readme.md

# 🏥 AI SOAP Note Generator for Google Colab

> Transform unstructured medical notes into professional SOAP documentation using Google's Gemma 3N model - **Optimized for Google Colab**

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/your-notebook-link)

## 📋 Overview

The AI SOAP Note Generator is an intelligent medical documentation tool that converts informal doctor's notes, patient encounters, and clinical observations into structured SOAP (Subjective, Objective, Assessment, Plan) format. This tool leverages Google's Gemma 3N language model and runs seamlessly in Google Colab with GPU acceleration.

## ✨ Features

- **🚀 Google Colab Ready**: No local setup required - runs entirely in the cloud
- **⚡ GPU Acceleration**: Leverages Colab's free GPU/TPU for fast processing
- **🧠 Gemma 3N Integration**: Uses Google's latest medical-aware language model
- **📱 Multiple Interfaces**: 
  - Interactive Jupyter widgets
  - Modern Gradio web interface
  - Direct function calls
- **📁 File Support**: Upload .txt files directly in Colab
- **🎯 Pre-loaded Examples**: Built-in medical scenarios for immediate testing
- **🔗 Shareable Links**: Generate public links to share your interface

## 🚀 Quick Start in Google Colab

### 1. Open the Notebook
Click the "Open in Colab" badge above or create a new notebook in [Google Colab](https://colab.research.google.com/)

### 2. Set Runtime to GPU (Recommended)
```

Runtime → Change runtime type → Hardware accelerator → GPU

```

### 3. Install Dependencies
Run this cell first:
```python

# Install required packages

!pip install -q gradio torch transformers accelerate bitsandbytes

!pip install -q ipywidgets



# Import libraries

import gradio as gr

import torch

from transformers import pipeline

import ipywidgets as widgets

from IPython.display import display, HTML

```

### 4. Run All Cells
Execute the notebook cells in order to:
- Load the Gemma 3N model
- Set up the interface
- Start generating SOAP notes

### 5. Use the Interface
- **Gradio Interface**: Click the public URL to access the web interface
- **Colab Widgets**: Use the interactive widgets directly in the notebook

## 📱 Interface Options

### Option 1: Gradio Web Interface (Recommended)
```python

# Launches a web interface with public sharing

gradio_interface.launch(share=True)

```
**Benefits:**
- Modern, responsive design
- Public shareable links
- Mobile-friendly
- Copy-to-clipboard functionality

### Option 2: Jupyter Widgets
```python

# Interactive widgets within the notebook

display(main_interface)

```
**Benefits:**
- Runs directly in Colab
- No external links needed
- Integrated with notebook workflow

## 🔧 Colab-Specific Setup

### GPU Configuration
```python

# Check GPU availability

import torch

print(f"CUDA available: {torch.cuda.is_available()}")

print(f"GPU device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'None'}")



# Configure for Colab GPU

device = "cuda" if torch.cuda.is_available() else "cpu"

model_config = {

    "device_map": "auto",

    "torch_dtype": torch.float16 if device == "cuda" else torch.float32

}

```

### File Upload in Colab
```python

# Method 1: Direct file upload

from google.colab import files

uploaded = files.upload()



# Method 2: Google Drive integration

from google.colab import drive

drive.mount('/content/drive')

```

### Save Results to Drive
```python

# Save generated SOAP notes to Google Drive

def save_to_drive(soap_note, filename):

    with open(f'/content/drive/MyDrive/{filename}', 'w') as f:

        f.write(soap_note)

    print(f"✅ Saved to Google Drive: {filename}")

```

## 📝 Usage Examples

### Example 1: Quick Test
```python

# Test with example data

test_note = """

Patient: 45yo male with chest pain x2 hours

Sharp substernal pain 7/10, radiates to left arm

SOB, diaphoresis, no nausea

Vitals: BP 150/90, HR 110, O2 96%

Anxious, diaphoretic appearance

"""



soap_result = generate_soap_note(test_note)

print(soap_result)

```

### Example 2: File Processing
```python

# Upload and process medical files

from google.colab import files

uploaded_files = files.upload()



for filename, content in uploaded_files.items():

    medical_text = content.decode('utf-8')

    soap_note = generate_soap_note(medical_text)

    

    # Save result

    output_filename = f"SOAP_{filename}"

    with open(output_filename, 'w') as f:

        f.write(soap_note)

    

    # Download result

    files.download(output_filename)

```

## 🎯 Pre-loaded Medical Examples

The notebook includes three clinical scenarios:

1. **Chest Pain Case**: Acute coronary syndrome workup
2. **Diabetes Case**: New onset diabetes mellitus
3. **Pediatric Case**: Streptococcal pharyngitis

Click any example button to load and test immediately.

## 🔍 Model Information

### Gemma 3N Configuration
```python

model_name = "google/gemma-3n-7b"  # Adjust based on availability

tokenizer_config = {

    "max_length": 2048,

    "temperature": 0.7,

    "do_sample": True

}

```

### Memory Optimization for Colab
```python

# For Colab's memory constraints

torch.cuda.empty_cache()

model = model.half()  # Use 16-bit precision

```

## ⚠️ Colab-Specific Considerations

### Runtime Limitations
- **12-hour session limit**: Save work frequently
- **GPU quota**: Free tier has daily limits
- **Memory constraints**: ~12-15GB RAM available

### Best Practices
1. **Save frequently**: Download important results
2. **Use GPU wisely**: Enable only when needed
3. **Monitor resources**: Check RAM/GPU usage
4. **Backup notebooks**: Save to Drive regularly

## 🛠️ Troubleshooting in Colab

### Common Issues

**"Runtime disconnected"**
```python

# Prevent disconnection

import time

while True:

    time.sleep(60)  # Keep session alive

```

**"Out of GPU memory"**
```python

# Clear GPU memory

torch.cuda.empty_cache()

# Restart runtime if needed: Runtime → Restart runtime

```

**"Package not found"**
```python

# Reinstall packages

!pip install --upgrade gradio transformers torch

```

**Gradio interface not loading**
```python

# Try without share link

gradio_interface.launch(share=False, debug=True)

```

## 📊 Performance Tips

### Optimize for Colab
```python

# Batch processing for multiple notes

def batch_process_notes(note_list):

    results = []

    for i, note in enumerate(note_list):

        print(f"Processing {i+1}/{len(note_list)}")

        soap_note = generate_soap_note(note)

        results.append(soap_note)

    return results

```

### Monitor Resources
```python

# Check memory usage

!nvidia-smi

!cat /proc/meminfo | grep MemAvailable

```

## 🔗 Sharing Your Work

### Share Notebook
1. **File → Save a copy in Drive**
2. **Share → Get shareable link**
3. Set permissions to "Anyone with the link"

### Share Interface
```python

# Gradio creates public URLs automatically

gradio_interface.launch(share=True)

# Copy the public URL to share with others

```

## 📋 Colab Notebook Structure

```

📓 SOAP_Note_Generator.ipynb

├── 🔧 Setup & Installation

├── 🧠 Model Loading

├── 📝 SOAP Generation Function

├── 🎨 Interface Creation

│   ├── Gradio Web Interface

│   └── Jupyter Widgets

├── 📋 Example Cases

├── 🚀 Launch Interface

└── 💾 Save/Export Functions

```

## 📄 Medical Disclaimer

> **⚕️ IMPORTANT**: This tool is for **educational and research purposes only**
> - Not intended for actual clinical use
> - Always consult qualified healthcare professionals
> - Remove patient identifiers before processing
> - Comply with HIPAA and privacy regulations

## 🆘 Getting Help

### In Colab:
1. Use `!pip list` to check installed packages
2. Check GPU with `!nvidia-smi`
3. Restart runtime if needed: `Runtime → Restart runtime`

### Common Commands:
```python

# Debug mode

import logging

logging.basicConfig(level=logging.DEBUG)



# Check versions

print(f"Torch: {torch.__version__}")

print(f"Transformers: {transformers.__version__}")

print(f"Gradio: {gr.__version__}")

```

## 🚀 Advanced Features

### Google Drive Integration
```python

# Mount Google Drive

from google.colab import drive

drive.mount('/content/drive')



# Save notebooks and results automatically

import shutil

shutil.copy('generated_soap_notes.txt', '/content/drive/MyDrive/')

```

### Scheduled Processing
```python

# Process notes at scheduled intervals

import schedule

import time



def scheduled_processing():

    # Your processing logic here

    pass



schedule.every(30).minutes.do(scheduled_processing)

```

---

**🔬 Ready to start generating professional SOAP notes in Google Colab!**

Click "Open in Colab" above and run all cells to get started immediately.