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import gradio as gr
import os
with gr.Blocks(title="Technical Documentation", css="footer {visibility: hidden}") as docs_demo:
with gr.Column():
gr.Markdown("""
# Technical Documentation
## Overview
This page provides details about the architecture, API, and usage of the MedGemma Agent application.
## Features
- Multimodal (text + image)
- Wikipedia tool integration
- Real-time streaming
- Medical knowledge base
---
## Architecture
- **Frontend:** Gradio Blocks, custom CSS
- **Backend:** Modal, FastAPI, VLLM, MedGemma-4B
- **Security:** API key authentication
### 🏗️ Technical Stack
- Streaming responses for real-time interaction
- Secure API key authentication
- Base64 image processing for multimodal inputs
### Frontend Interface
- Built with Gradio for seamless user interaction
- Custom CSS theming for professional appearance
- Example queries for common medical scenarios
```mermaid
graph TD
A[MedGemma Agent] --> B[Backend]
A --> C[Frontend]
A --> D[Model]
B --> B1[Modal]
B --> B2[FastAPI]
B --> B3[VLLM]
C --> C1[Gradio]
C --> C2[Custom CSS]
D --> D1[MedGemma-4B]
D --> D2[4-bit Quantization]
```
""")
gr.Markdown("""
## Backend Architecture
### 🎯 Performance Features
- Optimized for low latency responses
- GPU-accelerated inference
- Efficient memory utilization with 4-bit quantization
- Maximum context length of 8192 tokens
### 🔒 Security Measures
- API key authentication for all requests
- Secure image processing
- Protected model endpoints
```mermaid
flowchart LR
A[Client] --> B[FastAPI]
B --> C[Modal Container]
C --> D[VLLM]
D --> E[MedGemma-4B]
B --> F[Wikipedia API]
```
""")
with gr.Row():
with gr.Column():
gr.Markdown("""
## 💾 Model Deployment
### Model
- **Model:** unsloth/medgemma-4b-it-unsloth-bnb-4bit
- **Context Length:** 8192 tokens
- **Quantization:** 4-bit, bfloat16
- Utilizes Modal's GPU-accelerated containers
- Implements efficient model loading with VLLM
- Supports bfloat16 precision for optimal performance
""")
with gr.Column():
gr.Markdown("""
```mermaid
graph TD
A[Model Loading] --> B[GPU Acceleration]
B --> C[4-bit Quantization]
C --> D[8192 Token Context]
D --> E[Streaming Response]
```
""")
with gr.Column():
gr.Markdown("""
## 📊 System Architecture
```mermaid
flowchart TD
A[User Interface] --> B[API Gateway]
B --> C[Authentication]
C --> D[Model Service]
D --> E[Wikipedia Service]
D --> F[Image Processing]
F --> G[Model Inference]
E --> H[Response Generation]
G --> H
H --> I[Stream Response]
I --> A
```
""")
gr.Markdown("""
[Back to Main Application](https://huggingface.co/spaces/Agents-MCP-Hackathon/agentic-coach-advisor-medgemma)
""")
if __name__ == "__main__":
docs_demo.launch() |