adityayow/CourseSpace

1import streamlit as st2import pandas as pd3import requests4 5# Set the title of the Streamlit app6st.title("Airbnb Rental Price Prediction")7 8# Section for online prediction9st.subheader("Online Prediction")10 11# Collect user input for property features12room_type = st.selectbox("Room Type", ["Entire home/apt", "Private room", "Shared room"])13accommodates = st.number_input("Accommodates (Number of guests)", min_value=1, value=2)14bathrooms = st.number_input("Bathrooms", min_value=1, step=1, value=2)15cancellation_policy = st.selectbox("Cancellation Policy (kind of cancellation policy)", ["strict", "flexible", "moderate"])16cleaning_fee = st.selectbox("Cleaning Fee Charged?", ["True", "False"])17instant_bookable = st.selectbox("Instantly Bookable?", ["False", "True"])18review_scores_rating = st.number_input("Review Score Rating", min_value=0.0, max_value=100.0, step=1.0, value=90.0)19bedrooms = st.number_input("Bedrooms", min_value=0, step=1, value=1)20beds = st.number_input("Beds", min_value=0, step=1, value=1)21 22# Convert user input into a DataFrame23input_data = pd.DataFrame([{24    'room_type': room_type,25    'accommodates': accommodates,26    'bathrooms': bathrooms,27    'cancellation_policy': cancellation_policy,28    'cleaning_fee': cleaning_fee,29    'instant_bookable': 'f' if instant_bookable=="False" else "t",  # Convert to 't' or 'f'30    'review_scores_rating': review_scores_rating,31    'bedrooms': bedrooms,32    'beds': beds33}])34 35# Make prediction when the "Predict" button is clicked36if st.button("Predict"):37    response = requests.post("https://<username>-<repo_id>.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API38    if response.status_code == 200:39        prediction = response.json()['Predicted Price (in dollars)']40        st.success(f"Predicted Rental Price (in dollars): {prediction}")41    else:42        st.error("Error making prediction.")43 44# Section for batch prediction45st.subheader("Batch Prediction")46 47# Allow users to upload a CSV file for batch prediction48uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])49 50# Make batch prediction when the "Predict Batch" button is clicked51if uploaded_file is not None:52    if st.button("Predict Batch"):53        response = requests.post("https://<username>-<repo_id>.hf.space/v1/rentalbatch", files={"file": uploaded_file})  # Send file to Flask API54        if response.status_code == 200:55            predictions = response.json()56            st.success("Batch predictions completed!")57            st.write(predictions)  # Display the predictions58        else:59            st.error("Error making batch prediction.")60