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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Airbnb Rental Price Prediction")
# 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"):
response = requests.post("https://Thiresh-RentalPricePredictionBackend.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted Price (in dollars)']
st.success(f"Predicted Rental Price (in dollars): {prediction}")
else:
st.error("Error making prediction.")
# 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"):
response = requests.post("https://Thiresh-RentalPricePredictionBackend.hf.space/v1/rentalbatch", files={"file": uploaded_file}) # Send file to Flask API
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions) # Display the predictions
else:
st.error("Error making batch prediction.")
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