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Dockerfile

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+
+# Use a lightweight Python image as the base
+FROM python:3.8-slim
+
+# Set the working directory
+WORKDIR /app
+
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port the app runs on
+EXPOSE 5000
+
+# Command to run the application using Gunicorn
+CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]

app.py

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+
+from flask import Flask, request, jsonify
+# Import make_prediction function after creating the predict.py file
+# from predict import make_prediction # This import will work once predict.py is a separate file
+import pandas as pd
+import joblib
+from sklearn.compose import ColumnTransformer
+
+app = Flask(__name__)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """
+    Receives product and store data, makes a sales prediction, and returns the result.
+    """
+    try:
+        data = request.get_json()
+
+        if data is None:
+            return jsonify({'error': 'Invalid JSON data provided'}), 400
+
+        model_path = 'xgb_tuned_model.pkl'
+
+         # Load the model
+        model = joblib.load(model_path)
+
+        # Convert the input data to a pandas DataFrame
+        # Assuming the input data dictionary keys match the original DataFrame columns
+        input_df = pd.DataFrame([data])
+
+        # Make prediction using the make_prediction function
+        prediction = model.predict(input_df)[0]
+
+        if prediction is None:
+             return jsonify({'error': 'Prediction could not be made'}), 500
+
+        return jsonify({'predicted_sales': prediction})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    # This is for running locally, in a production Docker environment, a WSGI server would be used
+    app.run(host='0.0.0.0', port=5000) # Commented out to prevent blocking in notebook

requirements.txt

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+Flask
+pandas
+scikit-learn
+xgboost
+joblib

xgb_tuned_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:658182bcf828670006557caf1497e397ecc267b68bfc44966006ae08ddb716a1
+size 261993