Google Gemini AI is an advanced AI model designed with cutting-edge capabilities, ensuring scalability, speed, and safety. This section briefly outlines key features, potential applications, and the current development status, with plans for future availability.
Availability: Google Gemini AI is not yet publicly available.
Stay Informed:
- For updates, announcements, and potential public access, visit Google AI's blog and monitor research announcements.
- Explore alternative AI tools on Google Cloud, such as Vertex AI and Duet AI.
- Contact Google AI for specific inquiries or collaborations through designated channels.
Model Sizes:
- Nano
- Pro
- Ultra
Resource Requirements:
- Specify resource requirements for each model size.
Input and Output Modalities:
- Text
- Code
- Images
- Audio
Hardware Requirements:
- Compatibility with Google TPUs.
Software Dependencies:
- List any required dependencies or prerequisites for integration.
- Provide code examples or tutorials for common tasks when available.
- Offer best practices for effective and responsible use.
- Address safety and bias concerns, emphasizing Google's commitment to mitigating these issues.
- Clarify if and how contributions to Gemini's development are possible.
- Direct potential contributors to relevant channels or programs.
- Link to Google AI's Code of Conduct for responsible AI development and usage.
- Specify licensing terms applicable to Gemini AI.
- Outline any restrictions or limitations on usage.
- Provide contact information for inquiries, feedback, or collaborations.
- Link to Google AI's research page or relevant partnerships.
- Link to relevant research papers, blog posts, or documentation.
- List external resources for further exploration of AI and multimodal models.
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Last Update: [Insert Last Update Date]
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Encourage users to check for updates regularly.
- Acknowledge that information in the Readme file is subject to change.
- Note that Google Gemini AI is under development and may have limitations.
- Invite users to contribute to documentation and the community.
- Provide instructions on how to submit feedback or suggestions.
This repository contains two Streamlit web applications for interacting with Google Gemini AI: app.py for Question/Answer (Q/A) and Gemini-vision.py for image recognition and description creation. The applications leverage the Google Gemini Pro and Gemini Pro Vision models.
Before running the applications, make sure to set up the necessary environment variables by creating a .env file and populating it with your Google API key.
# .env file
GOOGLE_API_KEY=your_google_api_key
Install the required dependencies by running:
pip install -r requirements.txt
Q/A Application (app.py)
This application allows users to ask questions using the Gemini Pro model and receive responses.
streamlit run app.py
Enter your question in the text input.
Click the "Submit" button to get Gemini's response.
Image Recognition and Description Application (Gemini-vision.py)
This application utilizes the Gemini Pro Vision model for image recognition and description creation.
streamlit run Gemini-vision.py
Enter text in the provided text input.
Upload an image using the file uploader.
Click the "How do you feel about this image?" button to get Gemini's response.
Configuration
Ensure that the environment variable GOOGLE_API_KEY is set to your Google API key in the .env file.
Usage
Follow these steps to run the Gemini Pro Web Application:
Configure your Google API key in the .env file.
Install the required dependencies using pip install -r requirements.txt.
Run the Q/A application using streamlit run app.py.
Run the Image Recognition and Description application using streamlit run Gemini-vision.py.
License
This project is licensed under the Apache License 2.0.