from __future__ import annotations
from datetime import datetime
import pandas as pd
import json
from pathlib import Path

from huggingface_hub import hf_hub_download, HfApi
import streamlit as st

from features import FEATURES
from utils import check_password, process_olivia_data


REPO_URL = "https://huggingface.co/datasets/trevolution/conversation-analytics-comments"
api = HfApi()


features_to_show = ['C: Missed Expectation : No Call Back/Follow Up',
 'C: Missed Expectation - Not Informed',
 'C: Missed Promises',
 'C: Repeat Contact - General/Other',
 'C: Repeat Contact - Previous Calls',
 'C: Repeat Information',
 'C: Agent Hanged Up',
 'C: Disputing Charge / Chargeback',
 'A: Transfer',
 'A: Transfer offer',
 'C: Channel Switch - Website',
 'C: Objection - Competitor - Switch',
 'C: Channel Switch - Webchat',
 'Escalation: External - Attorney General',
 'Escalation: External - BBB',
 'Escalation: External - Legal',
 'Escalation: Internal - Complaint',
 'Escalation: Internal - Corporate',
 'Escalation: Internal - Do Not Contact/Remove from list',
 'Escalation: Internal - Supervisor']


style = (
    'border: 1px solid #ccc; '
    'padding: 10px; '
    'border-radius: 5px; '
    'max-height: 500px; '  # Set your desired maximum height
    'overflow: auto;'     # Enable vertical scrollbar if content exceeds max height
)


def get_div(input):
    return f'<div style="{style}"><p>{input}</p></div>'


def main():

    if not check_password():
        st.stop()

    comments_path = hf_hub_download(
        repo_id='trevolution/conversation-analytics-comments',
        repo_type='dataset',
        filename='comments_report_3.json',
        token=st.secrets['WRITE_TOKEN'],
    )
    with open(comments_path, 'r') as f:
        comments = json.load(f)
    with open('transcriptions_report_3.json', 'r') as f:
        transcriptions = json.load(f)
    with open('analytics_report_3.json', 'r') as f:
        analytics = json.load(f)
    call_ids = [json.loads(_['metadata'])['call_id'] for _ in transcriptions]
    call_ids = list(sorted(list(set(call_ids))))
    
    st.title('Olivia - Agent - Conversation Analytics')
    call_id = st.selectbox(
        'Call IDs:',
        call_ids,
        format_func=lambda call_id: f'{call_ids.index(call_id) + 1}: {call_id}'
    )

    if not st.session_state.get('selectbox'):
        st.session_state['selectbox'] = call_id
    else:
        if call_id != st.session_state['selectbox']:
            st.session_state['analyze_button'] = False
            st.session_state['selectbox'] = call_id

    transcription = [json.loads(_['transcription']) for _ in transcriptions if json.loads(_['metadata'])['call_id'] == call_id][0]
    try:
        analytics = [json.loads(_['analytics']) for _ in analytics if call_id == json.loads(_['metadata'])['call_id']][0]
        analytics = analytics['analytics']
        analytics = [f for f in analytics if f['name'] in features_to_show]
    except:
        analytics = None

    st.audio(f'{call_id}.ogg', format='audio/ogg')

    analyze_button = st.button("Get Conversation Analytics")
    if not st.session_state.get('analyze_button'):
        st.session_state['analyze_button'] = analyze_button

    if st.session_state['selectbox'] and st.session_state['analyze_button']:
        conversation = process_olivia_data(transcription)
        st.text('Conversation (Olivia Speech-to-Text):')
        st.markdown(get_div(conversation['text']), unsafe_allow_html=True)
 
        with st.spinner('Loading analytics...'):
            st.text('Analytics')
            readable_analytics = ''
            for i, feature in enumerate(analytics):
                readable_analytics += f"{i+1}. {feature['name']}: {feature['response']}. Quotation: {feature['quotation']}\n\n\n"
            st.markdown(get_div(readable_analytics), unsafe_allow_html=True)


    if "saved_comments" not in st.session_state:
        st.session_state['saved_comments'] = ""

    user_comments = st.text_area(f"Comments on {call_id}", key='user_comments', height=350)

    def submit():
        st.session_state['saved_comments'] = st.session_state['user_comments']
        st.session_state['user_comments'] = ""

    button = st.button("Save comments", on_click=submit)
    if button and st.session_state['saved_comments']:

        if call_id in comments:
            comments[call_id].append(
                {
                    'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                    'text': st.session_state['saved_comments']
                }
            )
        else:
            comments[call_id] = [
                {
                    'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                    'text': st.session_state['saved_comments']
                }
            ]

        api.upload_file(
            path_or_fileobj=json.dumps(comments).encode('utf-8'),
            path_in_repo="comments_report_3.json",
            repo_id="trevolution/conversation-analytics-comments",
            repo_type="dataset",
            token=st.secrets['WRITE_TOKEN'],
            commit_message=f"{call_id}_{datetime.now().strftime('%Y-%m-%d')}"
        )
        st.success("Saved")

    if comments.get(call_id):
        value = ''
        for comment in comments.get(call_id):
            value += f"{comment['timestamp']}: {comment['text']}\n"
        st.text_area(label='Comments:', value=value, disabled=True, height=350)
    else:
        st.text_area(label='Comments:', value="No comments exist at the moment", disabled=True)


if __name__ == "__main__":
    main()