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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
def format_seconds(seconds: int) -> str:
if seconds == 1:
return "1 second"
elif seconds < 60:
return f"{seconds} seconds"
else:
minutes = seconds // 60
remaining_seconds = seconds % 60
if minutes == 1:
minute_str = "1 minute"
else:
minute_str = f"{minutes} minutes"
if remaining_seconds == 1:
second_str = "1 second"
else:
second_str = f"{remaining_seconds} seconds"
return f"{minute_str} {second_str}"
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',
'Voucher',
'Refund Voucher',
'Refund'
]
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_7.json',
token=st.secrets['WRITE_TOKEN'],
)
with open(comments_path, 'r') as f:
comments = json.load(f)
with open('transcriptions_report_7.json', 'r') as f:
transcriptions = json.load(f)
with open('analytics_report_7.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'data/{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):
if feature['timestamp']:
start_time, end_time = int(feature['timestamp'][0]), int(feature['timestamp'][1])
start_time, end_time = format_seconds(start_time), format_seconds(end_time)
readable_analytics += f"{i+1}. {feature['name']}: {feature['response']}. Quotation: {feature['quotation']}. Timestamp: {start_time}-{end_time}\n\n\n"
else:
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_7.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()
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