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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+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
+# - `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"]

SuperKart_model_deployment_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d406fadaa32d6c1161a883700a42286723514c051c8fc29786969e5ad9278244
+size 8537345

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+app = Flask("SuperKart sales prediction app backend")
+
+# Load the trained churn prediction model
+model = joblib.load("SuperKart_model_deployment_model_v1_0.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 sales of the single product in a store
+@app.post('/v1/sales')
+def predict_churn():
+    # Get JSON data from the request
+    store_data = request.get_json()
+
+    # Extract relevant store features from the input data
+    requestData = {
+        'Product_Weight': store_data['Product_Weight'],
+        'Product_Sugar_Content': store_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': store_data['Product_Allocated_Area'],
+        'Product_Type': store_data['Product_Type'],
+        'Product_MRP': store_data['Product_MRP'],
+        'Store_Id': store_data['Store_Id'],
+        'Store_Establishment_Year': store_data['Store_Establishment_Year'],
+        'Store_Size': store_data['Store_Size'],
+        'Store_Location_City_Type': store_data['Store_Location_City_Type'],
+        'Store_Type': store_data['Store_Type'],
+        'Product_Store_Sales_Total': store_data['Product_Store_Sales_Total']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([requestData])
+
+    # Make a churn prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    #Calculate the actual price
+    predicted_sales = np.exp(prediction)
+
+    # Convert predicted_price to Python float
+    predicted_sales = round(float(predicted_sales), 2)
+  
+    # Return the prediction as a JSON response
+    return jsonify({'Predicted_Sale': predicted_sales})

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit==1.43.2