AI Project Using Streamlit and Gemini AI API

🔥 Excited to Share My Latest AI Project 🔥

This project leverages the power of Streamlit for the frontend and integrates with Gemini AI Flash 1.5 Model via the Gemini AI API. It allows users to interact with an AI-powered model through a simple web interface.

📋 Project Overview

This project demonstrates how to use Streamlit to build a frontend for an AI model, along with integrating the Gemini AI API for backend processing. It is designed to be fast and scalable.

Key Features:

• Interactive UI built with Streamlit
• AI-powered backend using Gemini AI Flash 1.5 model
• Real-time predictions and responses
• Easy to set up and deploy

🛠️ Technologies Used

• Frontend: Streamlit
• Backend: Gemini AI API (Flash 1.5 Model)
• Language: Python

🚀 Getting Started

Prerequisites To run this project, you will need to have the following installed:

• Python 3
• pip (Python package manager)

Setup

1. Clone the repository:

       github.com/omjadhav18/GeminiAPI_Project

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate   # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up your environment variables:

   Create a .env file in the root directory and add your Gemini API credentials:

   API_KEY="your-gemini-api-key"

5. Run the project:

   streamlit run main.py

6. Open the Streamlit app in your browser at http://localhost:8501.

📁 Project Structure

.
├── main.py                  # Main application file
├── environment_variables.py # File for managing environment variables
├── requirements.txt         # Dependencies list
├── .gitignore               # Files and directories to ignore in Git
└── README.md                # Project documentation

💡 Usage

After launching the app, you can interact with the AI by submitting queries through the Streamlit interface. The app sends the input to the Gemini AI Flash 1.5 model and returns predictions.

🤝 Contributing

1. Fork the repo.
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.

🔗 Connect with Me

Feel free to reach out if you have any questions or suggestions.

• LinkedIn: www.linkedin.com/in/om-jadhav-a25939288