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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app #Complete the code to mention the command in Docker to set the working directory
+
+# Copy all files from the current directory to the container's working directory
+COPY . . #Complete the code to mention the command in Docker to copy the files from the current directory to the container's working directory
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt #Complete the code to mention the command in Docker to install dependencies
+
+# 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
+# - `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:superkart_api"]

app.py

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+
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+
+# Initialize Flask app with a name
+superkart_api = Flask("superkart_api") #Complete the code to define the name of the app
+
+# Load the trained churn prediction model
+model = joblib.load("backend_files/random_forest.joblib") #Complete the code to define the location of the serialized model
+
+# Define a route for the home page
+@superkart_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Forecasting API!" #Complete the code to define a welcome message
+
+# Define an endpoint to predict churn for a single customer
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    # Get JSON data from the request
+    data = request.get_json()
+
+    # Extract relevant customer features from the input data. The order of the column names matters.
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Product_Allocated_Area': data['Product_Allocated_Area'],
+        'Product_MRP': data['Product_MRP'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Product_Id_char': data['Product_Id_char'],
+        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Type_Category': data['Product_Type_Category']
+    }
+
+    # 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]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Sales': prediction})
+
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    superkart_api.run(debug=True)

random_forest.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4bffc89e5ce9e126f8cfde3b84c53cd9b57fe7cf1c8cfeeb7e182053f6370d6
+size 432467

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.32.3
+uvicorn[standard]
+streamlit==1.43.2