File size: 3,179 Bytes
cad6845 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | 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)}')
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