Aligned with staging

app/user.py

@@ -107,8 +107,7 @@ class User:
                     "day": 5,
                     "coachingTheme": "FINAL_SUMMARY_STATE"
                     }
-                ]
-            }
+                ]}
         
         
         self.growth_plan = CircularQueue(array=growth_plan['growthPlan'], user_id=self.user_id)        
@@ -189,7 +188,7 @@ class User:
     def add_recent_wins(self, wins, context = None):
         prompt = f"""
         ## Role
-        You are an expert in writing achievement message and progress notification. Your task is to use the user's achievement and context to formulate a short and creative achievement message/progress notification. The output must be a one sentence short message (less than 15 words) in this JSON output schema:
+        You are an expert in writing achievement message and progress notification. Your task is to use the user's achievement and context to formulate a short achievement message/progress notification. The output must be a one sentence short message (less than 15 words) in this JSON output schema:
 
         ```json
         {{
@@ -206,7 +205,7 @@ class User:
         Output:
         ```
         {{
-        achievement_message: You crushed it! Completing that 10k run is a huge milestone—way to go!
+        achievement_message: You have completed a 10k run!
         }}
         ```
 
@@ -692,6 +691,8 @@ class User:
 
         8. You **MUST** call the process_reminder() function if the user wants to postpone their micro-action
         
+        9. When talking about Growth Guide, you must try to upsell the growth guide. Meaning that you are always encouraging the user to book a Growth Guide session and explain it benefits. Be like the Growth Guide's salesman!
+
         Today's Theme:
         {formatted_message}
         """
@@ -794,7 +795,7 @@ class User:
         logger.info(f"Summarizing zoom ai summary", extra={"user_id": self.user_id, "endpoint": "summarize_zoom"})
         # make an api call to gpt4o to summarize the zoom_ai_summary and produce a text with a focus on the most amount of user insight and info extracted
         system_prompt = f"""You are an expert at summarizing AI-generated Zoom transcripts concisely, focusing on extracting key user insights to enhance personalization in future interactions. Note that the zoom ai transcript may get the user's name wrong. Replace it with the actual user's name: {self.user_info}. Refer to the coach/guide as 'the Growth Guide'."""
-        prompt = f"Please summarize the following AI-generated Zoom transcript **in one short paragraph only** !!, emphasizing the most significant user insights and information:\n\n{zoom_ai_summary}"
+        prompt = f"Please summarize the following AI-generated Zoom transcript **in one short paragraph only, around 50 completion tokens maximum** !!, emphasizing the most significant user insights and information:\n\n{zoom_ai_summary}"
 
         response = self.client.chat.completions.create(
             model="gpt-4o",
@@ -802,10 +803,32 @@ class User:
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": prompt}
             ],
-            temperature=0.2
+            response_format = {
+                "type": "json_schema",
+                "json_schema": {
+                "name": "summarized_overview",
+                "strict": True,
+                "schema": {
+                    "type": "object",
+                    "properties": {
+                    "summary": {
+                        "type": "string",
+                        "description": "The summary of the zoom transcript"
+                    }
+                    },
+                    "required": [
+                    "achievement_message"
+                    ],
+                    "additionalProperties": False
+                }
+                }
+            },
+            temperature=0.5
         )
-        logger.info(f"Summary: {response.choices[0].message.content}", extra={"user_id": self.user_id, "endpoint": "summarize_zoom"})
-        return {'overview': response.choices[0].message.content}
+
+        overview_summary = json.loads(response.choices[0].message.content)['summary']
+        logger.info(f"Summary: {overview_summary}", extra={"user_id": self.user_id, "endpoint": "summarize_zoom"})
+        return {'overview': overview_summary}
 
     @catch_error
     def _update_user_data(self, data_type, text_input, extra_text=""):
@@ -996,12 +1019,15 @@ class User:
         if completed_micro_action:
             self.micro_actions[-1].status = "COMPLETE"
             self.micro_actions[-1].updated_at = pd.Timestamp.now(tz='UTC').strftime("%d-%m-%Y %a %H:%M:%S")
+            logger.info("Micro action status updated, checking number of actions completed...", extra={"user_id": self.user_id, "endpoint": "update_micro_action_status"})
 
             num_of_micro_actions_completed = sum(1 for item in self.micro_actions if item.status == 'COMPLETE')
 
             if (num_of_micro_actions_completed in (1,3,5)) or (num_of_micro_actions_completed % 10 == 0 and num_of_micro_actions_completed != 0):
                 self.add_life_score_point(variable = self.get_current_goal(full=True).area, points_added = 10, notes = f"Completing the {num_of_micro_actions_completed}-th micro-action")
-                self.add_recent_wins(wins = "You have completed a micro action!", context= self.micro_actions[-1]['content'])
+                self.add_recent_wins(wins = "You have completed a micro action!", context= self.micro_actions[-1].content)
+                logger.info("Added life score points based on number of actions completed.", extra={"user_id": self.user_id, "endpoint": "update_micro_action_status"})
+        logger.info("Process done.", extra={"user_id": self.user_id, "endpoint": "update_micro_action_status"})
 
     @catch_error
     def trigger_deep_reflection_point(self, area_of_deep_reflection):