updated app.py

app.py

@@ -1,24 +1,37 @@
-from fastapi import FastAPI, Request
-import joblib
-
-app = FastAPI(title="Food Surplus Predictor")
-
-# Load model from Hugging Face model repo
-model = joblib.load("https://huggingface.co/BeeBasic/food-for-all/resolve/main/best_model.joblib")
-
-@app.post("/predict")
-async def predict(request: Request):
-    data = await request.json()
-
-    canteens = data.get("canteens", [])
-    ngos = data.get("ngos", [])
-
-    total_surplus = sum(item["surplus"] for item in canteens)
-    total_need = sum(item["requirement"] for item in ngos)
-    balance = total_surplus - total_need
-
-    return {
-        "total_surplus": total_surplus,
-        "total_need": total_need,
-        "balance": balance
-    }
+from fastapi import FastAPI
+from pydantic import BaseModel
+from huggingface_hub import hf_hub_download
+import joblib
+import pandas as pd
+
+app = FastAPI(title="Food Surplus Predictor API")
+
+# Download model from your Hugging Face model repo
+model_path = hf_hub_download(
+    repo_id="BeeBasic/food-for-all",
+    filename="best_model.joblib",
+    repo_type="model"
+)
+model = joblib.load(model_path)
+
+# Define input data schema
+class CanteenInput(BaseModel):
+    canteen_id: int
+    day: int
+    month: int
+    year: int
+    day_of_week: int
+
+class RequestBody(BaseModel):
+    data: list[CanteenInput]
+
+@app.get("/")
+def home():
+    return {"message": "Food Surplus Prediction API is running!"}
+
+@app.post("/predict")
+def predict_surplus(request: RequestBody):
+    df = pd.DataFrame([canteen.dict() for canteen in request.data])
+    predictions = model.predict(df)
+    df["predicted_surplus"] = predictions
+    return df.to_dict(orient="records")