import streamlit as st
import pandas as pd
from datetime import datetime
from inference import Inference


class QueryInputForm:
    def __init__(self):
        # Title of the Streamlit form
        st.title("E-Commerce Recommendation Engine Demo")

        # Predefined options for channel and device type
        self.channel_options = [
            'Paid Social', 'Paid Search - Brand', 'Organic', 'Email - Transactional',
            'Affiliate', 'Paid Search', 'Direct', 'Referral', 'Email - Marketing',
            'Paid Search - Brand Reactivation', 'SMS - Marketing', 'Email - Trigger',
            'Referral - Whitelabel', 'Referral - Merchant', 'Social', 'SMS - Trigger',
        ]
        
        self.device_type_options = [
            'Mobile', 'Desktop', 'Phablet', 'Tablet', '', 'TV',
            'Portable Media Player', 'Wearable',
        ]
        
        # Default values for the form
        self.default_query_text = "pizza"

        # Initialize the recommender engine
        self.recommender_engine = Inference()

    def display_form(self):
        # Input fields for user ID, channel, device type, and query text
        self.channel = st.selectbox("Channel", options=self.channel_options)
        self.device_type = st.selectbox("Device Type", options=self.device_type_options)
        self.query_text = st.text_input("Query Text", value=self.default_query_text)

        # Submit button
        if st.button("Submit"):
            self.submit()

    def submit(self):
        # Pass the query information to the recommender engine
        raw_query = {
            'user_id': "new_user", # any user will be considered as a new user
            'channel': self.channel,
            'device_type': self.device_type,
            'query_text': self.query_text,
            'time': datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), # querytime
        }

        # Get recommendations
        self.recommender_engine.get_recommendations(raw_query)
        self.display_recommendations()

    def display_recommendations(self):
        # Output the recommendations from the inference engine
        st.write("Top recommendations:")
        st.dataframe(self.recommender_engine.recommendations, hide_index=True)


if __name__ == "__main__":
    form = QueryInputForm()
    form.display_form()