Gen AI Projects Repository📁

This repository features a collection of generative AI applications designed to showcase the capabilities of AI across various domains. Explore projects that generate text, images, music, and code, each demonstrating innovative uses of generative technology. Contribute your own projects and experiment with the potential of AI!

Table of Contents📑

• Project Overview
• Technologies Used
• API Key Generation Guide
• Deploying on Streamlit
• Getting Started
• Contributing
• Note
• License

Project Overview📝

This repository showcases a variety of generative AI applications, each designed to illustrate different aspects and capabilities of AI technology. Projects may include:

• Text summarization projects
• Text generation projects
• Text classification projects
• Image synthesis models
• Code generation tools, etc.,

The aim is to provide a platform for experimentation and demonstration of generative AI's potential across multiple domains.

Technologies Used💻

Each project may utilize a range of technologies and frameworks, including but not limited to:

• Python
• TensorFlow
• PyTorch
• Groq API
• Google Gemini API
• Hugging Face Transformers
• Streamlit
• Gradio
• FastAPI or Flask (for web apps)

API Key Generation Guide🔑

This guide walks you through the steps to generate API keys for Groq, Hugging Face, OpenAI, and Gemini.

• Getting Gemini API Key✨

  • Visit Google AI Studio.
  • Sign in using your Google account.
  • Navigate to API Key.
  • Click on Create API Key.
  • Ensure that you save the generated API Key, as it will be needed for deployment.

• Getting Groq API Key⚡

  • Visit Groq.
  • Sign in or create an account with Groq.
  • After logging in, navigate to the API Keys section in your account settings.
  • Click on Create New API Key.
  • A new API key will be generated. Copy and save it securely for future use.

• Getting Hugging Face API Key🤗

  • Visit Hugging Face.
  • Sign in or create an account if you don’t have one.
  • After logging in, go to your Account Settings.
  • Under Access Tokens, click on New Token.
  • Choose a scope for the token (e.g., Read or Write) depending on your needs.
  • Click on Generate.
  • Copy and save the generated API token. This will be required for accessing Hugging Face models and datasets.

• Getting OpenAI API Key🤖

  • Visit the OpenAI API page.
  • Sign in or create an OpenAI account if you don’t have one.
  • After logging in, navigate to the API section in the dashboard.
  • Click on Create new secret key.
  • A new API key will be generated. Copy and save it securely. This key will be used to interact with OpenAI's models.

Deploying on Streamlit

1. Visit Streamlit.
2. Sign in or sign up for a new account if you don't have one.
3. Authorize Streamlit with your GitHub account.
4. Click on Create App and select Deploy a public app from GitHub.
5. Provide the following details:
   • GitHub Repository (e.g., https://github.com/yourusername/your-repo)
   • Branch (e.g., main)
   • Main file path (e.g., app.py or main.py)
6. Click on Advanced Settings.
7. Under Environment Variables, add your API keys:

   GEMINI_API_KEY="your_gemini_api_key"
   GROQ_API_KEY="your_groq_api_key"
   HF_API_KEY="your_huggingface_api_key"
   OPENAI_API_KEY="your_openai_api_key"

8. Click on Deploy to launch your app.

Getting Started🚀

To get started with the projects in this repository, follow these steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/gen-ai-projects.git
   cd gen-ai-projects
   

2. Install Dependencies: Each project may have its own set of dependencies. Navigate to the specific project directory and install the necessary libraries. For example:

   cd project-name
   pip install -r requirements.txt
   

3. Run the Application: Follow the instructions in the respective project directory for running the application. This may involve executing a script or starting a web server.

Contributing🤝

Contributions are welcome! If you'd like to add your own generative AI project or improve existing ones, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeature

3. Make your changes and commit them:

   git commit -m 'Add some feature'

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a pull request.

Note: Please ensure that your contributions align with the project's goals and maintain a high standard of quality.

Note on Model Output📌

The model generates responses based on learned patterns from a large dataset, but there are important considerations:

• Accuracy Uncertainty: The model may not always provide correct answers, as its responses are probabilistic and context-dependent.

• Data Dependency: The quality of output relies on the training data. Gaps or outdated information in the data can affect accuracy.

• Version Variability: Different model versions may produce varying results due to changes in architecture and training.

Users should critically evaluate the outputs and verify information from reliable sources, especially for significant topics.

License:

This repository is licensed under the MIT License. See the LICENSE file for more information.