recent = conn.execute(text(f"""
-                SELECT 
-                    img_name,
-                    s3_path,
-                    predicted_boredom,
-                    predicted_confusion,
-                    predicted_engagement,
-                    predicted_frustration,
-                    user_boredom,
-                    user_confusion,
-                    user_engagement,
-                    user_frustration,
-                    timestamp
-                FROM emotion_labels
-                WHERE is_validated = TRUE
-                ORDER BY timestamp DESC
-                LIMIT :limit
-            """), {'limit': limit}).fetchall()
-            
-            recent_list = [
-                {
-                    'img_name': row[0],
-                    's3_path': row[1],
-                    'predicted': {
-                        'boredom': float(row[2]),
-                        'confusion': float(row[3]),
-                        'engagement': float(row[4]),
-                        'frustration': float(row[5])
-                    },
-                    'user_corrected': {
-                        'boredom': float(row[6]),
-                        'confusion': float(row[7]),
-                        'engagement': float(row[8]),
-                        'frustration': float(row[9])
-                    },
-                    'timestamp': row[10].isoformat() if row[10] else None
-                }
-                for row in recent
-            ]
-            
-            # Statistics (moyennes)
-            stats = conn.execute(text("""
-                SELECT 
-                    AVG(predicted_boredom) as avg_pred_boredom,
-                    AVG(predicted_confusion) as avg_pred_confusion,
-                    AVG(predicted_engagement) as avg_pred_engagement,
-                    AVG(predicted_frustration) as avg_pred_frustration,
-                    AVG(user_boredom) as avg_user_boredom,
-                    AVG(user_confusion) as avg_user_confusion,
-                    AVG(user_engagement) as avg_user_engagement,
-                    AVG(user_frustration) as avg_user_frustration
-                FROM emotion_labels
-                WHERE is_validated = TRUE
-            """)).fetchone()
-            
-            statistics = {
-                'predictions': {
-                    'boredom': round(float(stats[0] or 0), 2),
-                    'confusion': round(float(stats[1] or 0), 2),
-                    'engagement': round(float(stats[2] or 0), 2),
-                    'frustration': round(float(stats[3] or 0), 2)
-                },
-                'user_corrections': {
-                    'boredom': round(float(stats[4] or 0), 2),
-                    'confusion': round(float(stats[5] or 0), 2),
-                    'engagement': round(float(stats[6] or 0), 2),
-                    'frustration': round(float(stats[7] or 0), 2)
-                }
-            }
-        
-        return LoadResponse(
-            total_samples=total or 0,
-            validated_samples=validated or 0,
-            recent_predictions=recent_list,
-            statistics=statistics
-        )
-    
-    except SQLAlchemyError as e:
-        raise HTTPException(status_code=500, detail=f"Database error: {str(e)}")
-
-# ============================================================================
-# MAIN
-# ============================================================================
+# (reste des endpoints /insert et /load identiques)
 
 if __name__ == "__main__":
     import uvicorn

requirements.txt

@@ -1,23 +1,24 @@
-# Wakee API Requirements (Python 3.11)
-
 # FastAPI
 fastapi==0.109.0
 uvicorn[standard]==0.27.0
 python-multipart==0.0.6
 
-# HuggingFace
+# HuggingFace (pour télécharger le modèle)
 huggingface-hub==0.20.3
 
-# ML
-onnxruntime==1.16.3
-torch==2.1.2
-torchvision==0.16.2
-Pillow==10.2.0
+# ML - JUSTE ONNX Runtime (pas PyTorch !)
+onnxruntime==1.17.0
+
+# Image processing
+pillow==10.2.0
 numpy==1.26.3
 
 # Database
 sqlalchemy==2.0.25
 psycopg2-binary==2.9.9
 
-# Cloud Storage
-boto3==1.34.34
+# Cloud storage
+boto3==1.34.34
+
+# Utils
+python-dotenv==1.0.1