https://github.com/mlengineershub/ai_action_summit_hackathon_an2i
https://github.com/mlengineershub/ai_action_summit_hackathon_an2i
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/mlengineershub/ai_action_summit_hackathon_an2i
- Owner: mlengineershub
- Created: 2025-02-15T09:09:04.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-16T11:07:32.000Z (over 1 year ago)
- Last Synced: 2025-02-16T11:28:53.691Z (over 1 year ago)
- Language: Python
- Size: 754 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ai_action_summit_hackathon_an2i
A comprehensive medical prescription analysis system that leverages AI to enhance patient care and medical decision-making.
## Our Approach
Our system implements a multi-faceted approach to medical analysis and prescription safety:
1. **Real-time Conversation Analysis**
- Automated extraction of medical context from doctor-patient dialogues
- Dynamic generation of relevant medical queries
- Intelligent follow-up question suggestions
2. **Prescription Safety System**
- Automated anomaly detection in prescriptions
- Cross-referencing with patient medication history
- Real-time alerts for potential drug interactions
- Analysis of patient medication adherence patterns
3. **Comprehensive Reporting**
- Side-by-side display of medical reports and prescription analysis
- Detailed symptom and pathology documentation
- Clear treatment plans with safety considerations
- Intelligent summarization of medical findings
### Generated Reports
Access our sample medical reports and analysis:
- [Technical Report](reports/Tech_Report.pdf)
- [Business Plan](reports/Buisness_Plan.pdf)
## Flask API Service
The project includes a robust Flask API that provides various medical analysis endpoints:
### API Endpoints
- `/detect-prescription-anomalies` - Analyzes prescriptions for potential conflicts
- `/extract-ordonnance` - Extracts structured data from medical prescriptions
- `/summarize-ordonnances` - Generates summaries of multiple prescriptions
- `/search-medical-articles` - Searches for relevant medical literature
- `/fetch-article-abstract/{pmid}` - Retrieves abstracts of medical articles
- `/generate-search-summary` - Summarizes medical research findings
- `/generate-follow-up-questions` - Generates relevant follow-up questions
- `/extract-pertinent-points` - Extracts key medical points from conversations
- `/generate-search-propositions` - Suggests relevant medical search queries
- `/generate-report` - Generates comprehensive medical reports
The API runs on port 5000 by default and accepts JSON payloads for POST requests.
## Streamlit Dashboard
The project includes a user-friendly Streamlit dashboard that provides a graphical interface to interact with the API services. The dashboard offers:
### Features
- Interactive navigation sidebar for different functionalities
- Form-based input for medical data
- Real-time API response display
- Error handling and user feedback
- Medical article search interface with abstract viewing
- Report generation interface
- Prescription anomaly detection with visual alerts
### Dashboard Pages
1. Prescription Analysis
- Anomaly detection
- Ordonnance extraction
- Prescription summarization
2. Medical Research
- Article search with configurable results
- Abstract viewing
- Search summary generation
3. Medical Documentation
- Follow-up question generation
- Pertinent point extraction
- Report generation
The Streamlit dashboard connects to the Flask API at `http://localhost:5000`.
# Development
## Notes
- commit messages should follow conventional commits spec: https://www.conventionalcommits.org/en/v1.0.0/ (see sections: Summary, Examples)
- work must be merged from feature branches into main branch using Pull Requests. Once your code is ready to be reviewed, please mark me as a reviewer, which will notify me you're ready fir code review
## How-to's
### Install
1. install uv: https://docs.astral.sh/uv/getting-started/installation/#standalone-installer
2. run
```shell
uv sync --all-extras --dev
```
3. Before running scripts add the current directory to the PythonPath:
```
export PYTHONPATH=/home/azzedine/Projects/ai_action_summit_hackathon_an2i:$PYTHONPATH
```
### Running the Services Locally
1. Start the Flask API:
```shell
python3 -m workspace.src.api
```
2. Start the Streamlit Dashboard:
```shell
streamlit run workspace/app/main.py
```
### Docker Deployment
The project can be run using Docker Compose, which sets up all required services automatically.
#### Prerequisites
1. Install Docker and Docker Compose on your system
2. Copy the environment variables file:
```shell
cp .env.example .env
```
3. Update the `.env` file with your credentials:
- API_KEY: Your API key
- PROVIDER_URL: Your provider URL
- MongoDB credentials (if using MongoDB):
- USERNAME_MONGODB
- PASSWORD_MONGODB
- INSTANCE_MONGODB
- REGION_MONGODB
- PATH_TLS_CERTIFICATE_MONGODB
#### Services
The Docker Compose setup includes the following services:
- **API Service** (Port 5000): Flask API server
- **Streamlit Dashboard** (Port 8501): Web interface
- **Redis** (Port 6379): Message broker for Celery
- **Celery Workers**:
- LLM Worker: Handles language model tasks
- API Worker: Handles API-related tasks
- **Flower** (Port 5555): Celery monitoring interface (credentials: admin/admin)
#### Running with Docker Compose
1. Build and start all services:
```shell
docker compose up --build
```
2. Access the services:
- Streamlit Dashboard: http://localhost:8501
- Flask API: http://localhost:5000
- Flower Dashboard: http://localhost:5555
3. Stop all services:
```shell
docker compose down
```
### Lint (check & fix)
1. open a new terminal in the root of this repo and run:
```shell
uv run ruff check --fix
uv run ruff format
uv run mypy .
```
### Run tests
1. open a new terminal in the root of this repo and run:
```shell
uv run pytest