Upload app.py with huggingface_hub

app.py

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+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import xgboost as xgb
+import numpy as np
+import pickle
+from huggingface_hub import hf_hub_download
+
+app = FastAPI(title="Headache Predictor API")
+
+# Load model at startup
+model = None
+
+@app.on_event("startup")
+async def load_model():
+    global model
+    try:
+        model_path = hf_hub_download(
+            repo_id="emp-admin/headache-predictor-xgboost",
+            filename="model.pkl"
+        )
+        with open(model_path, 'rb') as f:
+            model = pickle.load(f)
+        print("✅ Model loaded successfully")
+    except Exception as e:
+        print(f"❌ Error loading model: {e}")
+
+class PredictionRequest(BaseModel):
+    features: list[float]
+
+class PredictionResponse(BaseModel):
+    prediction: int
+    probability: float
+
+@app.get("/")
+def read_root():
+    return {
+        "message": "Headache Predictor API",
+        "status": "running",
+        "endpoints": {
+            "predict": "/predict",
+            "health": "/health"
+        }
+    }
+
+@app.get("/health")
+def health_check():
+    return {
+        "status": "healthy",
+        "model_loaded": model is not None
+    }
+
+@app.post("/predict", response_model=PredictionResponse)
+def predict(request: PredictionRequest):
+    if model is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+
+    try:
+        # Convert input to numpy array
+        features = np.array(request.features).reshape(1, -1)
+
+        # Make prediction
+        prediction = model.predict(features)[0]
+        probability = float(model.predict_proba(features)[0][int(prediction)])
+
+        return PredictionResponse(
+            prediction=int(prediction),
+            probability=probability
+        )
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Prediction error: {str(e)}")