Update app.py

app.py

@@ -1,134 +1,110 @@
-import joblib
-import pandas as pd
-from flask import Flask, request, jsonify
-import numpy as np
-
-# Initialize Flask app
-sales_forecast_api = Flask("SuperKart Sales Forecast API")
-
-# Load the trained sales forecasting model
-model = joblib.load("superkart_sales_forecast_model.pkl")
-
-# Define a route for the home page
-@sales_forecast_api.get('/')
-def home():
-    return "Welcome to the SuperKart Sales Forecasting API! 🛒📊"
-
-# Define an endpoint to predict sales for a single product-store combination
-@sales_forecast_api.post('/v1/predict_sales')
-def predict_sales():
-    try:
-        # Get JSON data from the request
-        input_data = request.get_json()
-        
-        # Extract relevant features from the input data
-        sample = {
-            'Product_Weight': input_data['Product_Weight'],
-            'Product_Sugar_Content': input_data['Product_Sugar_Content'],
-            'Product_Allocated_Area': input_data['Product_Allocated_Area'],
-            'Product_Type': input_data['Product_Type'],
-            'Product_MRP': input_data['Product_MRP'],
-            'Store_Establishment_Year': input_data['Store_Establishment_Year'],
-            'Store_Size': input_data['Store_Size'],
-            'Store_Location_City_Type': input_data['Store_Location_City_Type'],
-            'Store_Type': input_data['Store_Type']
-        }
-        
-        # Convert the extracted data into a DataFrame
-        input_df = pd.DataFrame([sample])
-        
-        # Make sales prediction using the trained model
-        prediction = model.predict(input_df)[0]
-        
-        # Return the prediction as a JSON response
-        return jsonify({
-            'Product_Id': input_data.get('Product_Id', 'N/A'),
-            'Store_Id': input_data.get('Store_Id', 'N/A'),
-            'Predicted_Sales': round(prediction, 2),
-            'Currency': 'INR',
-            'Status': 'Success'
-        })
-    
-    except Exception as e:
-        return jsonify({
-            'Error': str(e),
-            'Status': 'Failed'
-        }), 400
-
-# Define an endpoint to predict sales for a batch of product-store combinations
-@sales_forecast_api.post('/v1/predict_sales_batch')
-def predict_sales_batch():
-    try:
-        # Get the uploaded CSV file from the request
-        file = request.files['file']
-        
-        # Read the file into a DataFrame
-        input_data = pd.read_csv(file)
-        
-        # Select only the required features for prediction
-        feature_columns = [
-            'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area', 
-            'Product_Type', 'Product_MRP', 'Store_Establishment_Year', 
-            'Store_Size', 'Store_Location_City_Type', 'Store_Type'
-        ]
-        
-        # Prepare data for prediction
-        prediction_data = input_data[feature_columns]
-        
-        # Make predictions for the batch data
-        predictions = model.predict(prediction_data)
-        
-        # Create output with Product_Id and Store_Id mapping
-        output_list = []
-        for i, (_, row) in enumerate(input_data.iterrows()):
-            prediction_entry = {
-                'Product_Id': row.get('Product_Id', f'Product_{i}'),
-                'Store_Id': row.get('Store_Id', f'Store_{i}'),
-                'Predicted_Sales': round(predictions[i], 2)
-            }
-            output_list.append(prediction_entry)
-        
-        return jsonify({
-            'Predictions': output_list,
-            'Total_Records': len(predictions),
-            'Currency': 'INR',
-            'Status': 'Success'
-        })
-    
-    except Exception as e:
-        return jsonify({
-            'Error': str(e),
-            'Status': 'Failed'
-        }), 400
-
-# Health check endpoint
-@sales_forecast_api.get('/health')
-def health_check():
-    return jsonify({
-        'Status': 'API is running',
-        'Model': 'XGBoost Sales Forecasting Model',
-        'Version': '1.0',
-        'Deployment': 'Hugging Face Spaces'
-    })
-
-# Get model information
-@sales_forecast_api.get('/model_info')
-def model_info():
-    return jsonify({
-        'Model_Name': 'SuperKart Sales Forecasting Model',
-        'Model_Type': 'XGBoost Regressor',
-        'Features': [
-            'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
-            'Product_Type', 'Product_MRP', 'Store_Establishment_Year',
-            'Store_Size', 'Store_Location_City_Type', 'Store_Type'
-        ],
-        'Target': 'Product_Store_Sales_Total',
-        'Performance': {
-            'Test_RMSE': 286.57,
-            'Test_R_Squared': 0.928
-        }
-    })
-
-# Run the Flask app
-if __name__ == '__main__':
-    sales_forecast_api.run(debug=True, host='0.0.0.0', port=7860)  # Port 7860 for Hugging Face
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+import numpy as np
+
+# Initialize Flask app
+sales_forecast_api = Flask("SuperKart Sales Forecast API")
+
+# Load the trained sales forecasting model
+model = joblib.load("superkart_sales_forecast_model.pkl")
+
+# Define a route for the home page
+@sales_forecast_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Forecasting API! 🛒📊"
+
+# Define an endpoint to predict sales for a single product-store combination
+@sales_forecast_api.post('/v1/predict_sales')
+def predict_sales():
+    try:
+        # Get JSON data from the request
+        input_data = request.get_json()
+        
+        # Extract relevant features from the input data
+        sample = {
+            'Product_Weight': input_data['Product_Weight'],
+            'Product_Sugar_Content': input_data['Product_Sugar_Content'],
+            'Product_Allocated_Area': input_data['Product_Allocated_Area'],
+            'Product_Type': input_data['Product_Type'],
+            'Product_MRP': input_data['Product_MRP'],
+            'Store_Establishment_Year': input_data['Store_Establishment_Year'],
+            'Store_Size': input_data['Store_Size'],
+            'Store_Location_City_Type': input_data['Store_Location_City_Type'],
+            'Store_Type': input_data['Store_Type']
+        }
+        
+        # Convert the extracted data into a DataFrame
+        input_df = pd.DataFrame([sample])
+        
+        # Make sales prediction using the trained model
+        prediction = model.predict(input_df)[0]
+        
+        # Convert NumPy float32 to Python float
+        prediction = float(prediction)  
+        
+        # Return the prediction as a JSON response
+        return jsonify({
+            'Product_Id': input_data.get('Product_Id', 'N/A'),
+            'Store_Id': input_data.get('Store_Id', 'N/A'),
+            'Predicted_Sales': round(prediction, 2),
+            'Currency': 'INR',
+            'Status': 'Success'
+        })
+    
+    except Exception as e:
+        return jsonify({
+            'Error': str(e),
+            'Status': 'Failed'
+        }), 400
+
+# Define an endpoint to predict sales for a batch of product-store combinations
+@sales_forecast_api.post('/v1/predict_sales_batch')
+def predict_sales_batch():
+    try:
+        # Get the uploaded CSV file from the request
+        file = request.files['file']
+        
+        # Read the file into a DataFrame
+        input_data = pd.read_csv(file)
+        
+        # Select only the required features for prediction
+        feature_columns = [
+            'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area', 
+            'Product_Type', 'Product_MRP', 'Store_Establishment_Year', 
+            'Store_Size', 'Store_Location_City_Type', 'Store_Type'
+        ]
+        
+        # Prepare data for prediction
+        prediction_data = input_data[feature_columns]
+        
+        # Make predictions for the batch data
+        predictions = model.predict(prediction_data)
+        
+        # Create output with Product_Id and Store_Id mapping
+        output_list = []
+        for i, (_, row) in enumerate(input_data.iterrows()):
+            prediction_entry = {
+                'Product_Id': row.get('Product_Id', f'Product_{i}'),
+                'Store_Id': row.get('Store_Id', f'Store_{i}'),
+                'Predicted_Sales': float(predictions[i])  
+            }
+            output_list.append(prediction_entry)
+        
+        return jsonify({
+            'Predictions': output_list,
+            'Total_Records': len(predictions),
+            'Currency': 'INR',
+            'Status': 'Success'
+        })
+    
+    except Exception as e:
+        return jsonify({
+            'Error': str(e),
+            'Status': 'Failed'
+        }), 400
+
+
+# Run the Flask app
+if __name__ == '__main__':
+    sales_forecast_api.run(debug=True, host='0.0.0.0', port=7860)  # Port 7860 for Hugging Face