Update app.py

app.py

@@ -1,105 +1,107 @@
-
-import sys
-import joblib
-import pandas as pd
-import numpy as np
-from flask import Flask, request, jsonify
-
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.preprocessing import OneHotEncoder, StandardScaler, LabelEncoder
-from sklearn.impute import SimpleImputer
-from sklearn.ensemble import RandomForestRegressor
-from xgboost import XGBRegressor # Included for compatibility if you switch models
-
-class FeatureEngineer(BaseEstimator, TransformerMixin):
-    def __init__(self):
-        self.le_prod = LabelEncoder()
-        self.le_store = LabelEncoder()
-
-    def fit(self, X, y=None):
-        X_copy = X.copy()
-        X_copy['Product_Id_Cd'] = X_copy['Product_Id'].apply(lambda x: x[:2])
-        X_copy['Product_Sugar_Content_Corr'] = X_copy['Product_Sugar_Content'].str.replace('reg', 'Regular', regex=True)
-        X_copy['Operation_Years'] = 2025 - X_copy['Store_Establishment_Year']
-        
-        self.le_prod.fit(X_copy['Product_Id_Cd'])
-        le_feat=['Product_Sugar_Content_Corr','Store_Size','Store_Location_City_Type','Store_Type','Product_Id_Cd']
-        for i in le_feat:
-            self.le_prod.fit(X_copy[i])
-        
-        self.le_store.fit(X_copy['Store_Id'])
-        return self
-
-    def transform(self, X):
-        X_copy = X.copy()
-        X_copy['Product_Id_Cd'] = X_copy['Product_Id'].apply(lambda x: x[:2])
-        X_copy['Product_Sugar_Content_Corr'] = X_copy['Product_Sugar_Content'].str.replace('reg', 'Regular', regex=True)
-        X_copy['Operation_Years'] = 2013 - X_copy['Store_Establishment_Year']
-        
-        try:
-            le_feat=['Product_Sugar_Content_Corr','Store_Size','Store_Location_City_Type','Store_Type','Product_Id_Cd']
-            for i in le_feat:
-                X_copy[i] = self.le_prod.transform(X_copy[i])
-        except ValueError:
-            X_copy['Product_Id_Cd'] = -1
-        
-        try:
-            X_copy['Store'] = self.le_store.transform(X_copy['Store_Id'])
-        except ValueError:
-            X_copy['Store'] = -1
-        
-        rem_feat=['Product_Id','Store_Id','Product_Sugar_Content','Product_Type', 'Store_Establishment_Year']
-        X_copy.drop(rem_feat, axis=1, inplace=True)
-        
-        return X_copy
-
-# This allows joblib's pickle to find the class reference it saved during training.
-sys.modules['__main__'].FeatureEngineer = FeatureEngineer
-
-# 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)
+
+import sys
+import joblib
+import pandas as pd
+import numpy as np
+from flask import Flask, request, jsonify
+
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.preprocessing import OneHotEncoder, StandardScaler, LabelEncoder
+from sklearn.impute import SimpleImputer
+from sklearn.ensemble import RandomForestRegressor
+from xgboost import XGBRegressor # Included for compatibility if you switch models
+
+class FeatureEngineer(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        self.le_prod = LabelEncoder()
+        self.le_store = LabelEncoder()
+
+    def fit(self, X, y=None):
+        X_copy = X.copy()
+        X_copy['Product_Id_Cd'] = X_copy['Product_Id'].apply(lambda x: x[:2])
+        X_copy['Product_Sugar_Content_Corr'] = X_copy['Product_Sugar_Content'].str.replace('reg', 'Regular', regex=True)
+        X_copy['Operation_Years'] = 2025 - X_copy['Store_Establishment_Year']
+        
+        self.le_prod.fit(X_copy['Product_Id_Cd'])
+        le_feat=['Product_Sugar_Content_Corr','Store_Size','Store_Location_City_Type','Store_Type','Product_Id_Cd']
+        for i in le_feat:
+            self.le_prod.fit(X_copy[i])
+        
+        self.le_store.fit(X_copy['Store_Id'])
+        return self
+
+    def transform(self, X):
+        X_copy = X.copy()
+        X_copy['Product_Id_Cd'] = X_copy['Product_Id'].apply(lambda x: x[:2])
+        X_copy['Product_Sugar_Content_Corr'] = X_copy['Product_Sugar_Content'].str.replace('reg', 'Regular', regex=True)
+        X_copy['Operation_Years'] = 2013 - X_copy['Store_Establishment_Year']
+        
+        try:
+            le_feat=['Product_Sugar_Content_Corr','Store_Size','Store_Location_City_Type','Store_Type','Product_Id_Cd']
+            for i in le_feat:
+                X_copy[i] = self.le_prod.transform(X_copy[i])
+        except ValueError:
+            X_copy['Product_Id_Cd'] = -1
+        
+        try:
+            X_copy['Store'] = self.le_store.transform(X_copy['Store_Id'])
+        except ValueError:
+            X_copy['Store'] = -1
+        
+        rem_feat=['Product_Id','Store_Id','Product_Sugar_Content','Product_Type', 'Store_Establishment_Year']
+        X_copy.drop(rem_feat, axis=1, inplace=True)
+        
+        return X_copy
+
+import sys
+
+# This allows joblib's pickle to find the class reference it saved during training.
+sys.modules['__main__'].FeatureEngineer = FeatureEngineer
+
+# 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)