Update interface branding to BluePlanet Medical Assistant

Refactor prompts in prompts.py for improved readability and structure

Fix formatting of FALLBACK_MODEL assignment in config.py

Update FALLBACK_MODEL to use ClinicalCamel-13B for improved performance

Update ME_LLAMA_MODEL to use the Llama-3.1-8B-Instruct variant and improve fallback model logging

update config

Update ME_LLAMA_MODEL to use the 13b variant for improved performance

Update model configurations to use new model paths and adjust tensor data types; increase max_new_tokens and modify sampling parameters in generate method for improved output quality.

model update

Update ME_LLAMA_MODEL to use google/medgemma-4b-it for improved performance

model and ui update

Update ME_LLAMA_MODEL and refine consultation prompt for clarity and focus

Refine consultation prompt for clarity and empathy, ensuring systematic information gathering and avoiding repetitive questions.

Enhance logging in handlers and update consultation prompt for clarity and empathy

Update max_new_tokens in generate method and enhance consultation prompt for clarity

Simple prompt update

Update conversation turn limit and enhance prompts for medical consultation app

add space

Implement medical consultation app with LangChain memory management and model integration

Add .gitignore file to exclude unnecessary files and directories

Enhance medical consultation app with LangChain memory management and improved patient context tracking

Update app.py

Update app.py

Update app.py

Update app.py

Update requirements.txt

Update app.py

Update requirements.txt

Update app.py

Update app.py

Refactor app.py to implement LangChain memory for enhanced conversation tracking. Update prompt building and response generation logic to utilize full conversation context, improving user interaction and medical assessment accuracy.

Refactor SYSTEM_PROMPT in app.py to enhance the medical consultation process with a structured approach for gathering patient information. Update follow-up question guidelines and improve response generation logic for more intelligent interactions.

Refactor app.py to streamline user information collection by removing redundant prompts for name and age. Implement a simple state tracking mechanism for improved conversation flow and enhance medical consultation process with structured follow-up questions.

Enhance user interaction in app.py by refining follow-up questions for symptom collection and implementing intelligent extraction of user name and age from messages. Improve response generation logic to ensure comprehensive medical assessments and treatment recommendations.

Refactor MEDITRON_PROMPT in app.py to enhance medical assessment and recommendations, ensuring evidence-based practices and clear communication. Update name and age extraction logic for improved accuracy in user responses.

Enhance prompt building in app.py to include intelligent follow-up questions and adjust response generation logic based on user information turns.

Enhance user information collection in app.py by emphasizing follow-up questions and refining response generation logic based on actual information turns.

Refactor app.py to extract user name and age from conversation history and improve response generation logic

Update SYSTEM_PROMPT in app.py for clarity and detail in patient information collection process

Refactor prompt building in app.py to utilize full message sequence and enhance response generation context

Refactor app.py to integrate LangChain memory for conversation tracking and update requirements.txt for LangChain dependency

Update requirements.txt

Refactor app.py to streamline conversation state management and update requirements.txt for package versions

update meditron

uppdate app and requirement.txt

Update requirements.txt

Update app.py

Update app.py

Update requirements.txt

Update app.py