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| import streamlit as st | |
| import requests | |
| # Set page title and icon | |
| st.set_page_config(page_title="ExtraaLearn Lead Prediction", page_icon="🎓") | |
| st.title("🎓 ExtraaLearn: Lead Conversion Prediction") | |
| st.markdown("Enter lead details below to predict the likelihood of conversion.") | |
| # Layout with columns for better UI | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| age = st.number_input("Age", min_value=18, max_value=65, value=25) | |
| current_occupation = st.selectbox("Current Occupation", ["Student", "Professional", "Unemployed", "Others"]) | |
| first_interaction = st.selectbox("First Interaction", ["Website", "Mobile App"]) | |
| profile_completed = st.slider("Profile Completed (%)", 0, 100, 50) | |
| website_visits = st.number_input("Website Visits", min_value=0, value=5) | |
| referral = st.selectbox("Referral", ["No", "Yes"]) | |
| with col2: | |
| time_spent_on_website = st.number_input("Time Spent on Website (m)", min_value=0, value=300) | |
| page_views_per_visit = st.number_input("Page Views Per Visit", min_value=0.0, value=2.5) | |
| last_activity = st.selectbox("Last Activity", ["Email Opened", "Website Activity", "Mobile App Activity", "Others"]) | |
| print_media_type1 = st.selectbox("Print Media (Type 1)", ["No", "Yes"]) | |
| print_media_type2 = st.selectbox("Print Media (Type 2)", ["No", "Yes"]) | |
| digital_media = st.selectbox("Digital Media", ["No", "Yes"]) | |
| educational_channels = st.selectbox("Educational Channels", ["No", "Yes"]) | |
| # Prepare the data dictionary for the API | |
| lead_data = { | |
| "age": age, | |
| "current_occupation": current_occupation, | |
| "first_interaction": first_interaction, | |
| "profile_completed": profile_completed, | |
| "website_visits": website_visits, | |
| "time_spent_on_website": time_spent_on_website, | |
| "page_views_per_visit": page_views_per_visit, | |
| "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 | |
| } | |
| st.divider() | |
| if st.button("Predict Conversion Potential", type='primary'): | |
| try: | |
| # Update the URL below once your backend API is deployed | |
| api_url = "https://shantanuchande-extlearn-api.hf.space/v1/predict" | |
| response = requests.post(api_url, json=lead_data) | |
| if response.status_code == 200: | |
| result = response.json() | |
| prediction = result["Status_Prediction"] | |
| probability = result["Conversion_Probability"] | |
| if prediction == 1: | |
| st.success(f"### High Potential Lead!!!!") | |
| st.write(f"Confidence: {probability*100:.2f}%") | |
| else: | |
| st.warning(f"### Low Potential Lead") | |
| st.write(f"Confidence: {(1-probability)*100:.2f}%") | |
| else: | |
| st.error(f"Error in API request: {response.status_code}") | |
| except Exception as e: | |
| st.error(f"Connection Error: {e}") | |