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Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+FROM python:3.9-slim
 
+# Set the working directory inside the container
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Copy all files from the current directory to the container's working directory
+COPY . .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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", "1", "-b", "0.0.0.0:7860", "app:revenue_predictor_api"]

app.py

@@ -0,0 +1,73 @@
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+import os
+from flask import Flask, request, jsonify  # For creating the Flask API
+
+# Initialize the Flask application
+revenue_predictor_api = Flask("Revenue Predictor")
+
+# Load the trained machine learning model
+#model = joblib.load("revenue_prediction_model_v1_0.joblib")
+
+model = joblib.load(os.path.join(os.path.dirname(__file__), "revenue_prediction_model_v1_0.joblib"))
+print("Model loaded successfully!")
+
+# Define a route for the home page (GET request)
+@revenue_predictor_api.get('/')
+def home():
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to the Revenue Prediction API!"
+
+# Define an endpoint for single product revenue prediction (POST request)
+@revenue_predictor_api.post('/v1/revenue')
+def predict_revenue():
+    """
+    This function handles POST requests to the '/v1/revenue' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
+    """
+    # Get the JSON data from the request body
+    property_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': property_data['Product_Weight'],
+        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
+        'Product_Type': property_data['Product_Type'],
+        'Product_MRP': property_data['Product_MRP'],
+        'Store_Id': property_data['Store_Id'],
+        'Store_Establishment_Year': property_data['Store_Establishment_Year'],
+        'Store_Size': property_data['Store_Size'],
+        'Store_Location_City_Type': property_data['Store_Location_City_Type'],
+        'Store_Type': property_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get sales_total)
+    predicted_sales_total = model.predict(input_data)[0]
+
+    # Calculate actual sales
+    predicted_total = np.exp(predicted_sales_total)
+
+    # Convert predicted_total to Python float
+    predicted_total = round(float(predicted_total), 2)
+    # The conversion above is needed as we convert the model prediction (sales_total) to actual sales using np.exp, which returns predictions as NumPy float32 values.
+    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
+
+    # Return the actual sales
+    return jsonify({'Predicted Sales Total (in dollars)': predicted_total})
+
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    #revenue_predictor_api.run(debug=True)
+    port = int(os.environ.get("PORT", 7860))  # Hugging Face provides PORT
+    revenue_predictor_api.run(host="0.0.0.0", port=port, debug=True)

requirements.txt

@@ -1,3 +1,11 @@
-altair
-pandas
-streamlit
+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

revenue_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:618fca0bc5e1e4cc9f73a4f3fca08884e0229f801cfc6eac6ce98dfad4b536d9
+size 209482