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@@ -13,4 +13,4 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 # - `-w 4`: Uses 4 worker processes for handling requests
 # - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
 # - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:cust_churn_predictor_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:SuperKart_predictor_api"]

SuperKart_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:945e3bbcca81bd8231d865137efc1a3f6a77f9f98b9ec285bd0400488c2a3610
+size 63874755

app.py

@@ -1,53 +1,52 @@
+
 import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
 # Initialize Flask app with a name
-cust_churn_predictor_api = Flask ("Customer Churn Predictor Week1")
+SuperKart_predictor_api = Flask ("SuperKart Predictor")
 
 # Load the trained churn prediction model
-model = joblib.load ("churn_prediction_model_v2_0.joblib")
+model = joblib.load ("SuperKart_prediction_model_v1_0.joblib")
 
 # Define a route for the home page
-@cust_churn_predictor_api.get ('/')
+@SuperKart_predictor_api.get ('/')
 def home ():
-    return "Welcome to the Customer Churn Prediction Week1 API!"
+    return "Welcome to the Super Kart Prediction!"
 
 # Define an endpoint to predict churn for a single customer
-@cust_churn_predictor_api.post ('/v1/customer')
-def predict_churn ():
+@SuperKart_predictor_api.post ('/v1/SuperKartSales')
+def predict_sales ():
     # Get JSON data from the request
-    customer_data = request.get_json ()
+    sales_data = request.get_json ()
 
     # Extract relevant customer features from the input data
-    sample = {
-        'customerID'      : customer_data ['customerID'],
-        'SeniorCitizen'   : customer_data ['SeniorCitizen'],
-        'tenure'          : customer_data ['tenure'],
-        'MonthlyCharges'  : customer_data ['MonthlyCharges'],
-        'TotalCharges'    : customer_data ['TotalCharges'],
-        'Partner'         : customer_data ['Partner'],
-        'Dependents'      : customer_data ['Dependents'],
-        'PhoneService'    : customer_data ['PhoneService'],
-        'InternetService' : customer_data ['InternetService'],
-        'Contract'        : customer_data ['Contract'],
-        'PaymentMethod'   : customer_data ['PaymentMethod']
+    data_info = {
+        'Product_Weight'            : sales_data ['Product_Weight'],
+        'Product_Sugar_Content'     : sales_data ['TotalCharges'],
+        'Product_Allocated_Area'    : sales_data ['Product_Allocated_Area'],
+        'Product_Type'              : sales_data ['Product_Type'],
+        'Product_MRP'               : sales_data ['Product_MRP'],
+        'Store_Id'                  : sales_data ['Store_Id'],
+        'Store_Establishment_Year'  : sales_data ['Store_Establishment_Year'],
+        'Store_Size'                : sales_data ['Store_Size'],
+        'Store_Location_City_Type'  : sales_data ['Store_Location_City_Type'],
+        'Store_Type'                : sales_data ['Store_Type']
     }
 
     # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame ([sample])
+    input_data = pd.DataFrame ([data_info])
 
     # Make a churn prediction using the trained model
-    prediction = model.predict (input_data).tolist ()[0]
-
-    # Map prediction result to a human-readable label
-    prediction_label = "churn" if prediction == 1 else "not churn"
+    predicted_sales = model.predict (input_data).tolist ()[0]
 
     # Return the prediction as a JSON response
-    return jsonify ({'Prediction': prediction_label})
+    return jsonify ({'Sales Prediction': predicted_sales})
 
-# Define an endpoint to predict churn for a batch of customers
-@cust_churn_predictor_api.post ('/v1/customerbatch')
+
+# Define an endpoint to predict sales for a batch of data
+# here we assume the data to conatain same columns as per the data provided for this project
+@SuperKart_predictor_api.post ('/v1/SuperKartBatchSales')
 def predict_churn_batch ():
     # Get the uploaded CSV file from the request
     file = request.files ['file']
@@ -55,18 +54,27 @@ def predict_churn_batch ():
     # Read the file into a DataFrame
     input_data = pd.read_csv (file)
 
-    # Make predictions for the batch data and convert raw predictions into a readable format
-    predictions = [
-        'Churn' if x == 1
-        else "Not Churn"
-        for x in model.predict (input_data.drop ("customerID",axis=1)).tolist ()
-    ]
+    # Handle Product_Id if present
+    if "Product_Id" in input_data.columns:
+        product_ids = input_data["Product_Id"].copy()
+        X = input_data.drop(columns=["Product_Id"])
+    else:
+        product_ids = None
+        X = input_data
+
+    # Make predictions
+    predictions = model.predict(X).tolist()
 
-    cust_id_list = input_data.customerID.values.tolist ()
-    output_dict = dict(zip (cust_id_list, predictions))
+    # Prepare response
+    if product_ids is not None:
+        # Return mapping of Product_Id to predictions
+        output_dict = dict(zip(product_ids.tolist(), predictions))
+    else:
+        # If no Product_Id, just return index → predictions
+        output_dict = dict(zip(input_data.index.tolist(), predictions))
 
-    return output_dict  
+    return output_dict
 
 # Run the Flask app in debug mode
 if __name__ == '__main__':
-    cust_churn_predictor_api.run (debug=True)
+    SuperKart_predictor_api.run (debug=True)