Update app.py

app.py

@@ -3,8 +3,7 @@ import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
-# Initialize Flask app
-app = Flask("SuperKart")
+app = Flask(__name__)
 
 MODEL_PATH = "superkart_model_v1_0.joblib"
 model = None
@@ -14,53 +13,41 @@ def load_model():
     global model
     if model is None:
         if not os.path.exists(MODEL_PATH):
-            raise FileNotFoundError(f"Model not found: {MODEL_PATH}")
+            raise FileNotFoundError(f"Model file not found: {MODEL_PATH}")
         model = joblib.load(MODEL_PATH)
 
 
-# Health check (required by Hugging Face Spaces)
+# Health check (important for deployment)
 @app.route("/", methods=["GET"])
 def health():
     return "SuperKart Backend is running"
 
 
-# Prediction endpoint (MATCHES STREAMLIT)
-@app.route("/predict", methods=["POST"])
+@app.route("/v1/predict", methods=["POST"])
 def predict():
     try:
         load_model()
 
         data = request.get_json(force=True)
 
-        # Convert incoming list-based JSON to DataFrame
-        df = pd.DataFrame(data)
+        sample = {
+            "Product_Weight": data["Product_Weight"],
+            "Product_Sugar_Content": data["Product_Sugar_Content"],
+            "Product_Allocated_Area": data["Product_Allocated_Area"],
+            "Product_MRP": data["Product_MRP"],
+            "Store_Size": data["Store_Size"],
+            "Store_Location_City_Type": data["Store_Location_City_Type"],
+            "Store_Type": data["Store_Type"],
+            "Product_Id_char": data["Product_Id_char"],
+            "Store_Age_Years": data["Store_Age_Years"],
+            "Product_Type_Category": data["Product_Type_Category"],
+        }
 
-        # -------------------------------
-        # FEATURE ENGINEERING (IMPORTANT)
-        # -------------------------------
+        query_df = pd.DataFrame([sample])
 
-        # Store_Age_Years = 2025 - Store_Establishment_Year
-        if "Store_Establishment_Year" in df.columns:
-            df["Store_Age_Years"] = 2025 - df["Store_Establishment_Year"]
-            df.drop(columns=["Store_Establishment_Year"], inplace=True)
+        prediction = model.predict(query_df)[0]
 
-        # Product_Id_char (dummy but required by model)
-        if "Product_Id_char" not in df.columns:
-            df["Product_Id_char"] = "A"
-
-        # Product_Type_Category (same as Product_Type)
-        if "Product_Type" in df.columns:
-            df["Product_Type_Category"] = df["Product_Type"]
-            df.drop(columns=["Product_Type"], inplace=True)
-
-        # -------------------------------
-        # PREDICTION
-        # -------------------------------
-        prediction = model.predict(df)
-
-        return jsonify({
-            "predictions": prediction.tolist()
-        })
+        return jsonify({"predictions": float(prediction)})
 
     except KeyError as e:
         return jsonify({"error": f"Missing field: {str(e)}"}), 400
@@ -68,6 +55,5 @@ def predict():
         return jsonify({"error": str(e)}), 500
 
 
-# Run on Hugging Face required port
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)