Update app.py

app.py

@@ -21,39 +21,38 @@ def home():
 # Prediction route
 @sales_prediction_api.route("/predict", methods=["POST"])
 def predict():
-    try:
-        data = request.get_json()
-
-        # Model choice
-        model_choice = data.get("model", "dt")
-
-        # Extract features (MATCHES STREAMLIT KEYS)
-        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_Size": data["Store_Size"],
-            "Store_Location_City_Type": data["Store_Location_City_Type"],
-            "Store_Type": data["Store_Type"],
-            "Store_Age": data["Store_Age"]
-        }
-
-        sample_df = pd.DataFrame([sample])
-
-        # Select model
-        if model_choice == "dt":
-            prediction = dt_model.predict(sample_df)[0]
-        elif model_choice == "xgb":
-            prediction = xgb_model.predict(sample_df)[0]
-        else:
-            return jsonify({"error": "Invalid model choice"}), 400
-
-        return jsonify({
-            "Prediction": float(prediction),
-            "ModelUsed": model_choice
-        })
+    data = request.get_json()
+
+    # Model choice
+    model_choice = data.get("model", "dt")
+
+    # Extract features (MATCHES STREAMLIT KEYS)
+    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_Size": data["Store_Size"],
+        "Store_Location_City_Type": data["Store_Location_City_Type"],
+        "Store_Type": data["Store_Type"],
+        "Store_Age": data["Store_Age"]
+    }
+
+    sample_df = pd.DataFrame([sample])
+
+    # Select model
+    if model_choice == "dt":
+        prediction = dt_model.predict(sample_df)[0]
+    elif model_choice == "xgb":
+        prediction = xgb_model.predict(sample_df)[0]
+    else:
+        return jsonify({"error": "Invalid model choice"}), 400
+
+    return jsonify({
+        "Prediction": float(prediction),
+        "ModelUsed": model_choice
+    })
 
 if __name__ == '__main__':
     sales_prediction_api.run(debug=True)