diff --git "a/app/utils.py" "b/app/utils.py"
--- "a/app/utils.py"
+++ "b/app/utils.py"
@@ -1,13 +1,13 @@
import logging
import boto3
-from botocore.exceptions import NoCredentialsError, PartialCredentialsError
+from botocore.exceptions import ClientError, NoCredentialsError, PartialCredentialsError
import os
from dotenv import load_dotenv
-from fastapi import HTTPException, Security, Query, status
+from fastapi import FastAPI, HTTPException, Security, Query, status
from fastapi.security import APIKeyHeader
from openai import OpenAI
-import openai
import pandas as pd
+from pydantic import BaseModel
import os
import logging
import json
@@ -15,16 +15,12 @@ import psycopg2
from psycopg2 import sql
import os
from dotenv import load_dotenv
-from datetime import datetime, timezone
+import datetime
+import threading
import pickle # Replace dill with pickle
-import uuid
-
-import pytz
-from app.cache import CustomTTLCache, upload_file_to_s3
-import pdfkit
-import PyPDF2
-
-from app.exceptions import BaseOurcoachException, DBError, OpenAIRequestError, UtilsError
+from cachetools import TTLCache
+import threading
+import time
load_dotenv()
@@ -40,316 +36,124 @@ REGION = os.getenv('AWS_REGION')
logger = logging.getLogger(__name__)
+class AutoSaveTTLCache(TTLCache):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.lock = threading.Lock()
+
+ def pop(self, key, *args):
+ with self.lock:
+ if key in self:
+ value = self[key]
+ # Save to S3 before removing
+ filename = f'{key}.pkl'
+ try:
+ # value.save_user()
+ upload_file_to_s3(filename)
+ logger.info(f"User {key} saved to S3 during cache eviction",
+ extra={'user_id': key, 'endpoint': 'cache_eviction'})
+ except Exception as e:
+ logger.error(f"Failed to save user {key} to S3 during cache eviction: {e}",
+ extra={'user_id': key, 'endpoint': 'cache_eviction'})
+ return super().pop(key, *args)
+
# Replace the simple TTLCache with our custom implementation
-user_cache = CustomTTLCache(ttl=120, cleanup_interval=30) # 2 minutes TTL
+user_cache = AutoSaveTTLCache(maxsize=100, ttl=1200) # 20 minutes TTL
-def catch_error(func):
- def wrapper(*args, **kwargs):
- try:
- return func(*args, **kwargs)
- except BaseOurcoachException as e:
- raise e
- except openai.BadRequestError as e:
- raise OpenAIRequestError(user_id='no-user', message="Bad Request to OpenAI", code="OpenAIError")
- except Exception as e:
- # Handle other exceptions
- logger.error(f"An unexpected error occurred in Utils: {e}")
- raise UtilsError(user_id='no-user', message="Unexpected error in Utils", e=str(e))
- return wrapper
-
-@catch_error
def force_file_move(source, destination):
function_name = force_file_move.__name__
logger.info(f"Attempting to move file from {source} to {destination}", extra={'endpoint': function_name})
- # Ensure the destination directory exists
- os.makedirs(os.path.dirname(destination), exist_ok=True)
-
- # Move the file, replacing if it already exists
- os.replace(source, destination)
- logger.info(f"File moved successfully: {source} -> {destination}", extra={'endpoint': function_name})
+ try:
+ # Ensure the destination directory exists
+ os.makedirs(os.path.dirname(destination), exist_ok=True)
+
+ # Move the file, replacing if it already exists
+ os.replace(source, destination)
+ logger.info(f"File moved successfully: {source} -> {destination}", extra={'endpoint': function_name})
+ except FileNotFoundError:
+ logger.error(f"Source file not found: {source}", extra={'endpoint': function_name})
+ except Exception as e:
+ logger.error(f"An error occurred while moving file: {e}", extra={'endpoint': function_name})
-@catch_error
def get_user(user_id):
function_name = get_user.__name__
logger.info(f"Fetching user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- logger.info(f"[CACHE]: {user_cache}", extra={'user_id': user_id, 'endpoint': function_name})
- if user_id in user_cache:
- logger.info(f"User {user_id} found in cache", extra={'user_id': user_id, 'endpoint': function_name})
- return user_cache[user_id]
- else:
- client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
- if not client:
- raise OpenAIRequestError(user_id=user_id, message="Error creating OpenAI client", code="OpenAIError")
- user_file = os.path.join('users', 'data', f'{user_id}.pkl')
- # if os.path.exists(user_file):
- # with open(user_file, 'rb') as f:
- # user = pickle.load(f)
- # user.client = client
- # user.conversations.client = client
- # with cache_lock:
- # user_cache[user_id] = user
- # return user
- logger.warning(f"User {user_id} not found locally. Attempting to download from S3", extra={'user_id': user_id, 'endpoint': function_name})
- download = download_file_from_s3(f'{user_id}.pkl', 'core-ai-assets')
- logger.info(f"Download success: {download}", extra={'user_id': user_id, 'endpoint': function_name})
- if (download):
- with open(user_file, 'rb') as f:
- user = pickle.load(f)
- user.client = client
- user.conversations.client = client
- user_cache[user_id] = user # No need for lock here
- os.remove(user_file)
- logger.info(f"User {user_id} loaded successfully from S3", extra={'user_id': user_id, 'endpoint': function_name})
- return user
- else:
- logger.error(f"User {user_id} pickle does not exist in S3", extra={'user_id': user_id, 'endpoint': function_name})
- # check if user_info exists
- user_info = get_user_info(user_id)
- if (user_info):
- # user has done onboarding but pickle file not created
- raise DBError(user_id=user_id, message="User has done onboarding but pickle file not created", code="NoPickleError")
- raise DBError(user_id=user_id, message="User has not onboarded yet", code="NoOnboardingError")
-
-@catch_error
-def generate_html(json_data, coach_name='Growth Guide', booking_id = None):
- function_name = generate_html.__name__
- data = json_data["pre_growth_guide_session_report"]
- user_overview = data["user_overview"]
- personality_insights = data["personality_insights"]
- progress_snapshot = data["progress_snapshot"]
- preparation_brief = json_data.get("users_growth_guide_preparation_brief", [])
- session_script = json_data["30_minute_coaching_session_script"]
-
- # Extract user name
- user_name = user_overview["name"]
-
- # Build Progress Snapshot
- progress_items = ""
- for key, value in progress_snapshot.items():
- # Convert key to title case with spaces
- formatted_key = key.replace("_", " ").title()
- progress_items += f'{formatted_key}: {value}\n'
-
- # Build Personality Insights
- love_languages = "".join(f"{lang}" for lang in personality_insights["top_love_languages"])
-
- # Build Preparation Brief
- preparation_items = "".join(
- f'{item["key"].replace("_", " ").title()}: {item["value"]}\n'
- for item in preparation_brief)
-
- # Build Session Overview
- session_overview_list = session_script["session_overview"]
- session_overview = "\n"
- for item in session_overview_list:
- session_overview += f"- {item}
\n"
- session_overview += "
"
-
- # Build Detailed Segments
- detailed_segments = ""
- for segment in session_script["detailed_segments"]:
- segment_title = segment["segment_title"]
-
- # Build Coach Dialogue list
- coach_dialogue_list = segment.get("coach_dialogue", [])
- coach_dialogue_html = "\n"
- for dialogue in coach_dialogue_list:
- coach_dialogue_html += f"- {dialogue}
\n"
- coach_dialogue_html += "
"
-
- # Build Guidance list
- guidance_list = segment.get("guidance", [])
- guidance_html = "\n"
- for guidance_point in guidance_list:
- guidance_html += f"- {guidance_point}
\n"
- guidance_html += "
"
-
- detailed_segments += f'''
-
-
{segment_title}
-
Coach Dialogue:{coach_dialogue_html}
-
Guidance:{guidance_html}
-
-
User Profile - {user_name}
-
-
User Overview
-
- - Name: {user_overview["name"]}
- - Age Group: {user_overview["age_group"]}
- - Primary Goals: {user_overview["primary_goals"]}
- - Preferred Coaching Style: {user_overview["preferred_coaching_style"]}
-
-
-
Personality Insights
-
- - MBTI: {personality_insights["mbti"]}
- - Top Love Languages:
-
- {love_languages}
-
-
- - Belief in Astrology: {personality_insights["belief_in_astrology"]}
-
-
-
Progress Snapshot
-
-
-
30-Minute Coaching Session Script
-
- Session Overview (30 Minutes)
- {session_overview}
-
- Detailed Segments
- {detailed_segments}
-
-
-
-
-
-
- '''
-
- file_path = os.path.join("bookings", "data",f"{booking_id}.html")
- path_to_upload = os.path.join("bookings", "to_upload",f"{booking_id}.pdf")
- password = "Ourcoach2024!"
-
- ## SAVING HTML FILE
- # Open the file in write mode
- with open(file_path, 'w', encoding='utf-8') as html_file:
- html_file.write(html_content)
- logger.info(f"File '{booking_id}.html' has been created successfully.", extra={'booking_id': booking_id, 'endpoint': function_name})
-
- # Saving as PDF File
- pdfkit.from_file(file_path, path_to_upload, options={'encoding': 'UTF-8'})
- logger.info(f"File '{booking_id}.pdf' has been created successfully.", extra={'booking_id': booking_id, 'endpoint': function_name})
-
- ## ENCRYPTING PDF
- logger.info(f"Encrypting '{booking_id}.pdf'...", extra={'booking_id': booking_id, 'endpoint': function_name})
- with open(path_to_upload, 'rb') as file:
- pdf_reader = PyPDF2.PdfReader(file)
- pdf_writer = PyPDF2.PdfWriter()
-
- # Add all pages to the writer
- for page_num in range(len(pdf_reader.pages)):
- pdf_writer.add_page(pdf_reader.pages[page_num])
-
- # Encrypt the PDF with the given password
- pdf_writer.encrypt(password)
-
- with open(path_to_upload, 'wb') as encrypted_file:
- pdf_writer.write(encrypted_file)
- logger.info(f"Succesfully encrypted '{booking_id}.pdf'", extra={'booking_id': booking_id, 'endpoint': function_name})
-
- filename = booking_id
-
- logger.info(f"Uploading file {filename} to S3", extra={'booking_id': booking_id, 'endpoint': function_name})
- bucket = 'core-ai-assets'
try:
- if (AWS_ACCESS_KEY and AWS_SECRET_KEY):
- session = boto3.session.Session(aws_access_key_id=AWS_ACCESS_KEY, aws_secret_access_key=AWS_SECRET_KEY, region_name=REGION)
- else:
- session = boto3.session.Session()
- s3_client = session.client('s3')
- with open(path_to_upload, "rb") as f:
- ## Upload to Production Folder
- s3_client.upload_fileobj(f, bucket, f'dev/pre_gg_reports/{filename}.pdf')
- logger.info(f"File {filename} uploaded successfully to S3", extra={'booking_id': booking_id, 'endpoint': function_name})
-
- # Removing files
- for file in os.listdir(os.path.join('bookings', 'data')):
- os.remove(os.path.join('bookings', 'data', file))
- for file in os.listdir(os.path.join('bookings', 'to_upload')):
- os.remove(os.path.join('bookings', 'to_upload', file))
+ if user_id in user_cache:
+ logger.info(f"User {user_id} found in cache", extra={'user_id': user_id, 'endpoint': function_name})
+ return user_cache[user_id]
+ except KeyError:
+ # TTLCache automatically removes expired entries
+ pass
- # force_file_move(os.path.join('users', 'to_upload', filename), os.path.join('users', 'data', filename))
- except (FileNotFoundError, NoCredentialsError, PartialCredentialsError) as e:
- raise DBError(user_id="no-user", message="Error uploading file to S3", code="S3Error")
+ client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+ user_file = os.path.join('users', 'data', f'{user_id}.pkl')
+ # if os.path.exists(user_file):
+ # with open(user_file, 'rb') as f:
+ # user = pickle.load(f)
+ # user.client = client
+ # user.conversations.client = client
+ # with cache_lock:
+ # user_cache[user_id] = user
+ # return user
+ logger.warning(f"User {user_id} not found locally. Attempting to download from S3", extra={'user_id': user_id, 'endpoint': function_name})
+ download = download_file_from_s3(f'{user_id}.pkl', 'core-ai-assets')
+ logger.info(f"Download success: {download}", extra={'user_id': user_id, 'endpoint': function_name})
+ if (download):
+ with open(user_file, 'rb') as f:
+ user = pickle.load(f)
+ user.client = client
+ user.conversations.client = client
+ user_cache[user_id] = user # No need for lock here
+ os.remove(user_file)
+ logger.info(f"User {user_id} loaded successfully from S3", extra={'user_id': user_id, 'endpoint': function_name})
+ return user
+ else:
+ logger.error(f"User {user_id} pickle does not exist in S3", extra={'user_id': user_id, 'endpoint': function_name})
+ # check if user_info exists
+ user_info = get_user_info(user_id)
+ if (user_info):
+ # user has done onboarding but pickle file not created
+ raise ReferenceError(f"User {user_id} pickle still being created")
+ raise LookupError(f"User [{user_id}] has not onboarded yet")
+
+def get_life_score(user_id):
+ function_name = get_life_score.__name__
+ logger.info(f"Generating user life score for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
-@catch_error
-def get_user_summary(user_id, update_rec_topics=False):
+ # Step 1: Call get_user to get user's info
+ try:
+ user = get_user(user_id)
+ # user_info = user.user_info
+ # user_messages = user.get_messages()
+ ## all the life scores here
+
+ life_score = {
+ "personal_growth_score": user.personal_growth_score,
+ "career_growth_score": user.career_growth_score,
+ "relationship_score": user.relationship_score,
+ "mental_well_being_score": user.mental_well_being_score,
+ "health_and_wellness_score": user.health_and_wellness_score
+ }
+ except LookupError as e:
+ logger.error(f"Error fetching user data: {e}", extra={'user_id': user_id, 'endpoint': function_name})
+ raise e
+
+ return life_score
+
+def get_user_summary(user_id):
function_name = get_user_summary.__name__
logger.info(f"Generating user summary for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
# Step 1: Call get_user to get user's info
- user = get_user(user_id)
- user_info = user.user_info
- user_messages = user.get_messages()
- user_goal = '' if not user.goal else user.goal[-1].content
+ try:
+ user = get_user(user_id)
+ user_info = user.user_info
+ user_messages = user.get_messages()
+ except LookupError as e:
+ logger.error(f"Error fetching user data: {e}", extra={'user_id': user_id, 'endpoint': function_name})
+ raise e
# Step 2: Construct the Prompt
chat_history = "\n".join(
@@ -366,9 +170,6 @@ def get_user_summary(user_id, update_rec_topics=False):
3. **30-Minute Coaching Session Script**: A detailed, partitioned script to help the coach prepare for the session, including dialogue, questions, and guidance tailored to the client's needs, covering the five key areas. The script should be partitioned into several sections in the JSON output, similar to the structure provided for the Pre-Growth Guide Session Report.
- ---
- **Important Note**
- The **chat history** shows the most updated information. Hence, if there is a difference between the goal/challenge/other key information in the user's chat history and the user's profile, you must create the reports based on the chat history!
---
**Instructions:**
@@ -420,52 +221,47 @@ def get_user_summary(user_id, update_rec_topics=False):
If any of the key areas are not discussed, include a note: "The user hasn't discussed this area yet. Maybe you can cover this during the Growth Guide session."
+ - **engagement_insights**:
+
+ - **daily_reflection_participation**: The user's engagement level with daily reflections.
+ - **weekly_streaks**: Any streaks or consistent patterns in the user's engagement.
+ - **recent_feedback**: Recent comments or feedback from the user highlighting current concerns or focus areas.
+
---
### **2. User's Growth Guide Preparation Brief**
**Objective**: Guide the user on what to discuss with the Growth Guide, providing actionable advice and highlighting key areas to focus on during their session, covering the five key areas.
- You must use the user's current **challenges** and **life goal** to make the preparation brief **personalized**! You **must** bold some words that you think is important! but it does **not** have to be the first few words!
-
- Important Rules:
- 1. **ALWAYS** be succinct, valuable and personalized! Do **NOT** ask generic question. Ask a personalized question! And bold the key parts of the user brief!
- 2. **Session Length Awareness**: Be realistic about what can be effectively discussed in a 30-minute session. Prioritize the areas that are most pressing or offer the greatest opportunity for positive change.
- 3. **Guidance for Interaction**: Provide specific suggestions for topics to discuss with the **Growth Guide**, you are encouraged to use phrases like "Discuss with your Growth Guide how to...".
- 4. And for the second time, please be succinct and concise!!!
- 5. You **must** bold some words that you think is important! but it does **not** have to be the first few words!
**Format**:
- Structure the brief with the following sections, and output it as a JSON object with these keys (don't forget to BE CONCISE! and you **must** bold some words that you think is important! but it does **not** have to be the first few words!):
+ Structure the brief with the following sections, and output it as a JSON object with these keys:
- - **reflect**: Provide personalized advice that encourages the user to contemplate their specific experiences, feelings, and thoughts related to each of the five key areas. Help them identify particular aspects they wish to improve, based on their challenges and goals.
+ - **reflect**: Encourage the user to consider what each focus area means to them and which aspects they want to improve.
- - **recall_successes**: Prompt the user to remember past occasions when they effectively managed or made improvements in these areas. Encourage them to consider the strategies, habits, or resources that contributed to these successes, and how they might apply them now.
+ - **recall_successes**: Prompt the user to think of times when they effectively managed or improved in the five key areas, and what strategies worked for them then.
- - **identify_challenges**: Advise the user to acknowledge current obstacles they are facing in each area. Encourage them to think critically about these challenges and consider potential solutions or support systems that could assist in overcoming them.
+ - **identify_challenges**: Encourage the user to be ready to discuss any obstacles they're facing in each area, and to consider possible solutions or resources that might help.
- - **set_goals**: Encourage the user to define clear and achievable objectives for the upcoming session. Guide them to consider how making improvements in each key area can positively impact their overall well-being and life satisfaction.
+ - **set_goals**: Ask the user to decide what they hope to achieve from the session and how improvements in each area will impact their life.
- - **additional_tips**: Offer practical advice to help the user prepare for the session. Suggestions may include arranging a quiet and comfortable space, gathering any relevant materials or notes, and approaching the session with openness and honesty.
+ - **additional_tips**: Provide practical advice for the session, such as choosing a quiet space, having materials ready, and being open to sharing thoughts honestly.
---
### **3. 30-Minute Coaching Session Script**
-
- **Objective**: Help the coach prepare for the session by providing a detailed, partitioned script tailored to the client's specific needs and goals, following a specific session order and focusing on the user's top three most important areas.
- **IMPORTANT**: BE VERY COMPREHENSIVE IN THE "GUIDANCE" SECTION OF DETAILED SEGMENT!!!
- **IMPORTANT**: NO NEED TO MENTION THE NAME OF THE COACH!!!
+ **Objective**: Help the coach prepare for the session by providing a detailed, partitioned script tailored to the client's specific needs and goals, following a specific session order and focusing on the user's top three most important areas.
**Instructions**:
- - **Session Overview (30 mins)**:
+ - **Session Order**:
The session should follow this specific order:
- 1. **Warm Welcome and Rapport Building** (10 mins)
+ 1. **Warm Welcome and Rapport Building** (5 mins)
2. **Exploring X Goals** (10 mins)
- 3. **Developing X Strategies** (5 mins)
+ 3. **Developing X Strategies** (10 mins)
4. **Wrap-Up and Commitment** (5 mins)
The "X" in "Exploring X Goals" and "Developing X Strategies" should be replaced with the user's top three most important areas from the five key areas. Focus on one area per session. If possible, prioritize the areas based on the user's expressed concerns or goals.
@@ -474,56 +270,47 @@ def get_user_summary(user_id, update_rec_topics=False):
For each segment, include:
- - **Numbered Title**: Number and title of the session segment (e.g., `1. Warm Welcome and Trust Building (10 Minutes)`).
-
- - **Coach Dialogue**: Provide the coach's dialogue for the segment, including initial statements, follow-up questions, and closing remarks. Present the dialogues as direct quotes, ensuring they align with the client's context and goals.
- In the coach dialogue, especially during the warm welcome session, you may ask opening question and mention disclaimers that include:
- - Opening question:
- To ask the user if there's anything he/she would like to talk about
- - Mention confidentiality:
- To tell the user that at ourcoach, we prioritize the privacy and confidentiality of our clients. All information shared during the coaching session will remain strictly confidential and used solely for your personal development.
- - What to expect from this session:
- To tell the user what can they expect from this session
- - Remind them that Zoom has recording turned on, so that they can receive an AI assisted report later:
- To tell the user to note that this session will be recorded on Zoom to provide you with a comprehensive AI-assisted report afterward. This report will include key takeaways and action steps to help you achieve your goals.
-
- And, in the coach dialogue during the "Exploring X Goals" session, you may ask the user if they have any other goals they want to explore, else if they don't, we can focus on the chosen goal!
+ - **segment_title**: Title of the session segment.
+ - **coach_dialogue**: Scripted prompts and questions the coach can use.
+ - **client_engagement**: Opportunities for the client to respond and reflect.
+ - **guidance**: Suggestions for the coach on how to navigate responses.
- And, in the coach dialogue during the "Wrap-Up and Commitment" session, based on today’s session, ask the user: Would you say that X is your biggest priority right now? Or are there any specific goals or areas you’d like to focus on in the coming weeks?
+ - **Additional Instructions**:
- - **Guidance**: Offer specific and comprehensive suggestions for the coach on how to navigate the session, including actionable points and strategies. Use bullet points to clearly present each guidance item.
- Note: For the "Plan Follow-up" part, it has to be next **month**
+ - Ensure that each of the user's top three areas is addressed in separate sessions. If any area was not previously discussed by the user, include guidance for the coach to introduce or explore that area during the session.
- - **Additional Instructions**:
+ - **Additional Tips for the Coach**:
+
+ Offer advice on tone, active listening, empathy, and techniques to encourage self-reflection and solution-focused thinking.
+
+ - **Action Plan Summary**:
+
+ Summarize the agreed-upon steps the client will take before the next session, covering the focused area.
+
+ - **Scheduling Next Session**:
- - Ensure that the **Coach Dialogue** is personalized and reflects the client's experiences and aspirations.
+ Include a prompt for confirming the next meeting time.
- - The **Guidance** should include actionable suggestions, emphasizing techniques like creating safety, setting expectations, building rapport, encouraging reflection, focusing on synergies, and action planning. Be very comprehensive in this part! And use tag to bold the headers of each guidance points/items!
+ - **End of Session**:
+
+ Conclude with a positive note, reinforcing confidence and expressing anticipation for the client's progress.
**Style Guidelines**:
- Use empathetic and supportive language.
-
- Encourage open-ended dialogue.
-
- Focus on actionable and achievable steps.
-
- Personalize the script to align with the client's experiences and aspirations.
- - Present information in a clear, organized manner, using numbering and bullet points where appropriate.
-
---
**Note**:
- If the user hasn't discussed one or more of the key areas, the outputs should note this and suggest that these areas can be covered during the Growth Guide session.
-
+
---
** JSON OUTPUT FORMAT EXAMPLE **:
-
- **IMPORTANT**: BE VERY COMPREHENSIVE IN THE "GUIDANCE" SECTION OF DETAILED SEGMENT!!!
- **IMPORTANT**: NO NEED TO MENTION THE NAME OF THE COACH!!!
{
"pre_growth_guide_session_report": {
@@ -544,54 +331,67 @@ def get_user_summary(user_id, update_rec_topics=False):
"relationships": "Feels disconnected from friends and family due to busy schedule; wishes to rebuild social connections.",
"career_growth": "Aiming for a promotion but feels uncertain about the necessary skills and how to stand out.",
"personal_growth": "Interested in learning new skills like photography and improving time management."
+ },
+ "engagement_insights": {
+ "daily_reflection_participation": "Consistently completes daily reflections, often focusing on stress levels and work-life balance.",
+ "weekly_streaks": "Maintained a 4-week streak of daily reflections and goal tracking.",
+ "recent_feedback": "\"I'm struggling to balance my work with my personal life and it's affecting my relationships.\""
}
},
"users_growth_guide_preparation_brief": [
{
"key": "reflect",
- "value": "..."
+ "value": "Consider what mental well-being means to you. Which aspects of your life—such as stress management, anxiety reduction, or emotional balance—do you want to improve?"
},
{
"key": "recall_successes",
- "value": "..."
+ "value": "Think of times when you effectively managed stress or maintained a positive mindset. What strategies or habits helped you during those times?"
},
{
"key": "identify_challenges",
- "value": "..."
+ "value": "Be ready to discuss current obstacles you're facing in managing stress and anxiety. Consider any patterns or triggers you've noticed."
},
{
"key": "set_goals",
- "value": "..."
+ "value": "Decide what you hope to achieve from this session. How would improving your mental well-being impact your daily life and long-term goals?"
},
{
"key": "additional_tips",
- "value": "..."
+ "value": "Environment: Choose a quiet, comfortable space.\nMaterials: Have a notebook and pen ready to jot down insights.\nOpenness: Be prepared to share your thoughts honestly and openly."
}
],
"30_minute_coaching_session_script": {
- "session_overview": ["Warm Welcome and Trust Building (10 Minutes)","Exploring Holistic Life Goals and Aspirations (10 Minutes)","Identifying Interconnections and Priorities (5 Minutes)","Wrap-Up and Next Steps (5 Minutes)"],
+ "session_overview": "1. Warm Welcome and Rapport Building (5 mins)\n2. Exploring Mental Well-being Goals (10 mins)\n3. Developing Mental Well-being Strategies (10 mins)\n4. Wrap-Up and Commitment (5 mins)",
"detailed_segments": [
{
- "segment_title": "1. Warm Welcome and Trust Building (10 Minutes)",
- "coach_dialogue": ["...","..."],
- "guidance": ["Create Safety: Reassure Yew Wai by emphasizing confidentiality.","Set Expectations: Clearly outline the session’s structure to provide clarity and ease.\nBuild Rapport: Show genuine curiosity about his recent experiences and emotions.\nValidation: Acknowledge his efforts with empathy, e.g., “That’s a lot to manage, but it’s incredible how committed you are to each aspect of your life.”]
+ "segment_title": "Warm Welcome and Rapport Building",
+ "coach_dialogue": "“Hello, Alex! It's great to see you today. How have you been feeling lately?”",
+ "client_engagement": "Allow Alex to share recent experiences and feelings.",
+ "guidance": "Listen attentively and acknowledge his feelings to build rapport."
},
{
- "segment_title": "2. Exploring Holistic Life Goals and Aspirations (10 Minutes)",
- "coach_dialogue": ["...","..."],
- "guidance": ["Encourage Reflection: Prompt Yew Wai to elaborate on his goals, covering areas like:","Career: Enhancing ourcoach user engagement and chat functionality.","Health: Preparing for the marathon and improving sleep.","Relationships: Nurturing his connection with Karina.","Personal Growth: Strengthening self-discipline.","Connect Goals: Highlight how goals may overlap, e.g., better sleep could enhance productivity at work.","Acknowledge Motivations: Reflect back his drivers for pursuing these goals, such as his desire for impact or balance."]
+ "segment_title": "Exploring Mental Well-being Goals",
+ "coach_dialogue": "“You've mentioned wanting to improve your mental well-being. Could you tell me more about what that means to you?”",
+ "client_engagement": "Encourage Alex to discuss specific aspects he wants to focus on.",
+ "guidance": "Ask open-ended questions to help him delve deeper into his goals."
},
{
- "segment_title": "3. Identifying Interconnections and Priorities (5 Minutes)",
- "coach_dialogue": ["...","..."],
- "guidance": ["Focus on Synergies: Show how one priority could impact other areas positively.","Example: A consistent morning routine could improve both health and work productivity.","Prioritize Actionable Areas: Help Yew Wai narrow his focus to one or two priorities.","Use Probing Questions: For example, “How could focusing on better sleep contribute to your overall energy and productivity?”"]
+ "segment_title": "Developing Mental Well-being Strategies",
+ "coach_dialogue": "“Let's explore some strategies to help manage your stress and improve your mental health. What ideas have you considered?”",
+ "client_engagement": "Allow him to suggest strategies; if needed, offer suggestions such as mindfulness techniques, time management, or setting boundaries.",
+ "guidance": "Collaborate to develop practical and achievable steps."
},
{
- "segment_title": "4. Wrap-Up and Next Steps (5 Minutes)",
- "coach_dialogue": ["...","..."],
- "guidance": ["Action Planning: Collaborate with Yew Wai to define specific actions, e.g.:","Scheduling a 30-minute morning routine.","Blocking focused hours for ourcoach work.","Planning a date night with Karina.","Encouragement: Reinforce the value of small, consistent steps. For example, “It’s incredible how even small habits can create big changes over time.”","Plan Follow-Up: Suggest reconnecting in a month to reflect on progress.","Close Positively: End with a motivational statement, e.g., “You’re on a path to amazing things, and it’s inspiring to see your dedication.”"]
+ "segment_title": "Wrap-Up and Commitment",
+ "coach_dialogue": "“To summarize, you've decided to incorporate mindfulness meditation and set clear work-life boundaries, right?”",
+ "client_engagement": "Ensure accuracy and gauge his commitment level.",
+ "guidance": "Offer encouragement and address any concerns he might have."
}
- ]
+ ],
+ "additional_tips_for_the_coach": "Maintain an empathetic tone throughout the session. Use active listening to validate his feelings. Encourage self-reflection by asking open-ended questions.",
+ "action_plan_summary": "Alex will practice mindfulness meditation for 10 minutes daily and set a strict end time for work each day to improve work-life balance.",
+ "scheduling_next_session": "“Let's plan to meet again in two weeks to discuss your progress. Does that work for you?”",
+ "end_of_session": "“Thank you for sharing so openly today, Alex. I believe these steps will make a positive impact. I'm looking forward to hearing about your progress in our next session.”"
}
}
"""
@@ -599,308 +399,314 @@ def get_user_summary(user_id, update_rec_topics=False):
# Combine user information and chat history for context
user_context = f"""
Based on the following user profile and chat history, generate the required reports.
-
- **Important Note**
- The **chat history** shows the most updated information. Hence, if there is a difference between the goal/challenge/other key information in the user's chat history and the user's profile, you must create the reports based on the chat history!
- ### CHAT HISTORY ###
- {chat_history}
-
- ### USER GOAL ###
- {user_goal}
-
### USER PROFILE ###
{user_info}
-
+
+ ### CHAT HISTORY ###
+ {chat_history}
"""
# Step 3: Call the OpenAI API using the specified function
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
- response = client.chat.completions.create(
- model="gpt-4o",
- messages=[
- {
- "role": "system",
- "content": [
- {
- "type": "text",
- "text": system_prompt
- }
- ]
- },
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": user_context
- }
- ]
- }
- ],
- response_format={
- "type": "json_schema",
- "json_schema": {
- "name": "growth_guide_session",
- "strict": True,
- "schema": {
- "type": "object",
- "properties": {
- "pre_growth_guide_session_report": {
+ try:
+ response = client.chat.completions.create(
+ model="gpt-4o-mini",
+ messages=[
+ {
+ "role": "system",
+ "content": [
+ {
+ "type": "text",
+ "text": system_prompt
+ }
+ ]
+ },
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "text",
+ "text": user_context
+ }
+ ]
+ }
+ ],
+ response_format={
+ "type": "json_schema",
+ "json_schema": {
+ "name": "growth_guide_session",
+ "strict": True,
+ "schema": {
"type": "object",
- "description": "A comprehensive summary of the user's profile and life context for the Growth Guide.",
"properties": {
- "user_overview": {
+ "pre_growth_guide_session_report": {
"type": "object",
+ "description": "A comprehensive summary of the user's profile and life context for the Growth Guide.",
"properties": {
- "name": {
- "type": "string",
- "description": "The user's full name."
+ "user_overview": {
+ "type": "object",
+ "properties": {
+ "name": {
+ "type": "string",
+ "description": "The user's full name."
+ },
+ "age_group": {
+ "type": "string",
+ "description": "The user's age range (e.g., '30-39')."
+ },
+ "primary_goals": {
+ "type": "string",
+ "description": "The main goals the user is focusing on."
+ },
+ "preferred_coaching_style": {
+ "type": "string",
+ "description": "The coaching style the user prefers."
+ }
},
- "age_group": {
- "type": "string",
- "description": "The user's age range (e.g., '30-39')."
+ "required": ["name", "age_group", "primary_goals", "preferred_coaching_style"],
+ "additionalProperties": False
},
- "primary_goals": {
- "type": "string",
- "description": "The main goals the user is focusing on."
+ "personality_insights": {
+ "type": "object",
+ "properties": {
+ "mbti": {
+ "type": "string",
+ "description": "The user's Myers-Briggs Type Indicator personality type."
+ },
+ "top_love_languages": {
+ "type": "array",
+ "items": {
+ "type": "string"
+ },
+ "description": "A list of the user's top two love languages."
+ },
+ "belief_in_astrology": {
+ "type": "string",
+ "description": "Whether the user believes in horoscope/astrology."
+ }
},
- "preferred_coaching_style": {
- "type": "string",
- "description": "The coaching style the user prefers."
+ "required": ["mbti", "top_love_languages", "belief_in_astrology"],
+ "additionalProperties": False
+ },
+ "progress_snapshot": {
+ "type": "object",
+ "properties": {
+ "mental_well_being": {
+ "type": "string",
+ "description": "Summary of the user's mental well-being."
+ },
+ "physical_health_and_wellness": {
+ "type": "string",
+ "description": "Summary of the user's physical health and wellness."
+ },
+ "relationships": {
+ "type": "string",
+ "description": "Summary of the user's relationships."
+ },
+ "career_growth": {
+ "type": "string",
+ "description": "Summary of the user's career growth."
+ },
+ "personal_growth": {
+ "type": "string",
+ "description": "Summary of the user's personal growth."
+ }
+ },
+ "required": [
+ "mental_well_being",
+ "physical_health_and_wellness",
+ "relationships",
+ "career_growth",
+ "personal_growth"
+ ],
+ "additionalProperties": False
+ },
+ "engagement_insights": {
+ "type": "object",
+ "properties": {
+ "daily_reflection_participation": {
+ "type": "string",
+ "description": "The user's engagement level with daily reflections."
+ },
+ "weekly_streaks": {
+ "type": "string",
+ "description": "Any streaks or consistent patterns in the user's engagement."
+ },
+ "recent_feedback": {
+ "type": "string",
+ "description": "Recent comments or feedback from the user."
+ }
+ },
+ "required": ["daily_reflection_participation", "weekly_streaks", "recent_feedback"],
+ "additionalProperties": False
}
},
- "required": ["name", "age_group", "primary_goals", "preferred_coaching_style"],
+ "required": ["user_overview", "personality_insights", "progress_snapshot", "engagement_insights"],
"additionalProperties": False
},
- "personality_insights": {
- "type": "object",
- "properties": {
- "mbti": {
- "type": "string",
- "description": "The user's Myers-Briggs Type Indicator personality type."
- },
- "top_love_languages": {
+ "users_growth_guide_preparation_brief": {
"type": "array",
+ "description": "A brief guiding the user on what to discuss with the Growth Guide, providing actionable advice and highlighting key areas to focus on.",
"items": {
- "type": "string"
- },
- "description": "A list of the user's top two love languages."
+ "type": "object",
+ "properties": {
+ "key": {
+ "type": "string",
+ "description": "The section heading."
+ },
+ "value": {
+ "type": "string",
+ "description": "Content for the section."
+ }
},
- "belief_in_astrology": {
- "type": "string",
- "description": "Whether the user believes in horoscope/astrology."
+ "required": [
+ "key",
+ "value"
+ ],
+ "additionalProperties": False
}
},
- "required": ["mbti", "top_love_languages", "belief_in_astrology"],
- "additionalProperties": False
- },
- "progress_snapshot": {
+ "30_minute_coaching_session_script": {
"type": "object",
+ "description": "A detailed, partitioned script to help the coach prepare for the session, following the specified session order and focusing on the user's top three most important areas.",
"properties": {
- "mental_well_being": {
+ "session_overview": {
"type": "string",
- "description": "Summary of the user's mental well-being."
+ "description": "Breakdown of the session segments with time frames."
},
- "physical_health_and_wellness": {
- "type": "string",
- "description": "Summary of the user's physical health and wellness."
+ "detailed_segments": {
+ "type": "array",
+ "items": {
+ "type": "object",
+ "properties": {
+ "segment_title": {
+ "type": "string",
+ "description": "Title of the session segment."
+ },
+ "coach_dialogue": {
+ "type": "string",
+ "description": "Scripted prompts and questions the coach can use."
+ },
+ "client_engagement": {
+ "type": "string",
+ "description": "Opportunities for the client to respond and reflect."
+ },
+ "guidance": {
+ "type": "string",
+ "description": "Suggestions for the coach on how to navigate responses."
+ }
+ },
+ "required": ["segment_title", "coach_dialogue", "client_engagement", "guidance"],
+ "additionalProperties": False
},
- "relationships": {
- "type": "string",
- "description": "Summary of the user's relationships."
+ "description": "Detailed information for each session segment."
},
- "career_growth": {
+ "additional_tips_for_the_coach": {
"type": "string",
- "description": "Summary of the user's career growth."
+ "description": "Advice on tone, active listening, empathy, etc."
},
- "personal_growth": {
+ "action_plan_summary": {
"type": "string",
- "description": "Summary of the user's personal growth."
- }
- },
- "required": [
- "mental_well_being",
- "physical_health_and_wellness",
- "relationships",
- "career_growth",
- "personal_growth"
- ],
- "additionalProperties": False
- }
- },
- "required": ["user_overview", "personality_insights", "progress_snapshot"],
- "additionalProperties": False
- },
- "users_growth_guide_preparation_brief": {
- "type": "array",
- "description": "A brief guiding the user on what to discuss with the Growth Guide, providing actionable advice and highlighting key areas to focus on.",
- "items": {
- "type": "object",
- "properties": {
- "key": {
+ "description": "Summary of the agreed-upon steps the client will take."
+ },
+ "scheduling_next_session": {
"type": "string",
- "description": "The section heading."
+ "description": "Prompt for confirming the next meeting time."
},
- "value": {
+ "end_of_session": {
"type": "string",
- "description": "Content for the section."
+ "description": "Conclusion with a positive note."
}
},
"required": [
- "key",
- "value"
+ "session_overview",
+ "detailed_segments",
+ "additional_tips_for_the_coach",
+ "action_plan_summary",
+ "scheduling_next_session",
+ "end_of_session"
],
"additionalProperties": False
}
},
- "30_minute_coaching_session_script": {
- "type": "object",
- "description": "A detailed, partitioned script to help the coach prepare for the session, following the specified session order and focusing on the user's top three most important areas.",
- "properties": {
- "session_overview": {
- "type": "array",
- "items": {
- "type": "string"
- },
- "description": "Breakdown of the session segments with time frames."
- },
- "detailed_segments": {
- "type": "array",
- "items": {
- "type": "object",
- "properties": {
- "segment_title": {
- "type": "string",
- "description": "Title of the session segment."
- },
- "coach_dialogue": {
- "type": "array",
- "items": {
- "type": "string"
- },
- "description": "Suggested coach dialogue during the session"
- },
- "guidance": {
- "type": "array",
- "items": {
- "type": "string"
- },
- "description": "Suggestions for the coach on how to navigate responses."
- }
- },
- "required": ["segment_title", "coach_dialogue", "guidance"],
- "additionalProperties": False
- },
- "description": "Detailed information for each session segment."
- }
- },
"required": [
- "session_overview",
- "detailed_segments"
+ "pre_growth_guide_session_report",
+ "users_growth_guide_preparation_brief",
+ "30_minute_coaching_session_script"
],
"additionalProperties": False
}
- },
- "required": [
- "pre_growth_guide_session_report",
- "users_growth_guide_preparation_brief",
- "30_minute_coaching_session_script"
- ],
- "additionalProperties": False
}
- }
- }
- ,
- temperature=0.5,
- max_tokens=3000,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0
- )
+ }
+ ,
+ temperature=0.5,
+ max_tokens=3000,
+ top_p=1,
+ frequency_penalty=0,
+ presence_penalty=0
+ )
- # Get response and convert into dictionary
- reports = json.loads(response.choices[0].message.content)
- # html_output = generate_html(reports, coach_name)
- # reports['html_report'] = html_output
+ # Get response and convert into dictionary
+ reports = json.loads(response.choices[0].message.content)
- # Store users_growth_guide_preparation_brief in the User object
- if update_rec_topics:
- user.set_recommened_gg_topics(reports['users_growth_guide_preparation_brief'])
+ except Exception as e:
+ logger.error(f"OpenAI API call failed: {e}", extra={'user_id': user_id, 'endpoint': function_name})
+ raise e
# Step 4: Return the JSON reports
logger.info(f"User summary generated successfully for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
return reports
-@catch_error
-def create_pre_gg_report(booking_id):
- function_name = create_pre_gg_report.__name__
-
- # Get user_id from booking_id
- logger.info(f"Retrieving booking details for {booking_id}", extra={'booking_id': booking_id, 'endpoint': function_name})
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
- 'port': '5432'
- }
- try:
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("""
- select user_id
- from {table}
- where id = %s
- """
- ).format(table=sql.Identifier('public', 'booking'))
- cursor.execute(query, (booking_id,))
- row = cursor.fetchone()
- if (row):
- colnames = [desc[0] for desc in cursor.description]
- booking_data = dict(zip(colnames, row))
- ### MODIFY THE FORMAT OF USER DATA
- user_id = booking_data['user_id']
- logger.info(f"User info retrieved successfully for {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- else:
- logger.warning(f"No user info found for {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- except psycopg2.Error as e:
- logger.error(f"Database error while retrieving user info for {user_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving user info", code="SQLError", e=str(e))
-
- # Run get_user_summary
- user_report = get_user_summary(user_id)
-
- # Run generate_html
- generate_html(user_report, booking_id=booking_id)
-
- return True
-
-@catch_error
def get_user_life_status(user_id):
function_name = get_user_life_status.__name__
logger.info(f"Generating user life status for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- user = get_user(user_id)
- user_info = user.user_info
- user_messages = user.get_messages()
+ # Step 1: Call get_user to get user's info
+ try:
+ user = get_user(user_id)
+ user_info = user.user_info
+ user_messages = user.get_messages()
+ except LookupError as e:
+ logger.error(f"Error fetching user data: {e}", extra={'user_id': user_id, 'endpoint': function_name})
+ raise e
# Step 2: Construct the Prompt
chat_history = "\n".join(
[f"{message['role'].capitalize()}: {message['content']}" for message in user_messages]
)
- logger.info(f"Fetched user data for: {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
# Build the system prompt according to the provided instructions
system_prompt = """
You are an AI assistant that generates a personalized life status report for users based on their profile and chat history. Your task is to analyze the provided user data and produce a JSON output following the specified schema.
**Instructions:**
- 1. **Mantra of the Week:**
+ 1. **Life Score:**
+ - Calculate numerical scores for each of the following areas based on the user's profile and chat history, especially their responses to quantitative questions (e.g., "on a scale of 1 to 5, how do you feel about ..."):
+ - `personal_growth`
+ - `career_growth`
+ - `mental_well_being`
+ - `health_and_wellness`
+ - `relationship`
+ - Each score should be a float between 0 and 1. If an area hasn't been covered in the chat history, assign it a score of 0.
+ - Compute the `overall` score as the average of the non-zero area scores.
+
+ 2. **Mantra of the Week:**
- Create a very short encouragement quote that encapsulates the user's journey toward achieving their goals.
- The mantra **MUST** be a single sentence with fewer than 5 words.
- - Do **NOT** call the user's name in the mantra!
+
+ 3. **This Week's Focus:**
+ - Identify the top three most important areas for the user from the five key areas:
+ - `personal_growth`
+ - `career_growth`
+ - `mental_well_being`
+ - `health_and_wellness`
+ - `relationship`
+ - For each selected area, provide a concise statement outlining the user's focus in that area.
+
+ 4. **Suggested Action Items:**
+ - For the same three areas identified above, suggest concrete and actionable items that the user can undertake to make progress.
**Output Format:**
@@ -908,13 +714,34 @@ def get_user_life_status(user_id):
```json
{
- "mantra_of_the_week": str
+ "life_score": {
+ "overall": float,
+ "personal_growth": float,
+ "health_and_wellness": float,
+ "mental_well_being": float,
+ "career_growth": float,
+ "relationship": float
+ },
+ "mantra_of_the_week": str,
+ "this_week_focus": [
+ {"area": str, "focus": str},
+ {"area": str, "focus": str},
+ {"area": str, "focus": str}
+ ],
+ "action_items": [
+ {"area": str, "focus": str},
+ {"area": str, "focus": str},
+ {"area": str, "focus": str}
+ ]
}
```
**Guidelines:**
+ - Ensure all numerical values are floats (e.g., 3.0 instead of 3).
- The `mantra_of_the_week` should be personalized, positive, and encouraging. It **MUST** be a single sentence with fewer than 5 words.
+ - Focus statements and action items should be clear, specific, and tailored to the user's context.
+ - Do not include any additional text or commentary outside of the JSON structure.
"""
# Combine user information and chat history for context
@@ -930,166 +757,170 @@ def get_user_life_status(user_id):
# Step 3: Call the OpenAI API using the specified function
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
- response = client.chat.completions.create(
- model="gpt-4o-mini",
- messages=[
- {
- "role": "system",
- "content": [
- {
- "type": "text",
- "text": system_prompt
- }
- ]
- },
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": user_context
- }
- ]
- }
- ],
- response_format={
- "type": "json_schema",
- "json_schema": {
- "name": "life_status_report",
- "strict": True,
- "schema": {
- "type": "object",
- "properties": {
- "mantra_of_the_week": {
- "type": "string",
- "description": "A very short encouragement quote that encapsulates the user's journey to achieve their goals."
+ try:
+ response = client.chat.completions.create(
+ model="gpt-4o-mini",
+ messages=[
+ {
+ "role": "system",
+ "content": [
+ {
+ "type": "text",
+ "text": system_prompt
}
- },
- "required": [
- "mantra_of_the_week"
- ],
- "additionalProperties": False
+ ]
+ },
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "text",
+ "text": user_context
+ }
+ ]
+ }
+ ],
+ response_format={
+ "type": "json_schema",
+ "json_schema": {
+ "name": "life_status_report",
+ "strict": True,
+ "schema": {
+ "type": "object",
+ "properties": {
+ "life_score": {
+ "type": "object",
+ "description": "Numerical life scores across different areas.",
+ "properties": {
+ "overall": {
+ "type": "number",
+ "description": "Overall life score, average of all area scores."
+ },
+ "personal_growth": {
+ "type": "number",
+ "description": "Life score for personal growth."
+ },
+ "health_and_wellness": {
+ "type": "number",
+ "description": "Life score for health and wellness."
+ },
+ "mental_well_being": {
+ "type": "number",
+ "description": "Life score for mental well-being."
+ },
+ "career_growth": {
+ "type": "number",
+ "description": "Life score for career growth."
+ },
+ "relationship": {
+ "type": "number",
+ "description": "Life score for relationships."
+ }
+ },
+ "required": [
+ "overall",
+ "personal_growth",
+ "health_and_wellness",
+ "mental_well_being",
+ "career_growth",
+ "relationship"
+ ],
+ "additionalProperties": False
+ },
+ "mantra_of_the_week": {
+ "type": "string",
+ "description": "A very short encouragement quote that encapsulates the user's journey to achieve their goals."
+ },
+ "this_week_focus": {
+ "type": "array",
+ "description": "List of the user's top 3 most important areas with focus.",
+ "items": {
+ "type": "object",
+ "properties": {
+ "area": {
+ "type": "string",
+ "description": "The area of focus (e.g., 'personal_growth')."
+ },
+ "focus": {
+ "type": "string",
+ "description": "The focus within that area."
+ }
+ },
+ "required": ["area", "focus"],
+ "additionalProperties": False
+ }
+ },
+ "action_items": {
+ "type": "array",
+ "description": "List of suggested concrete action items for the user.",
+ "items": {
+ "type": "object",
+ "properties": {
+ "area": {
+ "type": "string",
+ "description": "The area of action (e.g., 'personal_growth')."
+ },
+ "focus": {
+ "type": "string",
+ "description": "Concrete action items in that area."
+ }
+ },
+ "required": ["area", "focus"],
+ "additionalProperties": False
+ }
+ }
+ },
+ "required": [
+ "life_score",
+ "mantra_of_the_week",
+ "this_week_focus",
+ "action_items"
+ ],
+ "additionalProperties": False
+ }
}
}
- }
- ,
- temperature=0.5,
- max_tokens=3000,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0
- )
-
- # Get response and convert into dictionary
- mantra = json.loads(response.choices[0].message.content)["mantra_of_the_week"]
-
- # Update the users mantra
- # user.set_mantra(mantra)
- # We remove because we want the mantra to be updated weekly (by backend), not updated everytime we call this endpoint/func
-
- cumulative_life_score = {
- "overall": user.personal_growth_score + user.career_growth_score + user.relationship_score + user.mental_well_being_score + user.health_and_wellness_score,
- "personal_growth": user.personal_growth_score,
- "health_and_wellness": user.health_and_wellness_score,
- "mental_well_being": user.mental_well_being_score,
- "career_growth": user.career_growth_score,
- "relationship": user.relationship_score
- }
-
- logger.info(f"{user.score_history}",extra={'user_id': user_id, 'endpoint': function_name})
-
- # Get current life score
- if len(user.score_history) == 0:
- thirtydays_life_score = cumulative_life_score
- else:
- # Calculate previous 30 days date
- now = pd.Timestamp.now()
- thirty_days_ago = now - pd.Timedelta(days=30)
-
- # Filter the data
- filtered_data = [entry for entry in user.score_history if thirty_days_ago <= entry["created_at"] <= now]
- logger.info(f"Filtered Data: {filtered_data}", extra={'user_id': user_id, 'endpoint': function_name})
-
- # Normalize area names to match expected keys
- area_mapping = {
- "Personal Growth": "personal_growth",
- "Health and Wellness": "health_and_wellness",
- "Mental Well-being": "mental_well_being",
- "Career Growth": "career_growth",
- "Relationship": "relationship"
- }
-
- # Normalize area names in filtered data
- for entry in filtered_data:
- entry["area"] = area_mapping.get(entry["area"], entry["area"])
+ ,
+ temperature=0.5,
+ max_tokens=3000,
+ top_p=1,
+ frequency_penalty=0,
+ presence_penalty=0
+ )
- # Sum points_added, group by area
- temp_df = pd.DataFrame(filtered_data)
- grouped_points = temp_df.groupby("area")["points_added"].sum()
-
- # Debug: Check the grouped points result
- logger.info(f"Grouped Points: {grouped_points}", extra={'user_id': user_id, 'endpoint': function_name})
-
- # Structure the output safely
- thirtydays_life_score = {
- "overall": int(sum([
- grouped_points.get("personal_growth", 0),
- grouped_points.get("career_growth", 0),
- grouped_points.get("health_and_wellness", 0),
- grouped_points.get("mental_well_being", 0),
- grouped_points.get("relationship", 0),
- ])),
- "personal_growth": int(grouped_points.get("personal_growth", 0)),
- "health_and_wellness": int(grouped_points.get("health_and_wellness", 0)),
- "mental_well_being": int(grouped_points.get("mental_well_being", 0)),
- "career_growth": int(grouped_points.get("career_growth", 0)),
- "relationship": int(grouped_points.get("relationship", 0))
- }
+ # Get response and convert into dictionary
+ reports = json.loads(response.choices[0].message.content)
- # Debug: Check the final structured result
- logger.info(f"Final Thirty Days Life Score: {thirtydays_life_score}", extra={'user_id': user_id, 'endpoint': function_name})
-
- # Get current goal
- current_goal = '' if not user.goal else user.goal[-1].content
- # Get life score achievements in list
- recent_wins = user.recent_wins
- # Combine everything
-
- reports = {
- "life_score": thirtydays_life_score,
- "cumulative_life_score": cumulative_life_score,
- "mantra_of_the_week": mantra.replace('.',''),
- "goal": current_goal,
- "recent_wins": recent_wins
- }
+ except Exception as e:
+ logger.error(f"OpenAI API call failed: {e}", extra={'user_id': user_id, 'endpoint': function_name})
+ raise e
# Step 4: Return the JSON reports
- logger.info(f"User life status generated successfully for user {user_id}: {reports}", extra={'user_id': user_id, 'endpoint': function_name})
+ logger.info(f"User life status generated successfully for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
return reports
-async def get_api_key(api_key_header: str = Security(api_key_header)) -> str:
- if api_key_header not in api_keys: # Check against list of valid keys
- raise HTTPException(
- status_code=status.HTTP_403_FORBIDDEN,
- detail="Invalid API key"
- )
- return api_key_header
+def get_api_key(api_key_header: str = Security(api_key_header)) -> str:
+ if api_key_header == os.getenv("FASTAPI_KEY"):
+ return api_key_header
+ raise HTTPException(
+ status_code=403,
+ detail="Could not validate credentials"
+ )
-@catch_error
def get_user_info(user_id):
function_name = get_user_info.__name__
logger.info(f"Retrieving user info for {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
db_params = {
'dbname': 'ourcoach',
'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
+ 'password': 'tfPMEnYFWKGSQqZWVhQo',
+ 'host': 'production-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
'port': '5432'
}
try:
with psycopg2.connect(**db_params) as conn:
with conn.cursor() as cursor:
- query = sql.SQL("SELECT left(onboarding,length(onboarding)-1)||',\"growth_guide_name\":\"'||coalesce(b.full_name,'')||'\"}}' onboarding FROM {table} a LEFT JOIN {coach_tbl} b ON a.assign_coach_id = b.id WHERE a.id = %s").format(table=sql.Identifier('public', 'users'), coach_tbl = sql.Identifier('public','coach'))
+ query = sql.SQL("SELECT * FROM {table} WHERE id = %s").format(table=sql.Identifier('public', 'users'))
cursor.execute(query, (user_id,))
row = cursor.fetchone()
if (row):
@@ -1105,11 +936,7 @@ def get_user_info(user_id):
### USER PROFILE ###
Name: {user_data_clean.get('firstName', '')}
- Growth Guide Name: {user_data_clean.get('growth_guide_name', '')}
- {user_data_clean.get('firstName', '')}'s challenges (You **must** use this information for the PLANNING STATE):
- {challenges}
- Persona:
- {user_data_clean.get('legendPersona', '')}
+ {user_data_clean.get('firstName', '')}'s Legendary Persona: {user_data_clean.get('legendPersona', '')}
Pronouns: {user_data_clean.get('pronouns', '')}
Birthday: {user_data_clean.get('birthDate', '')}
{user_data_clean.get('firstName', '')}'s MBTI: {user_data_clean.get('mbti', '')}
@@ -1126,103 +953,18 @@ def get_user_info(user_id):
{user_data_clean.get('firstName', '')}'s current situation: {user_data_clean.get('mySituation', '')}
{user_data_clean.get('firstName', '')}'s most important person:
{whoImportant}
+ {user_data_clean.get('firstName', '')}'s challenges:
+ {challenges}
"""
logger.info(f"User info retrieved successfully for {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- return user_data_formatted, user_data_clean.get('legendPersona', '')
+ return user_data_formatted, user_data_clean.get('mattersMost', ['', '', '', '', ''])
else:
logger.warning(f"No user info found for {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving user info", code="NoOnboardingError", e=str(e))
-
- except psycopg2.Error as e:
- logger.error(f"Database error while retrieving user info for {user_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving user info", code="SQLError", e=str(e))
-
-@catch_error
-def get_growth_guide_summary(user_id, booking_id):
- function_name = get_growth_guide_summary.__name__
- logger.info(f"Retrieving growth guide summary for user {user_id} and session {booking_id}", extra={'user_id': user_id, 'endpoint': function_name})
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
- 'port': '5432'
- }
- try:
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("SELECT * FROM {table} WHERE user_id = %s AND booking_id = %s").format(table=sql.Identifier('public', 'user_notes'))
- cursor.execute(query, (user_id, booking_id))
- row = cursor.fetchone()
- if (row):
- colnames = [desc[0] for desc in cursor.description]
- summary_data = dict(zip(colnames, row))
- logger.info(f"Growth guide summary retrieved successfully for user {user_id} and session {booking_id}: {summary_data}", extra={'user_id': user_id, 'endpoint': function_name})
- return summary_data
- else:
- logger.warning(f"No growth guide summary found for user {user_id} and session {booking_id}", extra={'user_id': user_id, 'endpoint': function_name})
return None
except psycopg2.Error as e:
- logger.error(f"Database error while retrieving growth guide summary for user {user_id} and session {booking_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving user info", code="SQLError", e=str(e))
-
-
-@catch_error
-def get_all_bookings():
- function_name = get_all_bookings.__name__
- logger.info(f"Retrieving all bookings", extra={'endpoint': function_name})
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
- 'port': '5432'
- }
- try:
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("SELECT id, user_id FROM {table}").format(table=sql.Identifier('public', 'booking'))
- cursor.execute(query)
- rows = cursor.fetchall()
- bookings = [{'booking_id': row[0], 'user_id': row[1]} for row in rows]
- logger.info(f"Retrieved {len(bookings)} bookings", extra={'endpoint': function_name})
- return bookings
- except psycopg2.Error as e:
- bookings = []
- logger.error(f"Database error while retrieving bookings: {e}", extra={'endpoint': function_name})
- raise DBError(user_id='no-user', message="Error retrieving user info", code="SQLError", e=str(e))
- finally:
- return bookings
-
-@catch_error
-def update_growth_guide_summary(user_id, session_id, ourcoach_summary):
- function_name = update_growth_guide_summary.__name__
- logger.info(f"Updating growth guide summary for user {user_id} and session {session_id}", extra={'user_id': user_id, 'endpoint': function_name})
-
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
- 'port': '5432'
- }
-
- try:
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("""
- UPDATE {table}
- SET ourcoach_summary = %s
- WHERE user_id = %s AND booking_id = %s
- """).format(table=sql.Identifier('public', 'user_notes'))
- cursor.execute(query, (json.dumps(ourcoach_summary), user_id, session_id))
- conn.commit()
- logger.info(f"Growth guide summary updated successfully for user {user_id} and session {session_id}", extra={'user_id': user_id, 'endpoint': function_name})
- except psycopg2.Error as e:
- logger.error(f"Database error while updating growth guide summary: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error updating growth guide summary", code="SQLError", e=str(e))
+ logger.error(f"Database error while retrieving user info for {user_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
+ return None
-@catch_error
def add_growth_guide_session(user_id, session_id, coach_id, session_started_at, zoom_ai_summary, gg_report, ourcoach_summary):
function_name = add_growth_guide_session.__name__
logger.info(f"Adding growth guide session for user {user_id} and session {session_id}", extra={'user_id': user_id, 'endpoint': function_name})
@@ -1230,8 +972,8 @@ def add_growth_guide_session(user_id, session_id, coach_id, session_started_at,
db_params = {
'dbname': 'ourcoach',
'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
+ 'password': 'tfPMEnYFWKGSQqZWVhQo',
+ 'host': 'production-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
'port': '5432'
}
@@ -1242,7 +984,7 @@ def add_growth_guide_session(user_id, session_id, coach_id, session_started_at,
INSERT INTO {table} (booking_id, coach_id, session_started_at, user_id, updated_at, gg_report, ourcoach_summary, created_at, zoom_ai_summary)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
""").format(table=sql.Identifier('public', 'user_notes'))
- current_time = datetime.now(timezone.utc)
+ current_time = datetime.datetime.now()
cursor.execute(query, (
session_id,
coach_id,
@@ -1258,9 +1000,8 @@ def add_growth_guide_session(user_id, session_id, coach_id, session_started_at,
logger.info(f"Growth guide session added successfully for user {user_id} and session {session_id}", extra={'user_id': user_id, 'endpoint': function_name})
except psycopg2.Error as e:
logger.error(f"Database error while adding growth guide session: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error adding growth guide session", code="SQLError", e=str(e))
+ raise e
-@catch_error
def get_growth_guide_session(user_id, session_id):
# returns the zoom_ai_summary and the gg_report columns from the POST_GG table
function_name = get_growth_guide_session.__name__
@@ -1268,8 +1009,8 @@ def get_growth_guide_session(user_id, session_id):
db_params = {
'dbname': 'ourcoach',
'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
+ 'password': 'tfPMEnYFWKGSQqZWVhQo',
+ 'host': 'production-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
'port': '5432'
}
try:
@@ -1288,10 +1029,33 @@ def get_growth_guide_session(user_id, session_id):
return None
except psycopg2.Error as e:
logger.error(f"Database error while retrieving growth guide session for user {user_id} and session {session_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving user info", code="SQLError", e=str(e))
+ return None
-@catch_error
+def upload_file_to_s3(filename):
+ user_id = filename.split('.')[0]
+ function_name = upload_file_to_s3.__name__
+ logger.info(f"Uploading file {filename} to S3", extra={'user_id': user_id, 'endpoint': function_name})
+ bucket = 'core-ai-assets'
+ try:
+ if (AWS_ACCESS_KEY and AWS_SECRET_KEY):
+ session = boto3.session.Session(aws_access_key_id=AWS_ACCESS_KEY, aws_secret_access_key=AWS_SECRET_KEY, region_name=REGION)
+ else:
+ session = boto3.session.Session()
+ s3_client = session.client('s3')
+ with open(os.path.join('users', 'to_upload', filename), "rb") as f:
+ ## Upload to Production Folder
+ s3_client.upload_fileobj(f, bucket, f'production/users/{filename}')
+ logger.info(f"File {filename} uploaded successfully to S3", extra={'user_id': user_id, 'endpoint': function_name})
+
+ os.remove(os.path.join('users', 'to_upload', filename))
+
+ # force_file_move(os.path.join('users', 'to_upload', filename), os.path.join('users', 'data', filename))
+ return True
+ except (FileNotFoundError, NoCredentialsError, PartialCredentialsError) as e:
+ logger.error(f"S3 upload failed for {filename}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
+ return False
+
def download_file_from_s3(filename, bucket):
user_id = filename.split('.')[0]
function_name = download_file_from_s3.__name__
@@ -1305,30 +1069,16 @@ def download_file_from_s3(filename, bucket):
s3_client = session.client('s3')
with open(file_path, 'wb') as f:
## Upload to Production Folder
- s3_client.download_fileobj(bucket, f"dev/users/{filename}", f)
+ s3_client.download_fileobj(bucket, f"production/users/{filename}", f)
logger.info(f"File {filename} downloaded successfully from S3", extra={'user_id': user_id, 'endpoint': function_name})
return True
except Exception as e:
logger.error(f"Error downloading file {filename} from S3: {e}", extra={'user_id': user_id, 'endpoint': function_name})
if (os.path.exists(file_path)):
os.remove(file_path)
- raise DBError(user_id=user_id, message="Error downloading file from S3", code="S3Error", e=str(e))
-
-@catch_error
-def add_to_cache(user):
- user_id = user.user_id
- function_name = add_to_cache.__name__
- logger.info(f"Adding user {user_id} to the cache", extra={'user_id': user_id, 'endpoint': function_name})
- user_cache[user_id] = user
- logger.info(f"User {user_id} added to the cache", extra={'user_id': user_id, 'endpoint': function_name})
- return True
+ return False
-@catch_error
def pop_cache(user_id):
- if user_id == 'all':
- user_cache.reset_cache()
- return True
-
if user_id not in user_cache:
logger.warning(f"[POPPING] User {user_id} not found in the cache", extra={'user_id': user_id, 'endpoint': 'pop_cache'})
# check if file exists
@@ -1336,13 +1086,13 @@ def pop_cache(user_id):
# upload file
logger.info(f"Attempting upload file {user_id}.json to S3", extra={'user_id': user_id, 'endpoint': 'pop_cache'})
upload_file_to_s3(f"{user_id}.pkl")
+ try:
+ user_cache.pop(user_id, None)
+ logger.info(f"User {user_id} has been removed from the cache", extra={'user_id': user_id, 'endpoint': 'pop_cache'})
+ return True
+ except:
+ return False
- user_cache.pop(user_id, None)
- logger.info(f"User {user_id} has been removed from the cache", extra={'user_id': user_id, 'endpoint': 'pop_cache'})
- return True
-
-
-@catch_error
def update_user(user):
user_id = user.user_id
function_name = update_user.__name__
@@ -1356,15 +1106,14 @@ def update_user(user):
return True
-@catch_error
def upload_mementos_to_db(user_id):
function_name = upload_mementos_to_db.__name__
logger.info(f"Uploading mementos to DB for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
db_params = {
'dbname': 'ourcoach',
'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
+ 'password': 'tfPMEnYFWKGSQqZWVhQo',
+ 'host': 'production-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
'port': '5432'
}
folder_path = os.path.join("mementos", "to_upload", user_id)
@@ -1404,7 +1153,7 @@ def upload_mementos_to_db(user_id):
data.get('location', ''),
data.get('recurrence', ''),
data.get('context', ''),
- datetime.now(timezone.utc),
+ datetime.datetime.now(),
pd.to_datetime(data.get('follow_up_on', ''))
]
cursor.execute(base_query, memento_data)
@@ -1426,16 +1175,18 @@ def upload_mementos_to_db(user_id):
return True
except psycopg2.Error as e:
logger.error(f"Database error while uploading mementos: {str(e)}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error uploading mementos", code="SQLError", e=str(e))
+ raise ConnectionError(f"Database error: {str(e)}")
+ except Exception as e:
+ logger.error(f"Unexpected error uploading mementos: {str(e)}", extra={'user_id': user_id, 'endpoint': function_name})
+ return False
-@catch_error
def get_users_mementos(user_id, date):
function_name = get_users_mementos.__name__
db_params = {
'dbname': 'ourcoach',
'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
+ 'password': 'tfPMEnYFWKGSQqZWVhQo',
+ 'host': 'production-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
'port': '5432'
}
logger.info(f"Retrieving mementos for user {user_id} on date {date}", extra={'endpoint': function_name, 'user_id': user_id})
@@ -1462,214 +1213,8 @@ def get_users_mementos(user_id, date):
logger.info(f"No mementos found for user {user_id} on date {date}", extra={'endpoint': function_name, 'user_id': user_id})
return []
except psycopg2.Error as e:
- mementos = []
logger.error(f"Database error while retrieving mementos: {e}", extra={'endpoint': function_name, 'user_id': user_id})
- raise DBError(user_id=user_id, message="Error retrieving mementos", code="SQLError", e=str(e))
- finally:
- return mementos
-
-@catch_error
-def id_to_persona(assistant_id):
- # persona_to_assistant = {
- # "Coach Steve": "asst_mUm6MBcW544p1iVov9mwIC96",
- # "Coach Aris": "asst_4WcktKgYdDnXA1QUlWvrNfWV",
- # "Coach Teresa": "asst_4UVkFK6r2pbz6NK6kNzG4sTW"
- # }
-
- assistant_to_persona = {
- "asst_mUm6MBcW544p1iVov9mwIC96": "Coach Steve, based on the persona of Steve Jobs (Innovation & Leadership)",
- "asst_4WcktKgYdDnXA1QUlWvrNfWV": "Coach Aris, based on the persona of Aristotle (Logic & Decision Making)",
- "asst_4UVkFK6r2pbz6NK6kNzG4sTW": "Coach Teresa, based on the persona of Mother Teresa (Compassion & Empathy)"
- }
-
- return assistant_to_persona.get(assistant_id, "Coach Steve, based on the persona of Steve Jobs")
-
-@catch_error
-def get_growth_guide(user_id):
- function_name = get_growth_guide.__name__
- logger.info(f"Retrieving growth guide for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': "hvcTL3kN3pOG5KteT17T",
- 'host': "staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com",
- 'port': '5432'
- }
- try:
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("SELECT assign_coach_id FROM {table} WHERE id = %s").format(table=sql.Identifier('public', 'users'))
- cursor.execute(query, (user_id,))
- row = cursor.fetchone()
- if row:
- logger.info(f"Growth guide retrieved successfully for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- gg_id = row[0]
- # Now query the coach table (public.coach) and take columns = ['id', 'full_name', 'email', 'bio',]
- query = sql.SQL("SELECT full_name, email, bio FROM {table} WHERE id = %s").format(table=sql.Identifier('public', 'coach'))
- cursor.execute(query, (gg_id,))
- row = cursor.fetchone()
- if row:
- colnames = ['full_name', 'email', 'bio']
- coach_data = dict(zip(colnames, row))
- logger.info(f"Coach data {coach_data} retrieved successfully for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- return coach_data
- else:
- logger.warning(f"No growth guide found for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- return None
- except psycopg2.Error as e:
- logger.error(f"Database error while retrieving growth guide for user {user_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving growth guide", code="SQLError", e=str(e))
-
-def get_booked_gg_sessions(user_id):
- # query the public.booking table for all bookings with user_id = user_id. sort by most recent first.
- # also transform the status column from int to string as:
- # 0 : creating
- # 1 : pending
- # 2 : completed
- # 3 : canceled
- function_name = get_booked_gg_sessions.__name__
- logger.info(f"Retrieving booked growth guide sessions for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': "hvcTL3kN3pOG5KteT17T",
- 'host': "staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com",
- 'port': '5432'
- }
- try:
- # first, query the public.users table and get the users local timezone from the timezone column
- user_timezone = get_user_local_timezone(user_id)
-
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("SELECT * FROM {table} WHERE user_id = %s ORDER BY created_at DESC").format(table=sql.Identifier('public', 'booking'))
- cursor.execute(query, (user_id,))
- rows = cursor.fetchall()
- bookings = []
- if rows:
- colnames = [desc[0] for desc in cursor.description]
- raw_bookings = [dict(zip(colnames, row)) for row in rows]
- for booking in raw_bookings:
- booking['status'] = {
- 0: 'creating',
- 1: 'pending',
- 2: 'completed',
- 3: 'canceled'
- }.get(booking['status'], 'creating')
- # convert datetime (in UTC) to users local timezone and convert to a string in the format YYYY-MM-DD %a HH:MM:SS
- booking['session_date'] = booking['session_started_at'].astimezone(pytz.utc).astimezone(pytz.timezone(user_timezone)).strftime('%Y-%m-%d %a %H:%M:%S')
- booking['created_at'] = booking['created_at'].astimezone(pytz.utc).astimezone(pytz.timezone(user_timezone)).strftime('%Y-%m-%d %a %H:%M:%S')
- booking['updated_at'] = booking['updated_at'].astimezone(pytz.utc).astimezone(pytz.timezone(user_timezone)).strftime('%Y-%m-%d %a %H:%M:%S')
- booking['booking_id'] = booking['id']
- booking['user_rating'] = booking['rate']
- booking["user_session_feedback"] = booking['comment']
-
- # convert the coach_id to coach_name
- query = sql.SQL("SELECT full_name FROM {table} WHERE id = %s").format(table=sql.Identifier('public', 'coach'))
- cursor.execute(query, (booking['coach_id'],))
- row = cursor.fetchone()
- if row:
- booking['coach_name'] = row[0]
- else:
- booking['coach_name'] = 'Unknown'
-
- booking = {k: v for k, v in booking.items() if k in ['status', 'booking_id', 'duration', 'user_rating', 'user_session_feedback', 'session_date', 'coach_name', 'created_at', 'updated_at']}
- bookings.append(booking)
-
- logger.info(f"Retrieved {len(bookings)} booked growth guide sessions for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- return bookings
- else:
- logger.warning(f"No booked growth guide sessions found for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- return []
- except psycopg2.Error as e:
- bookings = []
- logger.error(f"Database error while retrieving booked growth guide sessions for user {user_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving booked growth guide sessions", code="SQLError", e=str(e))
- finally:
- return bookings
-
-@catch_error
-def get_user_local_timezone(user_id):
- function_name = get_user_local_timezone.__name__
- logger.info(f"Retrieving local timezone for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': "hvcTL3kN3pOG5KteT17T",
- 'host': "staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com",
- 'port': '5432'
- }
- try:
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("SELECT timezone FROM {table} WHERE id = %s").format(table=sql.Identifier('public', 'users'))
- cursor.execute(query, (user_id,))
- row = cursor.fetchone()
- if row:
- user_timezone = row[0]
- logger.info(f"User timezone {user_timezone} retrieved successfully for user {user_id}", extra={'user_id': user_id, 'endpoint': function_name})
- return user_timezone
- else:
- user_timezone = 'Asia/Singapore'
- logger.warning(f"No timezone found for user {user_id}. Using default timezone {user_timezone}", extra={'user_id': user_id, 'endpoint': function_name})
- return user_timezone
- except psycopg2.Error as e:
- logger.error(f"Database error while retrieving local timezone for user {user_id}: {e}", extra={'user_id': user_id, 'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving local timezone", code="SQLError", e=str(e))
-
-@catch_error
-def get_user_subscriptions(user_id):
- function_name = get_user_subscriptions.__name__
- logger.info(f"Retrieving subscriptions for user {user_id}", extra={'endpoint': function_name})
- db_params = {
- 'dbname': 'ourcoach',
- 'user': 'ourcoach',
- 'password': 'hvcTL3kN3pOG5KteT17T',
- 'host': 'staging-ourcoach.cx8se8o0iaiy.ap-southeast-1.rds.amazonaws.com',
- 'port': '5432'
- }
- try:
- # get users timezone
- user_timezone = get_user_local_timezone(user_id)
-
- with psycopg2.connect(**db_params) as conn:
- with conn.cursor() as cursor:
- query = sql.SQL("""
- SELECT * FROM {table}
- WHERE user_id = %s
- ORDER BY period_started DESC
- """).format(table=sql.Identifier('public', 'user_subscription'))
- cursor.execute(query, (user_id,))
- rows = cursor.fetchall()
-
- # for each row in rows, transform the period_started and period_ended columns to subscription_start_date and subscription_end_date
- # additionally, convert thesubscription_start_date, subscription_end_date, created_at, updated_at to the users local timezone
- if rows:
- colnames = [desc[0] for desc in cursor.description]
- rows = [dict(zip(colnames, row)) for row in rows]
- for row in rows:
- # pass
- row['subscription_start_date'] = row['period_started'].astimezone(pytz.utc).astimezone(pytz.timezone(user_timezone)).strftime('%Y-%m-%d %a %H:%M:%S')
- row['subscription_end_date'] = row['period_ended'].astimezone(pytz.utc).astimezone(pytz.timezone(user_timezone)).strftime('%Y-%m-%d %a %H:%M:%S')
- row['paid_at'] = row['paid_at'].astimezone(pytz.utc).astimezone(pytz.timezone(user_timezone)).strftime('%Y-%m-%d %a %H:%M:%S')
- row['canceled_at'] = row['canceled_at'].astimezone(pytz.utc).astimezone(pytz.timezone(user_timezone)).strftime('%Y-%m-%d %a %H:%M:%S') if row['canceled_at'] else None
- row['status'] = row['stripe_status']
- del row['period_started']
- del row['period_ended']
- del row['stripe_subscription_id']
- del row['stripe_invoice_id']
- del row['id']
- del row['user_id']
- logger.info(f"Retrieved {len(rows)} subscriptions for user {user_id}", extra={'endpoint': function_name})
- return rows
- else:
- return ["No subscriptions found for user"]
- except psycopg2.Error as e:
- logger.error(f"Database error while retrieving user subscriptions: {e}", extra={'endpoint': function_name})
- raise DBError(user_id=user_id, message="Error retrieving user subscriptions", code="SQLError", e=str(e))
-
-def generate_uuid():
- return str(uuid.uuid4())
+ return []
def print_log(level, message, **kwargs):
"""
@@ -1684,7 +1229,7 @@ def print_log(level, message, **kwargs):
print_log("INFO", "User logged in", user_id=123, action="login")
"""
log_entry = {
- "timestamp": datetime.utcnow().isoformat() + "Z",
+ "timestamp": datetime.datetime.utcnow().isoformat() + "Z",
"level": level,
"message": message,
}