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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 --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:boston_predictor_api"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+house_price_api = Flask("Boston House Price Predictor")
+
+# Load the trained Boston housing model
+model = joblib.load("boston_housing_model_v1_0.joblib")
+
+# Define a route for the home page
+@house_price_api.get('/')
+def home():
+    return "Welcome to the Boston House Price Prediction API!"
+
+# Define an endpoint to predict price for a single house
+@house_price_api.post('/v1/house')
+def predict_house_price():
+    # Get JSON data from the request
+    house_data = request.get_json()
+
+    # Extract relevant house features from the input data
+    sample = {
+        'CRIM': house_data['CRIM'],
+        'ZN': house_data['ZN'],
+        'INDUS': house_data['INDUS'],
+        'CHAS': house_data['CHAS'],
+        'NX': house_data['NX'],     # should be NOX in your dataset, check consistency
+        'RM': house_data['RM'],
+        'AGE': house_data['AGE'],
+        'DIS': house_data['DIS'],
+        'RAD': house_data['RAD'],
+        'TAX': house_data['TAX'],
+        'PTRATIO': house_data['PTRATIO'],
+        'LSTAT': house_data['LSTAT']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Predicted_MEDV': prediction})
+
+# Define an endpoint to predict price for a batch of houses
+@house_price_api.post('/v1/housebatch')
+def predict_house_batch():
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the file into a DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for the batch data
+    predictions = model.predict(input_data).tolist()
+
+    # Add predictions to the DataFrame
+    input_data['Predicted_MEDV'] = predictions
+
+    # Convert results to dictionary
+    result = input_data.to_dict(orient="records")
+
+    return jsonify(result)
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    house_price_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==2.28.1
+uvicorn[standard]