Update app.py

app.py

@@ -4,17 +4,18 @@ from huggingface_hub import hf_hub_download
 import joblib
 import pandas as pd
 
-app = FastAPI(title="Food Surplus Predictor API")
+# Initialize the app
+app = FastAPI(title="Food Surplus Prediction API")
 
-# Download model from your Hugging Face model repo
+# Download the trained model from your Hugging Face model repository
 model_path = hf_hub_download(
-    repo_id="BeeBasic/food-for-all",
-    filename="best_model.joblib",
+    repo_id="BeeBasic/food-for-all",   # your model repo
+    filename="best_model.joblib",      # model file name
     repo_type="model"
 )
 model = joblib.load(model_path)
 
-# Define input data schema
+# Input schema for each record
 class CanteenInput(BaseModel):
     canteen_id: int
     day: int
@@ -22,16 +23,29 @@ class CanteenInput(BaseModel):
     year: int
     day_of_week: int
 
+# Request body schema
 class RequestBody(BaseModel):
     data: list[CanteenInput]
 
+# Root route for quick health check
 @app.get("/")
 def home():
     return {"message": "Food Surplus Prediction API is running!"}
 
+# Prediction route
 @app.post("/predict")
 def predict_surplus(request: RequestBody):
+    # Convert request data into DataFrame
     df = pd.DataFrame([canteen.dict() for canteen in request.data])
+
+    # Reorder columns to match the order used during model training
+    df = df[['day', 'month', 'year', 'day_of_week', 'canteen_id']]
+
+    # Make predictions
     predictions = model.predict(df)
+
+    # Append results to DataFrame
     df["predicted_surplus"] = predictions
+
+    # Return as JSON
     return df.to_dict(orient="records")