🚀 Deploy from GitHub Actions - 2026-02-03 09:56:04

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements
+COPY requirements.txt .
+
+# Install Python packages
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy app
+COPY app.py .
+
+# Expose port
+EXPOSE 7860
+
+# Run FastAPI
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: Wakee Api
-emoji: 🦀
-colorFrom: pink
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+"""
+Wakee Reloaded - API FastAPI
+Logique : /predict ne sauvegarde RIEN, /insert fait tout
+"""
+
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from typing import List, Optional
+from huggingface_hub import hf_hub_download
+import onnxruntime as ort
+from PIL import Image
+import io
+import numpy as np
+from datetime import datetime
+import base64
+import os
+
+from sqlalchemy import create_engine, text
+from sqlalchemy.exc import SQLAlchemyError
+import boto3
+from botocore.exceptions import ClientError
+from torchvision import transforms
+
+# ============================================================================
+# CONFIGURATION
+# ============================================================================
+
+HF_MODEL_REPO = "Terorra/wakee-reloaded"
+MODEL_FILENAME = "model.onnx"
+
+NEON_DATABASE_URL = os.getenv("NEON_DATABASE_URL")
+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")
+
+# ============================================================================
+# 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)
+    frustration: float = Field(..., ge=0, le=3)
+    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]
+    statistics: dict
+
+# ============================================================================
+# FASTAPI APP
+# ============================================================================
+
+app = FastAPI(
+    title="Wakee Emotion API",
+    description="Multi-label emotion detection API",
+    version="1.0.0",
+    docs_url="/docs",
+    redoc_url="/redoc"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ============================================================================
+# GLOBAL VARIABLES
+# ============================================================================
+
+onnx_session = None
+db_engine = None
+s3_client = None
+transform = None
+
+# ============================================================================
+# STARTUP
+# ============================================================================
+
+@app.on_event("startup")
+async def startup_event():
+    global onnx_session, db_engine, s3_client, transform
+    
+    print("=" * 70)
+    print("🚀 DÉMARRAGE API WAKEE")
+    print("=" * 70)
+    
+    # 1. Download model from HF Model Hub
+    try:
+        print(f"\n📥 Téléchargement du modèle...")
+        print(f"   Repo : {HF_MODEL_REPO}")
+        print(f"   File : {MODEL_FILENAME}")
+        
+        model_path = hf_hub_download(
+            repo_id=HF_MODEL_REPO,
+            filename=MODEL_FILENAME,
+            cache_dir="/tmp/models"
+        )
+        
+        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"   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
+    if NEON_DATABASE_URL:
+        try:
+            db_engine = create_engine(NEON_DATABASE_URL)
+            with db_engine.connect() as conn:
+                conn.execute(text("SELECT 1"))
+            print("✅ Connexion NeonDB établie\n")
+        except Exception as e:
+            print(f"⚠️  NeonDB non disponible : {e}\n")
+            db_engine = None
+    else:
+        print("⚠️  NEON_DATABASE_URL non défini\n")
+    
+    # 4. Cloudflare R2
+    if all([R2_ACCOUNT_ID, R2_ACCESS_KEY_ID, R2_SECRET_ACCESS_KEY]):
+        try:
+            s3_client = boto3.client(
+                's3',
+                endpoint_url=f'https://{R2_ACCOUNT_ID}.r2.cloudflarestorage.com',
+                aws_access_key_id=R2_ACCESS_KEY_ID,
+                aws_secret_access_key=R2_SECRET_ACCESS_KEY,
+                region_name='auto'
+            )
+            s3_client.head_bucket(Bucket=R2_BUCKET_NAME)
+            print(f"✅ Connexion Cloudflare R2 (bucket: {R2_BUCKET_NAME})\n")
+        except Exception as e:
+            print(f"⚠️  Cloudflare R2 non disponible : {e}\n")
+            s3_client = None
+    else:
+        print("⚠️  R2 secrets non définis\n")
+    
+    print("=" * 70)
+    print("🎉 API WAKEE PRÊTE !")
+    print("=" * 70)
+    print(f"📊 Status :")
+    print(f"   - Modèle : {'✅' 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
+# ============================================================================
+
+@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"
+    }
+
+@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,
+        "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"
+        )
+    
+    if not file.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File must be an image")
+    
+    try:
+        # 1. Load image
+        image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        
+        # 2. Preprocessing
+        input_tensor = preprocess_image(image)
+        
+        # 3. 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),
+            engagement=round(float(scores_array[2]), 2),
+            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
+# ============================================================================
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+# Wakee API Requirements (Python 3.11)
+
+# FastAPI
+fastapi==0.109.0
+uvicorn[standard]==0.27.0
+python-multipart==0.0.6
+
+# HuggingFace
+huggingface-hub==0.20.3
+
+# ML
+onnxruntime==1.16.3
+torch==2.1.2
+torchvision==0.16.2
+Pillow==10.2.0
+numpy==1.26.3
+
+# Database
+sqlalchemy==2.0.25
+psycopg2-binary==2.9.9
+
+# Cloud Storage
+boto3==1.34.34