https://github.com/smaranjitghose/objectsightai
A powerful and intuitive image analysis interface powered by Google's Gemini Flash 2, built with Python and Streamlit
https://github.com/smaranjitghose/objectsightai
computer-vision gemini gemini-2-0-flash llm python streamlit
Last synced: 11 months ago
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A powerful and intuitive image analysis interface powered by Google's Gemini Flash 2, built with Python and Streamlit
- Host: GitHub
- URL: https://github.com/smaranjitghose/objectsightai
- Owner: smaranjitghose
- License: mit
- Created: 2024-12-28T22:16:43.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-04-21T21:07:52.000Z (about 1 year ago)
- Last Synced: 2025-04-21T22:22:39.064Z (about 1 year ago)
- Topics: computer-vision, gemini, gemini-2-0-flash, llm, python, streamlit
- Language: Python
- Homepage: https://objectsightai.streamlit.app/
- Size: 2.37 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# ObjectSight AI 👁️
A powerful and intuitive image analysis interface powered by Google's Gemini Flash 2, built with Python and Streamlit.
## 🌟 Features
- 🎯 Real-time object detection and localization
- 📦 Clear bounding box visualization with enhanced labels
- 🔄 Support for common image formats (JPG, JPEG, PNG)
- 🎨 Clean and minimalist user interface
- 💾 Download capability for analyzed images
- 🔑 Secure API key management
## 🖼️ Snapshot
## 🔧 Prerequisites
- Python 3.12 or higher
- A Web Browser
- Google API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
## 📥 Installation
1. Clone the repository:
```bash
git clone https://github.com/smaranjitghose/ObjectSightAI.git
cd ObjectSightAI
```
2. Create and activate virtual environment:
```bash
# Windows
python -m venv env
.\env\Scripts\activate
# Linux/Mac
python3 -m venv env
source env/bin/activate
```
3. Install required packages:
```bash
pip install -r requirements.txt
```
## 🚀 Usage
1. Start ObjectSight AI:
```bash
streamlit run app.py
```
2. Open your browser and navigate to:
```
http://localhost:8501
```
## 💡 Quick Start Guide
1. Enter your Google API key in the sidebar
2. Upload an image using the file uploader
3. Write a descriptive prompt about what to analyze
4. Click "Run!" to start the analysis
5. View results and download the analyzed image if desired
## 🎯 Example Prompts
```plaintext
"Identify and locate all objects in this image"
"Find and label all people and furniture"
"Detect all electronic devices"
"Locate and identify different types of vehicles"
```
## 🛠️ Troubleshooting
### Common Issues
1. **API Key Error**
- Verify API key is entered correctly
- Check if API key has necessary permissions
- Ensure API key is active
2. **Image Upload Issues**
- Check if image format is supported
- Ensure image size is under limit
- Verify image is not corrupted
3. **Analysis Failures**
- Check internet connection
- Verify API quota hasn't been exceeded
- Ensure prompt is clear and specific
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the project
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
Made with ❤️ by [Smaranjit Ghose](https://github.com/smaranjitghose)