Update app.py

app.py

@@ -14,7 +14,7 @@ model_path = hf_hub_download(
 )
 model = joblib.load(model_path)
 
-# Define schema
+# Define schema for input
 class CanteenInput(BaseModel):
     canteen_id: str
     canteen_name: str
@@ -38,16 +38,31 @@ def predict_surplus(request: RequestBody):
     # One-hot encode categorical columns
     df_encoded = pd.get_dummies(df, columns=["canteen_id", "canteen_name"])
 
-    # Align columns with model’s expected input
-    model_features = model.feature_names_ if hasattr(model, "feature_names_") else None
+    # Align columns with model features
+    model_features = getattr(model, "feature_names_", None)
     if model_features:
         for col in model_features:
             if col not in df_encoded.columns:
                 df_encoded[col] = 0
         df_encoded = df_encoded[model_features]
 
-    # Run prediction
+    # Predict
     predictions = model.predict(df_encoded)
     df["predicted_surplus"] = predictions
 
     return df.to_dict(orient="records")
+
+@app.get("/fetch_data")
+def fetch_data(date: str):
+    """
+    Temporary endpoint so your frontend doesn't explode.
+    Replace this with an actual DB lookup later if you want real data.
+    """
+    # You can later connect this to your stored predictions or history table.
+    sample_response = {
+        "date": date,
+        "canteen_id": "C002",
+        "canteen_name": "Anna University Mess",
+        "predicted_surplus": 24.0
+    }
+    return sample_response