Update app.py

app.py

@@ -23,33 +23,28 @@ def health():
     return "SuperKart Backend is running"
 
 
-@app.route("/v1/predict", methods=["POST"])
+@app.route("/predict", methods=["POST"]) # Changed from /v1/predict to match frontend
 def predict():
     try:
         load_model()
-
         data = request.get_json(force=True)
-
+        
+        # Keys must be strings to match the JSON sent by Streamlit
         sample = {
-            "Product_Weight": data[product_weight],
-            "Product_Sugar_Content": data[product_sugar_content],
-            "Product_Allocated_Area": data[product_allocated_area],
-            "Product_Type": data[product_type],
-            "Product_MRP": data[product_mrp],
-            "Store_Establishment_Year": data[store_establishment_year],
-            "Store_Size": data[store_size],
-            "Store_Location_City_Type": data[store_location_city_type],
-            "Store_Type": data[store_type]
+            "Product_Weight": data["Product_Weight"][0],
+            "Product_Sugar_Content": data["Product_Sugar_Content"][0],
+            "Product_Allocated_Area": data["Product_Allocated_Area"][0],
+            "Product_Type": data["Product_Type"][0],
+            "Product_MRP": data["Product_MRP"][0],
+            "Store_Establishment_Year": data["Store_Establishment_Year"][0],
+            "Store_Size": data["Store_Size"][0],
+            "Store_Location_City_Type": data["Store_Location_City_Type"][0],
+            "Store_Type": data["Store_Type"][0]
         }
-
+        
         query_df = pd.DataFrame([sample])
-
-        prediction = model.predict(query_df).tolist()[0]
-
-        return jsonify({"predictions": float(prediction)})
-
-    except KeyError as e:
-        return jsonify({"error": f"Missing field: {str(e)}"}), 400
+        prediction = model.predict(query_df).tolist()
+        return jsonify({"predictions": prediction}) 
     except Exception as e:
         return jsonify({"error": str(e)}), 500