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app.py

@@ -14,27 +14,18 @@ MODEL_PATH = "final_xgboost_pipeline.pkl"
 # --- Initialize Flask App ---
 app = Flask("SuperKart Sales Predictor")
 
-# --- Global Model Loading ---
+# Load the model
 try:
     model = joblib.load(MODEL_PATH)
     print("Model loaded successfully.") # Add logging
 except Exception as e:
     print(f"Error loading model: {e}") # Log error
 
-# --- API Routes ---
-
+# Define a route for the home page
 @app.get('/')
-def health_check():
-    """
-    Health check and information endpoint.
-    """
+def home():
     print("Home route accessed.") # Add logging
-    return jsonify({
-        "status": "OK",
-        "model_location": MODEL_PATH,
-        "model_status": "Loaded" if model else "Error: See logs",
-        "api_version": "No Versioning"
-    })
+    return "Welcome to the SuperKart Store Product Sales Prediction API."
 
 
 @app.route("/predict", methods=['POST']) # The simple, unversioned route
@@ -64,15 +55,12 @@ def predict_sales():
             # A well-built ML pipeline should handle all feature transformations internally.
             # input_df['Store_Establishment_Year'] = input_df['Store_Establishment_Year'].astype(str)
             
-            print(f"[{APP_NAME}] Input data prepared for pipeline.")
             # output variable removed as it was only used for the previous error
             
         except KeyError as ke:
-            print(f"[{APP_NAME}] Missing required feature: {ke}")
             return jsonify({'error': f'Missing required feature: {ke}. Please check all features are present.'}), 400
 
         except Exception as e:
-            print(f"[{APP_NAME}] Error converting data to DataFrame: {e}")
             return jsonify({'error': 'Error processing input data format', 'details': str(e)}), 400
 
 
@@ -85,7 +73,7 @@ def predict_sales():
         else:
             predicted_sales = prediction # Should ideally be a float
 
-        print(f"[{APP_NAME}] Prediction result: {predicted_sales:.2f}")
+        print(f"Prediction result: {predicted_sales:.2f}")
 
         # --- Response ---
         # 3. FIX: Return the actual predicted value instead of the string message
@@ -96,13 +84,13 @@ def predict_sales():
 
     except Exception as e:
         # Catch any unexpected runtime errors
-        print(f"[{APP_NAME}] An unexpected internal error occurred: {e}")
+        print(f"An unexpected internal error occurred: {e}")
         print(traceback.format_exc())
         return jsonify({
             'error': 'Internal Server Error during prediction',
             'details': str(e)
         }), 500
-        
+
 
 # --- Local Runner (Optional: Comment out for production WSGI) ---
 if __name__ == '__main__':