Deploy Streamlit frontend with backend linkage

streamlit_app.py

@@ -1,5 +1,12 @@
+import os
+# Redirect HOME so Streamlit can write config without root permissions
+os.environ["HOME"] = "/tmp"
+# Disable usage statistics to avoid writing to protected dirs
+os.environ["STREAMLIT_GATHER_USAGE_STATS"] = "false"
+
 import streamlit as st
-import requests, pandas as pd
+import requests
+import pandas as pd
 
 st.set_page_config(page_title="ExtraaLearn Lead Converter", layout="centered")
 st.title("🎓 ExtraaLearn Lead Conversion")
@@ -9,7 +16,7 @@ st.write("Enter lead details and click Predict.")
 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("Pages per Visit", 1, 20, 3)
+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"])
@@ -37,9 +44,7 @@ if st.button("Predict"):
         "referral": int(referral),
         "last_activity": last_act
     }
-    # Call the backend prediction endpoint
-    url = f"https://huggingface.co/spaces/{'"$BACKEND_REPO"'}'/predict"
-    resp = requests.post(url, json=payload)
+    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})")