Deploy Streamlit frontend with app.py entrypoint and backend linkage

app.py

@@ -0,0 +1,54 @@
+import os
+# Redirect HOME so Streamlit can write config without root permission
+os.environ["HOME"] = "/tmp"
+# Disable usage stats to avoid writing to protected dirs
+os.environ["STREAMLIT_GATHER_USAGE_STATS"] = "false"
+
+import streamlit as st
+import requests
+import pandas as pd
+
+# Page config
+st.set_page_config(page_title="ExtraaLearn Lead Converter", layout="centered")
+st.title("🎓 ExtraaLearn Lead Conversion")
+st.write("Enter lead details and click Predict.")
+
+# Input widgets
+age = st.number_input("Age", 18, 100, 30)
+visits = st.number_input("Website Visits", 0, 50, 1)
+time_spent = st.number_input("Time Spent on Website (s)", 0, 5000, 300)
+pages = st.number_input("Page Views per Visit", 1, 20, 3)
+
+occ = st.selectbox("Current Occupation", ["Professional","Unemployed","Student"])
+first_int = st.selectbox("First Interaction", ["Website","Mobile App"])
+profile = st.selectbox("Profile Completed", ["Low","Medium","High"])
+print1 = st.checkbox("Saw Newspaper Ad")
+print2 = st.checkbox("Saw Magazine Ad")
+digital = st.checkbox("Saw Digital Ad")
+edu_chan = st.checkbox("Heard via Education Channels")
+referral = st.checkbox("Heard via Referral")
+last_act = st.selectbox("Last Activity", ["Email Activity","Phone Activity","Website Activity"])
+
+if st.button("Predict"):
+    payload = {
+        "age": age,
+        "website_visits": visits,
+        "time_spent_on_website": time_spent,
+        "page_views_per_visit": pages,
+        "current_occupation": occ,
+        "first_interaction": first_int,
+        "profile_completed": profile,
+        "print_media_type1": int(print1),
+        "print_media_type2": int(print2),
+        "digital_media": int(digital),
+        "educational_channels": int(edu_chan),
+        "referral": int(referral),
+        "last_activity": last_act
+    }
+    # Call the backend predict endpoint
+    resp = requests.post("https://huggingface.co/spaces/singhina/ExtraaLeanBackend/predict", json=payload)
+    if resp.ok:
+        res = resp.json()
+        st.success(f"Conversion: {res['prediction']} (Prob: {res['probability']:.2f})")
+    else:
+        st.error(f"Error {resp.status_code}: {resp.text}")

huggingface.yml

@@ -1,2 +1,2 @@
 sdk: streamlit
-app_file: streamlit_app.py
+app_file: app.py