Delete app.py

app.py

@@ -1,69 +0,0 @@
-import os
-import tempfile
-import uvicorn
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from fastapi.responses import JSONResponse
-
-# Import your prediction functions
-from prediction import predict_eeg_recording, predict_ensemble_eeg_recording
-
-app = FastAPI(title="EEG Epilepsy Prediction API")
-
-@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"])
-async def predict_endpoint(
-    file: UploadFile = File(...),
-    model_choice: str = "2DCNN",
-    ensemble_method: str = None
-):
-    """
-    
-    Query parameters:
-      - model_choice: Choose one model among "2DCNN", "EEGNet", "EpilepsyNet", or "ensemble".
-      - ensemble_method: (Optional, required if model_choice is "ensemble") 
-                           The ensemble method to use ("average" or "voting").
-
-    """
-    print("Saving uploaded file as temporary file...")
-    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")
-    
-    print("Performing prediction using model_choice =", model_choice)
-    try:
-        if model_choice.lower() == "ensemble":
-            if ensemble_method is None:
-                raise HTTPException(status_code=400, detail="ensemble_method must be specified when using ensemble model_choice")
-            pred_label, mean_prob = predict_ensemble_eeg_recording(tmp_path, ensemble_method=ensemble_method, threshold=0.5)
-        else:
-            pred_label, mean_prob = predict_eeg_recording(tmp_path, model_name=model_choice, threshold=0.5)
-    except Exception as e:
-        os.remove(tmp_path)
-        raise HTTPException(status_code=400, detail=f"Prediction failed: {e}")
-    
-    os.remove(tmp_path)
-    
-    
-    response = {
-        "prediction": "epilepsy" if pred_label == 1 else "no epilepsy",
-        "confidence": mean_prob
-    }
-    print("Prediction complete, returning response...")
-    return JSONResponse(content=response)
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)