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Dockerfile

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+FROM python:3.11.3-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 --upgrade -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:sales_predictor_api"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+sales_predictor_api = Flask("SuperKart Sales Predictor")
+
+# Re-define the same functions used inside the pipeline
+def add_store_age(df):
+    df = df.copy()
+    df['Store_Age'] = 2025 - df['Store_Established_Year']
+    df = df.drop(columns=['Store_Established_Year'])
+    return df
+
+def map_ordered_features(X):
+    sugar_order_map = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
+    size_order_map = {'Small': 0, 'Medium': 1, 'High': 2}
+    city_order_map = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
+
+    X = X.copy()
+    X['Product_Sugar_Content'] = X['Product_Sugar_Content'].map(sugar_order_map).astype(int)
+    X['Store_Size'] = X['Store_Size'].map(size_order_map).astype(int)
+    X['Store_Location_City_Type'] = X['Store_Location_City_Type'].map(city_order_map).astype(int)
+    return X
+
+# Load the trained SuperKart Sales prediction model
+model = joblib.load("superkart_sales_model.pkl")
+
+# Define a route for the home page
+@sales_predictor_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Prediction API!"
+
+# Define an endpoint to predict churn for a single customer
+@sales_predictor_api.post('/v1/productsales')
+def predict_sales():
+    try:
+        # Get JSON data
+        sales_data = request.get_json()
+
+        # Ensure input is a list of records
+        if isinstance(sales_data, dict):
+            sales_data = [sales_data]
+
+        # Convert to DataFrame
+        input_data = pd.DataFrame(sales_data)
+
+        # Predict
+        prediction = model.predict(input_data).tolist()[0]
+
+        return jsonify({"prediction": float(prediction)})
+
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    sales_predictor_api.run(debug=True)

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===2.32.3

superkart_sales_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b9d9a00480131b5d599b0cc6d958ab39e6466a30c1a4d5e846e4157c3fa312c
+size 12299802