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import streamlit as st
from datetime import datetime, timedelta
from collections import defaultdict, Counter
from llama_index.llms.openai import OpenAI
from composio_llamaindex import ComposioToolSet, App, Action
import os
import json
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
class CalendarService:
def __init__(self):
self.toolset = ComposioToolSet(api_key=os.getenv('COMPOSIO_API_KEY'))
self.connection_request = None
# [Previous CalendarService methods remain the same]
def analyze_calendar_events(self, response_data):
"""
Analyze calendar events and return statistics about meetings.
"""
current_year = datetime.now().year
meetings = []
participants = []
meeting_times = []
total_duration = timedelta()
monthly_meetings = defaultdict(int)
daily_meetings = defaultdict(int)
events = response_data.get('data', {}).get('event_data', {}).get('event_data', [])
for event in events:
start_data = event.get('start', {})
end_data = event.get('end', {})
try:
start = datetime.fromisoformat(start_data.get('dateTime').replace('Z', '+00:00'))
end = datetime.fromisoformat(end_data.get('dateTime').replace('Z', '+00:00'))
if start.year == current_year:
duration = end - start
total_duration += duration
monthly_meetings[start.strftime('%B')] += 1
daily_meetings[start.strftime('%A')] += 1
meeting_times.append(start.strftime('%H:%M'))
if 'attendees' in event:
for attendee in event['attendees']:
if attendee.get('responseStatus') != 'declined':
participants.append(attendee.get('email'))
organizer_email = event.get('organizer', {}).get('email')
if organizer_email:
participants.append(organizer_email)
meetings.append({
'start': start,
'duration': duration,
'summary': event.get('summary', 'No Title')
})
except (ValueError, TypeError, AttributeError) as e:
st.error(f"Error processing event: {e}")
continue
total_meetings = len(meetings)
stats = {
"total_meetings_this_year": total_meetings
}
if total_meetings > 0:
stats.update({
"total_time_spent": str(total_duration),
"busiest_month": max(monthly_meetings.items(), key=lambda x: x[1])[0] if monthly_meetings else "N/A",
"busiest_day": max(daily_meetings.items(), key=lambda x: x[1])[0] if daily_meetings else "N/A",
"most_frequent_participant": Counter(participants).most_common(1)[0][0] if participants else "N/A",
"average_meeting_duration": str(total_duration / total_meetings),
"most_common_meeting_time": Counter(meeting_times).most_common(1)[0][0] if meeting_times else "N/A",
"monthly_breakdown": dict(monthly_meetings),
"daily_breakdown": dict(daily_meetings)
})
else:
stats.update({
"total_time_spent": "0:00:00",
"busiest_month": "N/A",
"busiest_day": "N/A",
"most_frequent_participant": "N/A",
"average_meeting_duration": "0:00:00",
"most_common_meeting_time": "N/A",
"monthly_breakdown": {},
"daily_breakdown": {}
})
return stats
def initiate_connection(self, entity_id: str, redirect_url: str = "https://calendar-wrapped-eight.vercel.app/") -> dict:
try:
self.connection_request = self.toolset.initiate_connection(
entity_id=entity_id,
app=App.GOOGLECALENDAR,
)
return {
'success': True,
'data': {
'redirect_url': self.connection_request.redirectUrl,
'message': "Please authenticate using the provided link."
}
}
except Exception as e:
return {
'success': False,
'error': str(e)
}
def check_connection_status(self, entity_id: str) -> dict:
try:
entity_id = self.toolset.get_entity(id=entity_id)
connection = entity_id.get_connection(app=App.GOOGLECALENDAR)
status = connection.status
return {
'success': True,
'data': {
'status': status,
'message': f"Connection status: {status}"
}
}
except Exception as e:
return {
'success': False,
'error': str(e)
}
def generate_wrapped(self, entity_id: str) -> dict:
try:
current_year = datetime.now().year
request_params = {
"calendar_id": "primary",
"timeMin": f"{current_year},1,1,0,0,0",
"timeMax": f"{current_year},12,31,23,59,59",
"single_events": True,
"max_results": 2500,
"order_by": "startTime"
}
events_response = self.toolset.execute_action(
action=Action.GOOGLECALENDAR_FIND_EVENT,
params=request_params,
entity_id=entity_id
)
if events_response["successfull"]:
stats = self.analyze_calendar_events(events_response)
llm = OpenAI(model='gpt-4', api_key=os.getenv('OPENAI_API_KEY'))
billionaire_prompt = f"""Based on these calendar stats, which tech billionaire's schedule does this most resemble and why?
Stats:
- {stats['total_meetings_this_year']} total meetings
- {stats['total_time_spent']} total time in meetings
- Most active on {stats['busiest_day']}s
- Busiest month is {stats['busiest_month']}
- Average meeting duration: {stats['average_meeting_duration']}
Suggest a different billionaire each time, dont say elon.
Return as JSON with format: {{"name": "billionaire name", "reason": "explanation"}}
"""
stats_prompt = f"""Analyze these calendar stats and write a brief, insightful one-sentence comment for each metric:
- Total meetings: {stats['total_meetings_this_year']}
- Total time in meetings: {stats['total_time_spent']}
- Busiest month: {stats['busiest_month']}
- Busiest day: {stats['busiest_day']}
- Average meeting duration: {stats['average_meeting_duration']}
- Most common meeting time: {stats['most_common_meeting_time']}
- Most frequent participant: {stats['most_frequent_participant']}
Return as JSON with format: {{"total_meetings_comment": "", "time_spent_comment": "", "busiest_times_comment": "", "collaborator_comment": "", "habits_comment": ""}}
"""
try:
billionaire_response = json.loads(llm.complete(billionaire_prompt).text)
stats_comments = json.loads(llm.complete(stats_prompt).text)
stats["schedule_analysis"] = billionaire_response
stats["metric_insights"] = stats_comments
except Exception as e:
st.error(f"Error processing LLM responses: {e}")
stats["schedule_analysis"] = {"name": "Unknown", "reason": "Analysis unavailable"}
stats["metric_insights"] = {
"total_meetings_comment": "",
"time_spent_comment": "",
"busiest_times_comment": "",
"collaborator_comment": "",
"habits_comment": ""
}
return {
'success': True,
'data': stats
}
else:
return {
'success': False,
'error': events_response.get("error", "Failed to fetch calendar events")
}
except Exception as e:
return {
'success': False,
'error': str(e)
}
def main():
st.set_page_config(page_title="Calendar Wrapped", layout="wide")
st.title("Calendar Wrapped")
service = CalendarService()
tab1, tab2, tab3 = st.tabs(["Connect", "Check Status", "Generate Wrapped"])
with tab1:
st.header("Initialize Connection")
entity_id = st.text_input("Entity ID")
redirect_url = st.text_input("Redirect URL", value="https://calendar-wrapped-eight.vercel.app/")
if st.button("Initialize Connection"):
if entity_id:
result = service.initiate_connection(entity_id, redirect_url)
if result['success']:
st.success(result['data']['message'])
st.markdown(f"[Click here to authenticate]({result['data']['redirect_url']})")
else:
st.error(f"Error: {result['error']}")
else:
st.warning("Please enter an Entity ID")
with tab2:
st.header("Check Connection Status")
status_entity_id = st.text_input("Entity ID", key="status_entity_id")
if st.button("Check Status"):
if status_entity_id:
result = service.check_connection_status(status_entity_id)
if result['success']:
st.success(result['data']['message'])
else:
st.error(f"Error: {result['error']}")
else:
st.warning("Please enter an Entity ID")
with tab3:
st.header("Generate Calendar Wrapped")
wrapped_entity_id = st.text_input("Entity ID", key="wrapped_entity_id")
if st.button("Generate Wrapped"):
if wrapped_entity_id:
with st.spinner("Generating your Calendar Wrapped..."):
result = service.generate_wrapped(wrapped_entity_id)
if result['success']:
data = result['data']
# Display basic stats
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Total Meetings", data['total_meetings_this_year'])
with col2:
st.metric("Total Time in Meetings", data['total_time_spent'])
with col3:
st.metric("Average Meeting Duration", data['average_meeting_duration'])
# Display schedule analysis
st.subheader("Schedule Analysis")
if data.get('schedule_analysis'):
st.write(f"Your schedule most resembles: **{data['schedule_analysis']['name']}**")
st.write(f"*{data['schedule_analysis']['reason']}*")
# Display insights
st.subheader("Insights")
if data.get('metric_insights'):
insights = data['metric_insights']
for key, value in insights.items():
if value: # Only display non-empty insights
st.write(f"- {value}")
# Display breakdowns
col1, col2 = st.columns(2)
with col1:
st.subheader("Monthly Breakdown")
st.bar_chart(data['monthly_breakdown'])
with col2:
st.subheader("Daily Breakdown")
st.bar_chart(data['daily_breakdown'])
else:
st.error(f"Error: {result['error']}")
else:
st.warning("Please enter an Entity ID")
if __name__ == "__main__":
main() |