https://github.com/shreyash729/cyfuture-hackathon
Aarogya - Your AI-Powered Healthcare Portal )
https://github.com/shreyash729/cyfuture-hackathon
artificial-intelligence css flask git html javascript jupyter-notebook machine-learning natural-language-processing nlp-machine-learning python render tailwindcss
Last synced: 3 months ago
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Aarogya - Your AI-Powered Healthcare Portal )
- Host: GitHub
- URL: https://github.com/shreyash729/cyfuture-hackathon
- Owner: shreyash729
- License: mit
- Created: 2025-06-03T19:26:39.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-20T19:20:51.000Z (about 1 year ago)
- Last Synced: 2025-06-20T20:40:54.896Z (about 1 year ago)
- Topics: artificial-intelligence, css, flask, git, html, javascript, jupyter-notebook, machine-learning, natural-language-processing, nlp-machine-learning, python, render, tailwindcss
- Language: Jupyter Notebook
- Homepage: https://aarogya-hackathon.onrender.com/
- Size: 70.1 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.markdown
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README
# Aarogya - Cyfuture Hackathon
Aarogya is an innovative healthcare portal designed to enhance patient care through artificial intelligence and machine learning. Developed for the Cyfuture Hackathon, Aarogya streamlines healthcare workflows with features tailored for patients and providers.
## Features
### Automated Clinical Documentation:
The Automated Clinical Documentation app uses voice recognition and NLP to generate structured clinical notes from doctor-patient conversations. It streamlines documentation, reducing administrative burden for healthcare providers.
- ***Documentation:*** https://aarogya-hackathon.onrender.com/documentation3
- Note: Render’s free tier may experience \~(30-60)-second cold starts after inactivity.
- ***demo Video:***
https://github.com/user-attachments/assets/3fe6b3b7-150f-41b1-bf55-aca1d0e3d669
#
### Symptom Checker
Powered by Google Gemini AI, this feature analyzes user-reported symptoms to provide potential diagnoses, enhancing patient accessibility to health insights.
- ***Documentation:*** https://aarogya-hackathon.onrender.com/documentation1
- Note: Render’s free tier may experience \~(30-60)-second cold starts after inactivity.
- ***demo Video:***
https://github.com/user-attachments/assets/0690a4d3-ad5b-4105-8cb5-ad93ddca942d
#
### Predictive Patient Risk Models :
Utilizes a machine learning model (`RandomForestClassifier`) to predict hospital readmission risks based on patient data, supporting proactive care.
- ***Documentation:*** https://aarogya-hackathon.onrender.com/documentation2
- Note: Render’s free tier may experience \~30-second cold starts after inactivity.
- ***demo Video:***
https://github.com/user-attachments/assets/431d1b27-2d82-41b0-8cc0-a03af5a8fca4
#
## Setup Instructions
### Live Demo
- Visit https://aarogya-hackathon.onrender.com.
- Note: Render’s free tier may experience \~(30-60)-second cold starts after inactivity.
### Local Demo
To run the app locally, including the speech-to-text feature:
- Python version >= 3 required
- `Git` is required: (IF NOT available download using following Links)
- `Windows`: https://git-scm.com/downloads/win
- `Macos` : https://git-scm.com/downloads/mac
- `linux` : https://git-scm.com/downloads/linux
1. **Clone the Repository**:
```bash
git clone https://github.com/shreyash729/Cyfuture-Hackathon.git
cd Cyfuture-Hackathon
```
2. **creates a virtual environment**:
```bash
python -m venv venv
```
3. **Set Up Gemini Api Key**:
```bash
set GOOGLE_API_KEY=YOUR_GEMINI_API_KEY # replace it with your api key
```
4. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
4. **Run the App**:
```bash
python app.py
```
- Check if terminal displays `🚀 Starting Flask server...`
- Access http://127.0.0.1:5000/ to Use Aarogya
- Ensure `model/vosk-model-small-hi-0.22` is present.
- Replace `vosk-model-small-hi-0.22` with `vosk-model-hi-0.22` for better Accuracy
- Download Vosk NLP model from `https://alphacephei.com/vosk/models`
## Screenshot
- **Frontend**: Flask, Tailwind CSS (blue/white/gray healthcare theme).
- **Backend**: Python, Flask, scikit-learn (RandomForestClassifier), Google Gemini AI.
- **Deployment**: Render free tier, with Gunicorn.
- **NLP**: Vosk speech-to-text (local demo), mocked with text input online.
- **Model**: Pre-trained `readmission_model.pkl` for hospital readmission predictions.
- **Constraints**: Render’s free tier lacks PortAudio, limiting PyAudio deployment.
## References
### NLP model:
Small model (Lesser Accuracy): [vosk-model-small-hi-0.22](https://alphacephei.com/vosk/models/vosk-model-small-hi-0.22.zip)
For Better Accuracy: [vosk-model-hi-0.22](https://alphacephei.com/vosk/models/vosk-model-hi-0.22.zip)
### DataSet:
[kaggle dataset](https://www.kaggle.com/datasets/dubradave/hospital-readmissions/data)