import streamlit as st
import pandas as pd
import requests

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

# Collect user input for property features
age                        = st.number_input("age", min_value=5, max_value=90, step=1, value=30)
website_visits             = st.number_input("website_visits", min_value=0, step=1, value=1)
time_spent_on_website      = st.number_input("time_spent_on_website", min_value=0, step=1, value=1)
page_views_per_visit       = st.number_input("page_views_per_visit", min_value=0, step=1, value=1)
current_occupation         = st.selectbox("current_occupation", ["Professional", "Student", "Unemployed"])
first_interaction          = st.selectbox("first_interaction", ["Mobile App", "Website"])
profile_completed          = st.selectbox("profile_completed", ["Medium", "High", "Low"])
last_activity              = st.selectbox("last_activity", ["Website Activity", "Email Activity", "Phone Activity"])
print_media_type1          = st.selectbox("print_media_type1", ["Yes", "No"])
print_media_type2          = st.selectbox("print_media_type2", ["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 DataFrame
input_data = pd.DataFrame([{
	'age'                       :  'age',
	'website_visits'            :  'website_visits',
	'time_spent_on_website'     :  'time_spent_on_website',
	'page_views_per_visit'      :  'page_views_per_visit',
	'current_occupation'        :  'current_occupation',
	'first_interaction'         :  'first_interaction',
	'profile_completed'         :  'profile_completed',
	'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'
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://<username>-<repo_id>.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Price (in dollars)']
        st.success(f"Predicted Rental Price (in dollars): {prediction}")
    else:
        st.error("Error making prediction.")

# 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"])

# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
    if st.button("Predict Batch"):
        response = requests.post("https://jackfroooot-AssignmentExtraaLearnBackend.hf.space/v1/rentalbatch", files={"file": uploaded_file})  # Send file to Flask API
        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.write(predictions)  # Display the predictions
        else:
            st.error("Error making batch prediction.")