Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,16 @@
+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:extralearn_predictor_api"]

app.py

@@ -0,0 +1,85 @@
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+
+# Initialize the Flask application
+extralearn_predictor_api = Flask("extra Learn Status Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("extralearn.joblib")
+
+# Define a route for the home page (GET request)
+@extralearn_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 Airbnb Rental Price Prediction API!"
+
+# Define an endpoint for single property prediction (POST request)
+@extralearn_predictor_api.post('/v1/extralearn')
+def predict_extralearn():
+    """
+    This function handles POST requests to the '/v1/extralearn' 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
+    extralearn_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'age': extralearn_data['age'],
+        'profile_completed': extralearn_data['profile_completed'],
+        'current_occupation': extralearn_data['current_occupation'],
+        'first_interaction': extralearn_data['first_interaction'],
+        'last_activity': extralearn_data['last_activity'],
+        'referral': extralearn_data['referral'],
+        'digital_media': extralearn_data['digital_media']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_status = model.predict(input_data)[0]
+
+    
+
+    # Return the actual price
+    return jsonify({'Predicted Status': predicted_status})
+
+
+# Define an endpoint for batch prediction (POST request)
+@extralearn_predictor_api.post('/v1/extralearnbatch')
+def predict_rental_price_batch():
+    """
+    This function handles POST requests to the '/v1/extralearnbatch' endpoint.
+    It expects a CSV file containing property details for multiple properties
+    and returns the predicted rental prices as a dictionary in the JSON response.
+    """
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the CSV file into a Pandas DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for all properties in the DataFrame (get log_prices)
+    predicted_status = model.predict(input_data).tolist()
+
+    
+
+    # Create a dictionary to store the predictions
+    output_dict = {
+        'Predicted Status': predicted_status
+    }
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    extralearn_predictor_api.run(debug=True)

requirements.txt

@@ -0,0 +1,11 @@
+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