Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,80 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import requests
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+# Set the title and header of the app
+st.set_page_config(page_title="ExtraaLearn Lead Conversion Predictor", layout="wide")
+st.title("🚀 ExtraaLearn Lead Conversion Predictor")
+st.markdown("### Predict whether a lead will convert to a paid customer.")
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+# --- API Configuration ---
+# REPLACE THIS WITH YOUR ACTUAL DEPLOYED API URL
+API_URL = "https://<your-username>-<your-space-name>.hf.space/predict" 
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+# --- User Input Form ---
+st.header("Lead Information")
+with st.form("lead_form"):
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        age = st.slider("Age", 18, 65, 30)
+        current_occupation = st.radio("Current Occupation", ['Professional', 'Unemployed', 'Student'])
+        first_interaction = st.radio("First Interaction", ['Website', 'Mobile App'])
+    
+    with col2:
+        profile_completed = st.selectbox("Profile Completed", ['Low', 'Medium', 'High'])
+        website_visits = st.slider("Website Visits", 0, 30, 3)
+        time_spent_on_website = st.slider("Time Spent on Website (seconds)", 0, 2600, 500)
+    
+    with col3:
+        page_views_per_visit = st.number_input("Pages Viewed per Visit", 0.0, 20.0, 3.0)
+        last_activity = st.selectbox("Last Activity", ['Email Activity', 'Phone Activity', 'Website Activity'])
+        
+        st.markdown("<br>", unsafe_allow_html=True)
+        st.write("---")
+        st.subheader("Source of Information")
+        print_media_type1 = st.checkbox("Seen on Newspaper Ad?")
+        print_media_type2 = st.checkbox("Seen on Magazine Ad?")
+        digital_media = st.checkbox("Seen on Digital Ad?")
+        educational_channels = st.checkbox("Heard on Educational Channels?")
+        referral = st.checkbox("Referred by someone?")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+    # Submit button for the form
+    submit_button = st.form_submit_button(label='Predict Lead Conversion')
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# --- Prediction Logic ---
+if submit_button:
+    # Prepare data to be sent to the API
+    input_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": "Yes" if print_media_type1 else "No",
+        "print_media_type2": "Yes" if print_media_type2 else "No",
+        "digital_media": "Yes" if digital_media else "No",
+        "educational_channels": "Yes" if educational_channels else "No",
+        "referral": "Yes" if referral else "No"
+    }
+    
+    with st.spinner("Analyzing lead data..."):
+        try:
+            response = requests.post(API_URL, json=input_data)
+            if response.status_code == 200:
+                prediction = response.json()
+                st.success("✅ Prediction Successful!")
+                
+                # Display the prediction result
+                st.write(f"The model predicts this lead is **{prediction['prediction_label']}**.")
+                st.progress(int(prediction['probabilities']['Converted'] * 100), text="Conversion Probability")
+                st.info(f"Probability of Conversion: **{prediction['probabilities']['Converted']:.2f}**")
+            else:
+                st.error(f"❌ API call failed with status code: {response.status_code}")
+                st.json(response.json())
+        except requests.exceptions.RequestException as e:
+            st.error(f"❌ An error occurred while connecting to the API: {e}")