app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("ExtraaLearn Customer Prediction")

# Section for online prediction
st.subheader("Single Customer Prediction")

# Collect user input for customer features
age = st.number_input("Age", min_value=15, max_value=80, step=1, value=25)

current_occupation = st.selectbox("Current Occupation", [
    "Student", "Unemployed", "Employed", "Self-Employed", "Professional"
])

first_interaction = st.selectbox("First Interaction", [
    "Website", "Mobile App", "Referral", "Advertisement", "Social Media"
])

# Updated: profileCompleted as dropdown with High/Medium/Low
profile_completed = st.selectbox("Profile Completed", ["High", "Medium", "Low"])

website_visits = st.number_input("Website Visits", min_value=0, max_value=100, step=1, value=15)

time_spent_on_website = st.number_input("Time Spent on Website (seconds)", min_value=0, max_value=3600, step=10, value=1200)

page_views_per_visit = st.number_input("Page Views Per Visit", min_value=1, max_value=20, step=1, value=8)

last_activity = st.selectbox("Last Activity", [
    "Login", "Course View", "Payment Page", "Profile Update", "Website Activity"
])

print_media_type1 = st.selectbox("Print Media Type 1", ["Yes", "No"])
print_media_type2 = st.selectbox("Print Media Type 2", ["Yes", "No"])
digital_media = st.selectbox("Digital Media", ["Yes", "No"])
educational_channels = st.selectbox("Educational Channels", ["Yes", "No"])
referral = st.selectbox("Referral", ["Yes", "No"])

# Convert user input into a dictionary matching the API requirements
input_data = {
    'age': age,
    'currentOccupation': current_occupation,
    'firstInteraction': first_interaction,
    'profileCompleted': profile_completed,  # This will now be "High", "Medium", or "Low"
    'websiteVisits': website_visits,
    'timeSpentOnWebsite': time_spent_on_website,
    'pageViewsPerVisit': page_views_per_visit,
    'lastActivity': last_activity,
    'printMediaType1': print_media_type1,
    'printMediaType2': print_media_type2,
    'digitalMedia': digital_media,
    'educationalChannels': educational_channels,
    'referral': referral
}

# Make prediction when the "Predict" button is clicked
if st.button("Predict Customer Conversion"):
    try:
        response = requests.post(
            "https://manasranjanpani-extraalearncustomerpredictionbackend.hf.space/v1/customers",
            json=input_data
        )

        if response.status_code == 200:
            result = response.json()
            prediction = result['predicted_customer_status']
            st.success(f"Predicted Customer Conversion Score: {prediction:.2f}")

            # Display additional info if available
            if 'input_received' in result:
                st.info(f"Processed {len(result['input_received']['fields_processed'])} features successfully")

        else:
            error_data = response.json()
            st.error(f"Error making prediction: {error_data.get('error', 'Unknown error')}")

    except requests.exceptions.RequestException as e:
        st.error(f"Connection error: {e}")
    except Exception as e:
        st.error(f"Unexpected error: {e}")

# Section for batch prediction
st.subheader("Batch Prediction")

# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])

if uploaded_file is not None:
    # Display preview of uploaded data
    st.write("Preview of uploaded data:")
    batch_data = pd.read_csv(uploaded_file)
    st.dataframe(batch_data.head())

    # Check if required columns are present
    required_columns = list(input_data.keys())
    missing_columns = [col for col in required_columns if col not in batch_data.columns]

    if missing_columns:
        st.warning(f"Missing required columns in CSV: {missing_columns}")
        st.info(f"Required columns: {required_columns}")
    else:
        st.success("All required columns present!")

# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None and st.button("Predict Batch"):
    try:
        # Reset file pointer to beginning
        uploaded_file.seek(0)

        response = requests.post(
            "https://manasranjanpani-extraalearncustomerpredictionbackend.hf.space/v1/customersbatch",
            files={"file": uploaded_file}
        )

        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")

            # Display predictions in a nice format
            if 'predictions' in predictions:
                st.write("Predictions:")
                for i, pred in enumerate(predictions['predictions']):
                    st.write(f"Customer {i+1}: {pred:.2f}")
            else:
                # If predictions are keyed by ID
                predictions_df = pd.DataFrame(list(predictions.items()), columns=['Customer ID', 'Prediction'])
                st.dataframe(predictions_df)

        else:
            error_data = response.json()
            st.error(f"Error making batch prediction: {error_data.get('error', 'Unknown error')}")

    except requests.exceptions.RequestException as e:
        st.error(f"Connection error: {e}")
    except Exception as e:
        st.error(f"Unexpected error: {e}")

# Add information section
st.subheader("Check API Status")
if st.button("Check API Status"):
    try:
        response = requests.get("https://manasranjanpani-extraalearncustomerpredictionbackend.hf.space/ping")
        if response.status_code == 200:
            st.success("API is running and responsive")
        else:
            st.error("API is not responding properly")
    except:
        st.error("Cannot connect to API")