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@@ -11,6 +11,6 @@ RUN pip install --no-cache-dir -r requirements.txt
 
 # Define the command to start the application using Gunicorn with 4 worker processes
 # - `-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
+# - `-b 0.0.0.0:5000`: Binds the server to port 5000 on all network interfaces (matching the Flask app port)
 # - `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:app"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "app:app"]

app.py

@@ -2,70 +2,85 @@ import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
-# Initialize Flask app with a name
-app = Flask("Telecom Customer Churn Predictor")
+# Initialize Flask app
+app = Flask(__name__)
 
-# Load the trained churn prediction model
-model = joblib.load("churn_prediction_model_v1_0.joblib")
+# Load the trained sales forecasting model pipeline
+model = joblib.load("backend_files/final_sales_forecasting_model.joblib") # Adjust path as needed
 
 # Define a route for the home page
-@app.get('/')
+@app.route('/')
 def home():
-    return "Welcome to the Telecom Customer Churn Prediction API"
+    return "Welcome to the SuperKart Sales Forecasting API"
 
-# Define an endpoint to predict churn for a single customer
-@app.post('/v1/customer')
-def predict_churn():
+# Define an endpoint to predict sales for a single product-store combination
+@app.route('/predict_single', methods=['POST'])
+def predict_single():
     # Get JSON data from the request
-    customer_data = request.get_json()
-
-    # Extract relevant customer features from the input data
-    sample = {
-        'SeniorCitizen': customer_data['SeniorCitizen'],
-        'Partner': customer_data['Partner'],
-        'Dependents': customer_data['Dependents'],
-        'tenure': customer_data['tenure'],
-        'PhoneService': customer_data['PhoneService'],
-        'InternetService': customer_data['InternetService'],
-        'Contract': customer_data['Contract'],
-        'PaymentMethod': customer_data['PaymentMethod'],
-        'MonthlyCharges': customer_data['MonthlyCharges'],
-        'TotalCharges': customer_data['TotalCharges']
-    }
-
-    # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame([sample])
-
-    # 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"
-
-    # Return the prediction as a JSON response
-    return jsonify({'Prediction': prediction_label})
-
-# Define an endpoint to predict churn for a batch of customers
-@app.post('/v1/customerbatch')
-def predict_churn_batch():
-    # Get the uploaded CSV file from the request
+    data = request.get_json()
+
+    # Extract relevant features from the input data, ensuring correct order and names
+    # The keys in the dictionary should match the column names in your original training data X
+    try:
+        sample = {
+            'Product_Id': data['Product_Id'],
+            'Product_Weight': data['Product_Weight'],
+            'Product_Sugar_Content': data['Product_Sugar_Content'],
+            'Product_Allocated_Area': data['Product_Allocated_Area'],
+            'Product_Type': data['Product_Type'],
+            'Product_MRP': data['Product_MRP'],
+            'Store_Id': data['Store_Id'],
+            'Store_Establishment_Year': data['Store_Establishment_Year'],
+            'Store_Size': data['Store_Size'],
+            'Store_Location_City_Type': data['Store_Location_City_Type'],
+            'Store_Type': data['Store_Type']
+        }
+
+        # Convert the extracted data into a DataFrame
+        input_data = pd.DataFrame([sample])
+
+        # Make a sales prediction using the trained model pipeline
+        prediction = model.predict(input_data).tolist()[0]
+
+        # Return the prediction as a JSON response
+        return jsonify({'predicted_sales': prediction})
+
+    except KeyError as e:
+        return jsonify({'error': f'Missing data for key: {e}'}), 400
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+
+# Define an endpoint to predict sales for a batch of product-store combinations from a CSV file
+@app.route('/predict_batch', methods=['POST'])
+def predict_batch():
+    # Get the uploaded file from the request
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file part in the request'}), 400
+
     file = request.files['file']
 
-    # Read the file into a DataFrame
-    input_data = pd.read_csv(file)
+    # If the user does not select a file, the browser submits an empty file without a filename.
+    if file.filename == '':
+        return jsonify({'error': 'No selected file'}), 400
+
+    if file:
+        try:
+            # Read the file into a DataFrame
+            input_data = pd.read_csv(file)
+
+            # Make sales predictions using the trained model pipeline
+            predictions = model.predict(input_data).tolist()
 
-    # 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()
-    ]
+            # Return the predictions as a JSON response
+            return jsonify({'predicted_sales': predictions})
 
-    cust_id_list = input_data.customerID.values.tolist()
-    output_dict = dict(zip(cust_id_list, predictions))
+        except Exception as e:
+            return jsonify({'error': str(e)}), 500
+    else:
+        return jsonify({'error': 'Something went wrong with file upload'}), 500
 
-    return output_dict
 
-# Run the Flask app in debug mode
 if __name__ == '__main__':
-    app.run(debug=True)
+    # Run the Flask app
+    app.run(debug=True, host='0.0.0.0', port=5000) # Run on all available interfaces and port 5000

final_sales_forecasting_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3de540996696fa424339c00294222bc600489164d994d93c7e5a3483dccd6e6
+size 65134690