Update app.py

app.py

@@ -1,51 +1,51 @@
-
-import .FeatureEngineering
-import joblib
-import pandas as pd
-from flask import Flask, request, jsonify
-
-# Initialize Flask app with a name
-app = Flask("SuperKart Sales Predictor")
-
-# Load the trained churn prediction model
-model = joblib.load("XGBoostRegressor_BEST_Pipeline.joblib")
-
-# Define a route for the home page
-@app.get('/')
-def home():
-    return "Welcome to the SuperKart Sales Prediction API"
-
-# Define an endpoint to predict churn for a single customer
-@app.post('/v1/product')
-def predict_churn():
-    # Get JSON data from the request
-    customer_data = request.get_json()
-    
-    # Extract relevant customer features from the input data
-    sample = {
-        'Product_Id': customer_data['Product_Id'],
-        'Product_Weight': customer_data['Product_Weight'],
-        'Product_Sugar_Content': customer_data['Product_Sugar_Content'],
-        'Product_Allocated_Area': customer_data['Product_Allocated_Area'],
-        'Product_Type': customer_data['Product_Type'],
-        'Product_MRP': customer_data['Product_MRP'],
-        'Store_Id': customer_data['Store_Id'],
-        'Store_Establishment_Year': customer_data['Store_Establishment_Year'],
-        'Store_Size': customer_data['Store_Size'],
-        'Store_Location_City_Type': customer_data['Store_Location_City_Type'],
-        'Store_Type': customer_data['Store_Type']
-    }
-
-    # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # Make a Sales prediction using the trained model
-    prediction = model.predict(input_data).tolist()[0]
-    
-    # Return the prediction as a JSON response
-    return jsonify({'Prediction': prediction})
-
-
-# Run the Flask app in debug mode
-if __name__ == '__main__':
-    app.run(debug=True)
+
+from FeatureEngineer import FeatureEngineer
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+app = Flask("SuperKart Sales Predictor")
+
+# Load the trained churn prediction model
+model = joblib.load("XGBoostRegressor_BEST_Pipeline.joblib")
+
+# Define a route for the home page
+@app.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Prediction API"
+
+# Define an endpoint to predict churn for a single customer
+@app.post('/v1/product')
+def predict_churn():
+    # Get JSON data from the request
+    customer_data = request.get_json()
+    
+    # Extract relevant customer features from the input data
+    sample = {
+        'Product_Id': customer_data['Product_Id'],
+        'Product_Weight': customer_data['Product_Weight'],
+        'Product_Sugar_Content': customer_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': customer_data['Product_Allocated_Area'],
+        'Product_Type': customer_data['Product_Type'],
+        'Product_MRP': customer_data['Product_MRP'],
+        'Store_Id': customer_data['Store_Id'],
+        'Store_Establishment_Year': customer_data['Store_Establishment_Year'],
+        'Store_Size': customer_data['Store_Size'],
+        'Store_Location_City_Type': customer_data['Store_Location_City_Type'],
+        'Store_Type': customer_data['Store_Type']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a Sales prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+    
+    # Return the prediction as a JSON response
+    return jsonify({'Prediction': prediction})
+
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)