frontend_files/app.py

import streamlit as st
import pandas as pd
import requests
import os

# Set the title of the Streamlit app
st.title('Airbnb Rental Price Prediction')

# Get the backend API URL from environment variables (set during Container Apps deployment)
# Format: https://<app-name>.<region>.azurecontainerapps.io
backend_api_url = os.getenv('BACKEND_API_URL', 'https://rental-price-prediction-api.azurecontainerapps.io')

# Section for online prediction
st.subheader('Online Prediction')

# Collect user input for property features
room_type = st.selectbox('Room Type', ['Entire home/apt', 'Private room', 'Shared room'])
accommodates = st.number_input('Accommodates (Number of guests)', min_value=1, value=2)
bathrooms = st.number_input('Bathrooms', min_value=1, step=1, value=2)
cancellation_policy = st.selectbox('Cancellation Policy (kind of cancellation policy)', ['strict', 'flexible', 'moderate'])
cleaning_fee = st.selectbox('Cleaning Fee Charged?', ['True', 'False'])
instant_bookable = st.selectbox('Instantly Bookable?', ['False', 'True'])
review_scores_rating = st.number_input('Review Score Rating', min_value=0.0, max_value=100.0, step=1.0, value=90.0)
bedrooms = st.number_input('Bedrooms', min_value=0, step=1, value=1)
beds = st.number_input('Beds', min_value=0, step=1, value=1)

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'room_type': room_type,
    'accommodates': accommodates,
    'bathrooms': bathrooms,
    'cancellation_policy': cancellation_policy,
    'cleaning_fee': cleaning_fee,
    'instant_bookable': 'f' if instant_bookable=='False' else 't',  # Convert to 't' or 'f'
    'review_scores_rating': review_scores_rating,
    'bedrooms': bedrooms,
    'beds': beds
}])

# Make prediction when the 'Predict' button is clicked
if st.button('Predict'):
    try:
        response = requests.post(f'{backend_api_url}/v1/rental', json=input_data.to_dict(orient='records')[0])
        if response.status_code == 200:
            prediction = response.json()['Predicted Price (in dollars)']
            st.success(f'Predicted Rental Price (in dollars): {prediction}')
        else:
            st.error(f'Error making prediction. Status code: {response.status_code}')
    except Exception as e:
        st.error(f'Error connecting to backend: {str(e)}')

# Section for batch prediction
st.subheader('Batch Prediction')

# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader('Upload CSV file for batch prediction', type=['csv'])

# Make batch prediction when the 'Predict Batch' button is clicked
if uploaded_file is not None:
    if st.button('Predict Batch'):
        try:
            response = requests.post(f'{backend_api_url}/v1/rentalbatch', files={'file': uploaded_file})
            if response.status_code == 200:
                predictions = response.json()
                st.success('Batch predictions completed!')
                st.write(predictions)  # Display the predictions
            else:
                st.error(f'Error making batch prediction. Status code: {response.status_code}')
        except Exception as e:
            st.error(f'Error connecting to backend: {str(e)}')