The Gen AI Hackathon project aims to utilize machine learning for healthcare by developing a web application that can detect various diseases such as asthma, cancer, diabetes, and stroke. The application provides real-time diagnosis based on predictive modeling.
31
stars
43
commits
Jupyter Notebook
primary language
Mar 22, 2024
updated
The Gen AI Hackathon project aims to utilize machine learning for healthcare by developing a web application that can detect various diseases such as asthma, cancer, diabetes, and stroke. The application provides real-time diagnosis based on predictive modeling. If a disease is detected, the user receives preventive measures and can engage in further conversation with a llm chatbot specialized in that disease.
We have worked on both aspects of the problem statement, i.e, Disease Diagnosis , treatment Recommendation.
The disease diagnosis part consists of the identification of disease based on information received from user through form-filling. Then ML model specific to that disease is used for the diagnosis of the particular disease.
The recommendation process plans out a detailed report regarding causes, symptoms, medical prescription and changes to be made in living style.
There is a LLM based chat-doctor finetuned on dataset (link mentioned at last), which can given guidance on general medical related questions.








Clone the repository:
https://github.com/shivam6862/Gen-AI-Hackathon.git
cd Gen-AI-Hackathon
Install dependencies and Run the application:
cd frontend
npm install
npm run dev
Install dependencies and Run the application:
cd backend
python -m venv env [for windows]
pip install -r requirements.txt
cd genaimechbackend
python manage.py runserver
Give .env in backend/genaimechbackend/genaimechbackend/.env
HOST=
PROJECT_NAME=
DB_USERNAME=
PASSWORD=
SECRET_KEY=
Access the application at port
The finetuning is done on Intel/Mistral-7B-v0.1-int4-inc, the code for fine-tuning is written under medical_finetune.py. The file can be run using below command
python medical_finetune.py --bf16 True --use_ipex True --max_seq_length 512
Team name - genaimech
Jupyter Notebook
45.6%
TypeScript
25.4%
CSS
15.3%
Python
13.0%
The Gen AI Hackathon project aims to utilize machine learning for healthcare by developing a web application that can detect various diseases such as asthma, cancer, diabetes, and stroke. The application provides real-time diagnosis based on predictive modeling.
31
stars
43
commits
Jupyter Notebook
primary language
Mar 22, 2024
updated
The Gen AI Hackathon project aims to utilize machine learning for healthcare by developing a web application that can detect various diseases such as asthma, cancer, diabetes, and stroke. The application provides real-time diagnosis based on predictive modeling. If a disease is detected, the user receives preventive measures and can engage in further conversation with a llm chatbot specialized in that disease.
We have worked on both aspects of the problem statement, i.e, Disease Diagnosis , treatment Recommendation.
The disease diagnosis part consists of the identification of disease based on information received from user through form-filling. Then ML model specific to that disease is used for the diagnosis of the particular disease.
The recommendation process plans out a detailed report regarding causes, symptoms, medical prescription and changes to be made in living style.
There is a LLM based chat-doctor finetuned on dataset (link mentioned at last), which can given guidance on general medical related questions.








Clone the repository:
https://github.com/shivam6862/Gen-AI-Hackathon.git
cd Gen-AI-Hackathon
Install dependencies and Run the application:
cd frontend
npm install
npm run dev
Install dependencies and Run the application:
cd backend
python -m venv env [for windows]
pip install -r requirements.txt
cd genaimechbackend
python manage.py runserver
Give .env in backend/genaimechbackend/genaimechbackend/.env
HOST=
PROJECT_NAME=
DB_USERNAME=
PASSWORD=
SECRET_KEY=
Access the application at port
The finetuning is done on Intel/Mistral-7B-v0.1-int4-inc, the code for fine-tuning is written under medical_finetune.py. The file can be run using below command
python medical_finetune.py --bf16 True --use_ipex True --max_seq_length 512
Team name - genaimech
Jupyter Notebook
45.6%
TypeScript
25.4%
CSS
15.3%
Python
13.0%