metadata

title: MedSketch AI
emoji: 🏆
colorFrom: indigo
colorTo: pink
sdk: streamlit
sdk_version: 1.44.1
app_file: app.py
pinned: false
short_description: Medical Image

MedSketch AI – Advanced Clinical Diagram Generator 🖼️

MedSketch AI is a web application built with Streamlit that leverages cutting-edge AI models (like OpenAI's DALL-E 3 via the GPT-4o API endpoint access) to generate medical diagrams and illustrations from text prompts. It allows users to specify styles, associate metadata, perform batch generation, annotate the results, and export annotations.

[Insert Screenshot/GIF of the App Here - Highly Recommended!] A visual demonstration of MedSketch AI in action.

✨ Features

AI-Powered Generation: Create medical diagrams using models like OpenAI's DALL-E 3 (accessed via API). (Placeholder for Stable Diffusion LoRA integration).
Style Presets & Customization: Apply predefined styles (Anatomical, H&E, IHC) or define custom styles. Control stylization strength.
Batch Processing: Generate multiple diagrams simultaneously by entering one prompt per line.
Metadata Association: Tag generated images with optional Patient ID, Region of Interest (ROI), and UMLS/SNOMED CT codes.
Interactive Annotation: Draw annotations (freehand) directly onto the generated images using streamlit-drawable-canvas.
Session History: Keep track of generated images and their associated metadata within the current session.
Annotation Export: Download all annotations made during the session as a structured JSON file, including associated metadata and generation details.
Robust Error Handling: Provides informative feedback on API errors or other issues.
Configurable: Easy setup using Streamlit Secrets or environment variables for API keys.
Clear History: Option to clear the session history and annotations.

Prerequisites

Python: Version 3.8 or higher recommended.
pip: Python package installer.
Git: For cloning the repository.
OpenAI API Key: You need an API key from OpenAI to use the DALL-E 3 / GPT-4o generation features.

🚀 Installation & Setup

Clone the Repository:

git clone https://github.com/your-username/medsketch-ai.git # Replace with your repo URL
cd medsketch-ai

Create requirements.txt: Create a file named requirements.txt in the project root with the following content:
```
streamlit
openai
streamlit-drawable-canvas
Pillow
requests
```
Install Dependencies:
```
pip install -r requirements.txt
```
Configure OpenAI API Key: You must provide your OpenAI API key. There are two primary methods:
- a) Streamlit Secrets (Recommended for Deployment):
  - Create a directory named .streamlit in your project root if it doesn't exist.
  - Inside .streamlit, create a file named secrets.toml.
  - Add your API key to secrets.toml:
```
# .streamlit/secrets.toml
OPENAI_API_KEY="sk-YourSecretOpenAI_ApiKeyGoesHere"
```
  - Important: Ensure .streamlit/secrets.toml is added to your .gitignore file to prevent accidentally committing your secret key.
- b) Environment Variable (Good for Local Development):
  - Set the OPENAI_API_KEY environment variable in your terminal session:
  - Linux/macOS: bash export OPENAI_API_KEY='sk-YourSecretOpenAI_ApiKeyGoesHere'
  - Windows (Command Prompt): bash set OPENAI_API_KEY=sk-YourSecretOpenAI_ApiKeyGoesHere
  - Windows (PowerShell): bash $env:OPENAI_API_KEY='sk-YourSecretOpenAI_ApiKeyGoesHere'
  - The application will automatically look for this environment variable if the Streamlit secret is not found.

▶️ Running the Application

Once the dependencies are installed and the API key is configured, run the Streamlit app from your project's root directory:

streamlit run app.py
Use code with caution.
Markdown
Your default web browser should automatically open to the application's URL (usually http://localhost:8501).
📖 Usage Guide
Configure Settings (Sidebar):
Select Model: Choose between "GPT-4o (API)" (uses DALL-E 3) or the placeholder "Stable Diffusion LoRA".
Select Preset Style: Choose a visual style like "Anatomical Diagram", "H&E Histology", etc., or select "Custom" and enter your own style description.
Stylization Strength: Adjust the slider to control how strongly the style influences the output (this is conceptually passed in the prompt).
(Optional) Metadata: Enter relevant Patient/Case ID, ROI, or UMLS/SNOMED codes. These will be associated with the generated images in the history and export.
Enter Prompts (Main Area):
In the text area, describe the medical diagram(s) you want to generate.
For batch generation, enter one prompt per line.
Generate:
Click the "🚀 Generate Diagram(s)" button.
View Results:
Generated images will appear below the button, organized in columns.
Each result includes the image, the prompt used, and a download button (⬇️ Download PNG).
Annotate (Optional):
Below each image, an annotation canvas (✏️ Annotate:) is provided.
Use your mouse to draw directly on the image (default is freehand red lines).
Annotations are automatically saved to the session state.
Review History & Export Annotations (Bottom Section):
The "📚 Session History & Annotations" section appears once generations are complete.
It lists the prompts used, model/style settings, and associated metadata for each generated item.
You can expand each item to view the raw JSON data of any annotations made.
Click "⬇️ Export All Annotations (JSON)" to download a JSON file containing all annotations from the current session, enriched with metadata and generation details.
Clear History (Sidebar):
Use the "⚠️ Clear History & Annotations" button in the sidebar to reset the session.
🛠️ Technology Stack
Framework: Streamlit
AI Generation: OpenAI API (DALL-E 3)
Annotation: streamlit-drawable-canvas
Image Handling: Pillow (PIL Fork)
API Requests: requests (for image download if using URL format)
Language: Python
💡 Future Enhancements (Roadmap)
Implement actual Stable Diffusion LoRA model integration.
Support for additional AI image generation models.
More advanced annotation tools (shapes, text, colors).
Ability to load/edit existing annotations.
Improved image storage/retrieval in session state (potentially using caching or temporary files).
User accounts and persistent storage (beyond session).
More sophisticated prompt engineering assistance.
🙏 Contributing
Contributions are welcome! If you have suggestions for improvements or find a bug, please feel free to:
Open an issue to discuss the change or report the bug.
Fork the repository, make your changes, and submit a pull request.
Please ensure your code follows basic Python best practices and includes documentation where necessary.
📜 License
This project is licensed under the MIT License - see the LICENSE.txt file for details.
(You should create a LICENSE.txt file in your repository containing the text of the MIT License or your chosen license).
**To make this README complete:**

1.  **Replace Placeholders:** Update `https://github.com/your-username/medsketch-ai.git` and `https://your-deployed-app-url.com` with your actual URLs.
2.  **Add Screenshot/GIF:** Capture a compelling visual of your app and embed it where indicated. This significantly improves understanding.
3.  **Create `LICENSE.txt`:** Add a file named `LICENSE.txt` to your repository containing the full text of the MIT license (or whichever license you choose). You can easily find standard license text online (e.g., choosealicense.com).
4.  **Commit `requirements.txt`:** Make sure the `requirements.txt` file described is actually created and committed to your repository.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference