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Dockerfile

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+
+# Use an official lightweight Python image
+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file first to take advantage of Docker caching
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose the port Streamlit will run on (Hugging Face Spaces uses 7860)
+EXPOSE 7860
+
+# Command to run the Streamlit app
+# We bind to 0.0.0.0 and port 7860 for cloud compatibility
+CMD ["streamlit", "run", "app.py", "--server.port", "7860", "--server.address", "0.0.0.0"]

app.py

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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}")

requirements.txt

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+streamlit
+requests
+pandas
+numpy