https://github.com/recker-dev/mini-cdss-streamlit-frontend
Agentic-AI (Lang-graph) based project, streamlining patient report generation and documentation process significantly.
https://github.com/recker-dev/mini-cdss-streamlit-frontend
agenticai genai huggingface langgraph streamlit
Last synced: about 1 year ago
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Agentic-AI (Lang-graph) based project, streamlining patient report generation and documentation process significantly.
- Host: GitHub
- URL: https://github.com/recker-dev/mini-cdss-streamlit-frontend
- Owner: Recker-Dev
- Created: 2025-02-15T19:24:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T17:29:38.000Z (over 1 year ago)
- Last Synced: 2025-04-14T18:50:58.641Z (about 1 year ago)
- Topics: agenticai, genai, huggingface, langgraph, streamlit
- Language: Python
- Homepage: https://mini-cdss-app-frontend-bypzbmstrxrkwdbwz5bdon.streamlit.app/
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Mini-CDSS Application
## BACKEND CODE: [https://github.com/Recker-Dev/Mini-CDSS-FastAPI]
## Overview
This is a Streamlit-based application designed to assist doctors in initial patient encounters. The application generates an initial patient report, provides diagnoses, and utilizes Tavily Web Search to find relevant best practices.
## Features
- Generate initial patient reports based on input data
- Provide preliminary diagnoses
- Search for best practices using Tavily Web Search
- Extract medical insights from uploaded documents
- RAG-Chat with uploaded documents
- Use vision models to analyze uploaded images and provide insights
- Offer feedback mechanisms for refining outputs
## Installation
To run this application locally/cloud, follow these steps:
### Prerequisites
Ensure you have the following installed:
- Python 3.x
- pip
### Setup
```bash
# Clone the repository
git clone https://github.com/Recker-Dev/Mini-CDSS-Streamlit-Frontend.git
cd Mini-CDSS-Streamlit-Frontend
```
# Install dependencies
```bash
pip install -r requirements.txt
```
## Running the Application
```bash
streamlit run app.py
```
## Usage
1. Open the application in your browser at `http://localhost:8501` and enter and validate the API Keys.
2. Upload patient data or documents
3. Review generated reports and diagnoses
4. Query images using the vision model
5. Access relevant best practices
## Deployment
To deploy this application on Streamlit Cloud or any hosting service, follow these steps:
1. Change the api-endpoint of FASTAPI of yours, Backend Code [https://github.com/Recker-Dev/Mini-CDSS-FastAPI]
2. Deploy Application on Streamlit Cloud.
3. .env vars should be in Backend side and not on Streamlit side.
## 📝 License
This project is licensed under the MIT License.
## 📬 Contact
For any issues or suggestions, reach out at [reckerdev@gmail.com](mailto:reckerdev@gmail.com).