Upload app.py with huggingface_hub

app.py

@@ -6,6 +6,7 @@ import numpy as np
 import pickle
 from huggingface_hub import hf_hub_download
 import os
+from typing import List, Union
 
 app = FastAPI(title="Headache Predictor API")
 
@@ -33,21 +34,43 @@ async def load_model():
         import traceback
         traceback.print_exc()
 
-class PredictionRequest(BaseModel):
-    features: list[float]
+class SinglePredictionRequest(BaseModel):
+    features: List[float]
 
-class PredictionResponse(BaseModel):
+class BatchPredictionRequest(BaseModel):
+    instances: List[List[float]]
+
+class DayPrediction(BaseModel):
+    day: int
+    prediction: int
+    probability: float
+
+class SinglePredictionResponse(BaseModel):
     prediction: int
     probability: float
 
+class BatchPredictionResponse(BaseModel):
+    predictions: List[DayPrediction]
+
 @app.get("/")
 def read_root():
     return {
         "message": "Headache Predictor API",
         "status": "running",
         "endpoints": {
-            "predict": "/predict",
+            "predict": "/predict - Single day prediction",
+            "predict_batch": "/predict/batch - 7-day forecast",
             "health": "/health"
+        },
+        "examples": {
+            "single": {
+                "url": "/predict",
+                "body": {"features": [1, 0, 0, 0, 1, 0, 1005.0, -9.5, 85.0, 15.5, 64.0, 5.5, 41.0, 0.0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 10, 40, 4, 7.0, 50.0, 60.0, 3.5, 1.5, 6.8]}
+            },
+            "batch": {
+                "url": "/predict/batch",
+                "body": {"instances": [["array of 37 features for day 1"], ["array for day 2"], "..."]}
+            }
         }
     }
 
@@ -58,8 +81,9 @@ def health_check():
         "model_loaded": model is not None
     }
 
-@app.post("/predict", response_model=PredictionResponse)
-def predict(request: PredictionRequest):
+@app.post("/predict", response_model=SinglePredictionResponse)
+def predict(request: SinglePredictionRequest):
+    """Predict headache risk for a single day"""
     if model is None:
         raise HTTPException(status_code=503, detail="Model not loaded")
 
@@ -71,9 +95,40 @@ def predict(request: PredictionRequest):
         prediction = model.predict(features)[0]
         probability = float(model.predict_proba(features)[0][int(prediction)])
 
-        return PredictionResponse(
+        return SinglePredictionResponse(
             prediction=int(prediction),
             probability=probability
         )
     except Exception as e:
         raise HTTPException(status_code=400, detail=f"Prediction error: {str(e)}")
+
+@app.post("/predict/batch", response_model=BatchPredictionResponse)
+def predict_batch(request: BatchPredictionRequest):
+    """Predict headache risk for multiple days (7-day forecast)"""
+    if model is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+
+    try:
+        # Convert all instances to numpy array
+        features = np.array(request.instances)
+
+        if features.ndim != 2:
+            raise ValueError(f"Expected 2D array, got shape {features.shape}")
+
+        # Make predictions for all days
+        predictions = model.predict(features)
+        probabilities = model.predict_proba(features)
+
+        # Format results
+        results = []
+        for i, (pred, prob_array) in enumerate(zip(predictions, probabilities), 1):
+            results.append(DayPrediction(
+                day=i,
+                prediction=int(pred),
+                probability=float(prob_array[int(pred)])
+            ))
+
+        return BatchPredictionResponse(predictions=results)
+
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Batch prediction error: {str(e)}")