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Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 5000 available to the world outside this container
+EXPOSE 5000
+
+# Run app.py when the container launches
+CMD ["python", "app.py"]

app.py

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+
+import flask
+import joblib
+import pandas as pd
+
+# Load the trained model
+model = joblib.load('tuned_random_forest_model.pkl') # Replace with the actual model filename
+
+app = flask.Flask(__name__)
+
+@app.route('/')
+def home():
+    return "Lead Conversion Prediction Backend"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get the data from the request
+        data = flask.request.get_json(force=True)
+        df_predict = pd.DataFrame(data)
+
+        # Make predictions
+        predictions = model.predict(df_predict)
+
+        # Return the predictions as JSON
+        return flask.jsonify(predictions.tolist())
+
+    except Exception as e:
+        return flask.jsonify({'error': str(e)})
+
+if __name__ == '__main__':
+    # Run the Flask app
+    app.run(host='0.0.0.0', port=5000)

requirements.txt

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+flask==3.0.3
+joblib==1.4.2
+pandas==2.2.2
+scikit-learn==1.6.1