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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| # Set the title of the Streamlit app | |
| st.title("ExtraaLearn Customer Predictor") | |
| st.subheader("Online Prediction") | |
| # Collect user input for property features | |
| age = st.number_input("age", min_value=5, max_value=90, step=1, value=30) | |
| website_visits = st.number_input("website_visits", min_value=0, step=1, value=1) | |
| time_spent_on_website = st.number_input("time_spent_on_website", min_value=0, step=1, value=1) | |
| page_views_per_visit = st.number_input("page_views_per_visit", min_value=0, step=1, value=1) | |
| current_occupation = st.selectbox("current_occupation", ["Professional", "Student", "Unemployed"]) | |
| first_interaction = st.selectbox("first_interaction", ["Mobile App", "Website"]) | |
| profile_completed = st.selectbox("profile_completed", ["Medium", "High", "Low"]) | |
| last_activity = st.selectbox("last_activity", ["Website Activity", "Email Activity", "Phone Activity"]) | |
| print_media_type1 = st.selectbox("print_media_type1", ["Yes", "No"]) | |
| print_media_type2 = st.selectbox("print_media_type2", ["Yes", "No"]) | |
| digital_media = st.selectbox("digital_media", ["Yes", "No"]) | |
| educational_channels = st.selectbox("educational_channels", ["Yes", "No"]) | |
| referral = st.selectbox("referral", ["Yes", "No"]) | |
| # Convert user input into a DataFrame | |
| input_data = pd.DataFrame([{ | |
| 'age' : 'age', | |
| 'website_visits' : 'website_visits', | |
| 'time_spent_on_website' : 'time_spent_on_website', | |
| 'page_views_per_visit' : 'page_views_per_visit', | |
| 'current_occupation' : 'current_occupation', | |
| 'first_interaction' : 'first_interaction', | |
| 'profile_completed' : 'profile_completed', | |
| 'last_activity' : 'last_activity', | |
| 'print_media_type1' : 'print_media_type1', | |
| 'print_media_type2' : 'print_media_type2', | |
| 'digital_media' : 'digital_media', | |
| 'educational_channels' : 'educational_channels', | |
| 'referral' : 'referral' | |
| }]) | |
| # Make prediction when the "Predict" button is clicked | |
| if st.button("Predict"): | |
| response = requests.post("https://<username>-<repo_id>.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://jackfroooot-AssignmentExtraaLearnBackend.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.") | |