https://github.com/asshejan/image-description-locally-using-llava
A Streamlit app that uses the Ollama llava model to describe uploaded images. Simply upload an image, and the model generates a description. Easy setup and interactive interface!
https://github.com/asshejan/image-description-locally-using-llava
ai chatbot image image-vision lava llama
Last synced: about 2 months ago
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A Streamlit app that uses the Ollama llava model to describe uploaded images. Simply upload an image, and the model generates a description. Easy setup and interactive interface!
- Host: GitHub
- URL: https://github.com/asshejan/image-description-locally-using-llava
- Owner: asshejan
- Created: 2025-01-26T21:49:00.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-01-26T22:25:39.000Z (11 months ago)
- Last Synced: 2025-03-22T07:53:28.158Z (9 months ago)
- Topics: ai, chatbot, image, image-vision, lava, llama
- Language: Python
- Homepage:
- Size: 3.76 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Image Description with Ollama
This project is a simple Streamlit-based web application that allows users to upload an image and get a description of it using the Ollama `llava` model. The app provides an intuitive interface for interacting with the model, making it easy to describe uploaded images.
## Features
- Upload images in `.jpg`, `.jpeg`, or `.png` formats.
- Display the uploaded image for user confirmation.
- Use the `ollama` model to generate a description of the image.
- Streamlit interface for simplicity and accessibility.
## Requirements
To run this project, you need the following:
- Python 3.7 or higher
- Required Python libraries:
- `streamlit`
- `ollama`
## Installation
1. Clone this repository:
```bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
```
2. Install the required dependencies:
```bash
pip install streamlit ollama
```
3. Run the Streamlit app:
```bash
streamlit run test.py
```
## Usage
1. Launch the app locally by running the above command.
2. Upload an image using the file uploader.
3. Click the "Describe Image" button to get the model's description of the image.
## Code Overview
The main functionality is implemented in the `test.py` file:
- **Image Upload:**
Allows users to upload an image via Streamlit's `file_uploader` widget.
- **Image Display:**
The uploaded image is displayed using `st.image()`.
- **Model Interaction:**
The uploaded image is saved temporarily and passed to the `ollama.chat` function to get a description.
- **Error Handling:**
Handles exceptions to ensure a smooth user experience.
## Screenshot
## Folder Structure
```plaintext
.
├── test.py # Main application file
├── images/ # Folder for storing example images or screenshots
└── README.md # Project documentation
```
## Example Output
- **Input:** An uploaded image of a cat.
- **Output:**
"This is an image of a cat sitting on a windowsill, looking outside."
## Future Improvements
- Add support for multiple image uploads.
- Provide additional model configuration options.
- Enhance the UI with more styling and interactivity.
## Contributing
Contributions are welcome! Please fork this repository and submit a pull request for any enhancements or bug fixes.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Acknowledgments
- [Ollama](https://www.ollama.com) for the `llava` model.
- [Streamlit](https://streamlit.io/) for making web app development so accessible.
---
Feel free to reach out if you have any questions or suggestions!