Update app.py

app.py

@@ -174,86 +174,86 @@ class CalendarService:
             )
     
     def generate_wrapped(self, entity_id: str) -> APIResponse:
-    try:
-        # 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"
-        
-        request_params = {
-            "calendar_id": "primary",
-            "timeMin": time_min,
-            "timeMax": time_max,
-            "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)
-            
-            # Get tech billionaire comparison
-            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']}
+        try:
+            # 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"
             
-            Return as JSON with format: {{"name": "billionaire name", "reason": "explanation"}}
-            """
+            request_params = {
+                "calendar_id": "primary",
+                "timeMin": time_min,
+                "timeMax": time_max,
+                "single_events": True,
+                "max_results": 2500,
+                "order_by": "startTime"
+            }
             
-            # Get comments for each stat
-            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": ""}}
-            """
+            events_response = self.toolset.execute_action(
+                action=Action.GOOGLECALENDAR_FIND_EVENT,
+                params=request_params,
+                entity_id=entity_id
+            )
             
-            # Make LLM calls
-            try:
-                billionaire_response = json.loads(llm.complete(billionaire_prompt))
-                stats_comments = json.loads(llm.complete(stats_prompt))
+            if events_response["successfull"]:
+                stats = self.analyze_calendar_events(events_response)
+                
+                # Get tech billionaire comparison
+                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']}
+                
+                Return as JSON with format: {{"name": "billionaire name", "reason": "explanation"}}
+                """
+                
+                # Get comments for each stat
+                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": ""}}
+                """
+                
+                # Make LLM calls
+                try:
+                    billionaire_response = json.loads(llm.complete(billionaire_prompt))
+                    stats_comments = json.loads(llm.complete(stats_prompt))
+                    
+                    # Add new fields to stats
+                    stats["schedule_analysis"] = billionaire_response
+                    stats["metric_insights"] = stats_comments
+                except (json.JSONDecodeError, Exception) as e:
+                    print(f"Error processing LLM responses: {e}")
+                    # Add empty defaults if LLM processing fails
+                    stats["schedule_analysis"] = {"name": "Unknown", "reason": "Analysis unavailable"}
+                    stats["metric_insights"] = {"total_meetings_comment": "", "time_spent_comment": "", 
+                                              "busiest_times_comment": "", "collaborator_comment": "", 
+                                              "habits_comment": ""}
                 
-                # Add new fields to stats
-                stats["schedule_analysis"] = billionaire_response
-                stats["metric_insights"] = stats_comments
-            except (json.JSONDecodeError, Exception) as e:
-                print(f"Error processing LLM responses: {e}")
-                # Add empty defaults if LLM processing fails
-                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 APIResponse(
+                    success=True,
+                    data=stats
+                )
+            else:
+                return APIResponse(
+                    success=False,
+                    error=events_response["error"] or "Failed to fetch calendar events"
+                )
             
-            return APIResponse(
-                success=True,
-                data=stats
-            )
-        else:
+        except Exception as e:
             return APIResponse(
                 success=False,
-                error=events_response["error"] or "Failed to fetch calendar events"
+                error=f"Failed to generate wrapped: {str(e)}"
             )
-        
-    except Exception as e:
-        return APIResponse(
-            success=False,
-            error=f"Failed to generate wrapped: {str(e)}"
-        )
 
 def create_gradio_api():
     service = CalendarService()