Update app.py

app.py

@@ -4,6 +4,7 @@ from typing import Optional, Dict, Any
 from composio_llamaindex import ComposioToolSet, App, Action
 from datetime import datetime, timedelta
 from collections import defaultdict, Counter
+from llama_index_llms_openai import OpenAI
 import gradio as gr
 import os
 import json
@@ -12,6 +13,8 @@ from dotenv import load_dotenv
 # Load environment variables
 load_dotenv()
 
+llm = OpenAI(model='gpt-4o', api_key=os.getenv('OPENAI_API_KEY'))
+
 class ConnectionStatus(Enum):
     PENDING = "pending"
     ACTIVE = "active"
@@ -114,36 +117,80 @@ class CalendarService:
         
         return stats
         
-    def initiate_connection(self, entity_id: str, redirect_url: Optional[str] = None) -> APIResponse:
+    def generate_wrapped(self, entity_id: str) -> APIResponse:
         try:
-            if not redirect_url:
-                redirect_url = "https://yourwebsite.com/connection/success"
-                
-            connection_request = self.toolset.initiate_connection(
-                entity_id=entity_id,
-                app=App.GOOGLECALENDAR
-            )
+            # Get current year's start and end dates
+            current_year = datetime.now().year
+            time_min = f"{current_year},1,1,0,0,0"
+            time_max = f"{current_year},12,31,23,59,59"
             
-            self.connections[entity_id] = {
-                'status': ConnectionStatus.PENDING.value,
-                'redirect_url': connection_request.redirectUrl,
-                'created_at': datetime.now().isoformat()
+            request_params = {
+                "calendar_id": "primary",
+                "timeMin": time_min,
+                "timeMax": time_max,
+                "single_events": True,
+                "max_results": 2500,
+                "order_by": "startTime"
             }
             
-            return APIResponse(
-                success=True,
-                data={
-                    'status': ConnectionStatus.PENDING.value,
-                    'redirect_url': connection_request.redirectUrl,
-                    'wait_time': 60,
-                    'message': "Please authenticate using the provided link."
-                }
+            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)
+                
+                # Create a prompt for the LLM with the stats
+                prompt = f"""Based on the following calendar statistics, analyze which tech billionaire this person's schedule most resembles and provide brief comments for each metric:
+
+Stats:
+- Total meetings this year: {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 collaborator: {stats['most_frequent_participant']}
+
+Please provide:
+1. Which tech billionaire's schedule this most resembles and why
+2. A one-sentence comment for each of the above metrics
+Format your response as JSON with keys: 'billionaire_match' and 'metric_comments'
+
+Dont make any extra comments before or after. Just give the required output and shut up. Dont send any sentences saying like here's your response, or the output is generated"""
+
+                llm_analysis = llm.complete(prompt)
+                
+                try:
+                    llm_json = json.loads(llm_analysis)
+                except json.JSONDecodeError:
+                    llm_json = {
+                        "billionaire_match": "Analysis unavailable",
+                        "metric_comments": "Comments unavailable"
+                    }
+                
+                # Add LLM analysis to stats
+                stats.update({
+                    "schedule_analysis": llm_json["billionaire_match"],
+                    "metric_insights": llm_json["metric_comments"]
+                })
+                
+                return APIResponse(
+                    success=True,
+                    data=stats
+                )
+            else:
+                return APIResponse(
+                    success=False,
+                    error=events_response["error"] or "Failed to fetch calendar events"
+                )
+            
         except Exception as e:
             return APIResponse(
                 success=False,
-                error=f"Failed to initiate connection: {str(e)}"
+                error=f"Failed to generate wrapped: {str(e)}"
             )
     
     def check_status(self, entity_id: str) -> APIResponse:
@@ -191,7 +238,7 @@ class CalendarService:
                 params=request_params,
                 entity_id=entity_id
             )
-            
+            llm_response = llm.complete(f"{str(events_response)} is the event response. Based on this what tech billionaire are they most similar to and why??")
             if events_response["successfull"]:
                 stats = self.analyze_calendar_events(events_response)
                 return APIResponse(