Update app.py

app.py

@@ -1,58 +1,35 @@
+# app.py (API FastAPI)
 import os
 import tempfile
-import uvicorn
 from fastapi import FastAPI, UploadFile, File, HTTPException
 from fastapi.responses import JSONResponse
 import tensorflow as tf
-
-# Import your prediction function from your prediction module.
-from prediction import predict_eeg_recording
+from prediction import predict_eeg_recording  # à adapter selon ton nom de module
 
 app = FastAPI(title="EEG Epilepsy Prediction API")
-
-# Load the trained model once at startup.
 model = tf.keras.models.load_model('model1_2dcnn.h5')
 
-@app.get("/", tags=["Introduction Endpoints"])
-async def index():
-    """
-    Simply returns a welcome message!
-    """
-    message = (
-        "Hello world! Welcome to the EEG Epilepsy Prediction API. "
-        "Submit an EEG recording EDF file to the `/predict` endpoint to receive a prediction."
-    )
-    return message
-
-@app.post("/predict", tags=["Machine Learning"])
+@app.post("/predict")
 async def predict_endpoint(file: UploadFile = File(...)):
-    """
-    Accepts an EEG EDF file, processes it using the preprocessing and spectrogram conversion functions,
-    and returns an aggregated prediction (epilepsy or no epilepsy) along with the mean probability.
-    """
-    # Save the uploaded file temporarily.
     try:
         suffix = os.path.splitext(file.filename)[1]
         with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
             tmp.write(await file.read())
             tmp_path = tmp.name
-    except Exception as e:
-        raise HTTPException(status_code=500, detail="Error saving temporary file")
-    
-    # Use your prediction function to process the file and obtain an aggregated prediction.
+    except Exception:
+        raise HTTPException(status_code=500, detail="Erreur lors de la sauvegarde temporaire")
+
     try:
-        pred_label, mean_prob = predict_eeg_recording(tmp_path, model, threshold=0.5)
+        label, mean_prob, segment_probs = predict_eeg_recording(tmp_path, model, threshold=0.5)
     except Exception as e:
         os.remove(tmp_path)
-        raise HTTPException(status_code=400, detail=f"Prediction failed: {e}")
+        raise HTTPException(status_code=400, detail=f"Erreur prédiction : {e}")
     
     os.remove(tmp_path)
-    
+
     response = {
-        "prediction": "epilepsy" if pred_label == 1 else "no epilepsy",
-        "mean_probability": float(mean_prob)
+        "prediction": "epilepsy" if label == 1 else "no epilepsy",
+        "mean_probability": float(mean_prob),
+        "segment_probabilities": [float(p) for p in segment_probs]
     }
     return JSONResponse(content=response)
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)