AI-Image-Recogniton-Chatbot-LLM-Model

AI Image Recogniton Chatbot LLM Model

This is an AI-Gemini Chatbot LLM And Large Image Model Application with Uing Gemini Pro Free Mode:-



This project is a Streamlit application that utilizes the Google Generative AI Gemini model to answer questions based on an input image or text. The application provides a user-friendly interface for uploading images, entering text prompts, and receiving responses from the Gemini model.

## Features

- Upload images in JPG, JPEG, or PNG format.
- Enter text prompts to ask questions or provide context
- Receive responses from the Gemini model based on the input image and/or text
- Responsive UI with custom CSS styling
- Streaming response display for longer or complex responses

## Installation

1. Clone the repository:

```bash
git clone https://github.com/your-username/gemini-image-qa-chatbot.git
  1. Navigate to the project directory:
cd gemini-image-qa-chatbot
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Create a .env file in the project directory and add your Google API key:
GOOGLE_API_KEY=your_google_api_key

Usage

  1. Run the Streamlit application:
streamlit run app.py
  1. The application will open in your default web browser.
  2. Upload an image or enter a text prompt in the provided input area.
  3. Click the “Ask the question” button to receive a response from the Gemini model.
  4. The response will be displayed in the application, with longer responses streamed in chunks.

Contributing

Refer to > https://github.com/imSanko/AI-Image-Recogniton-Chatbot/blob/main/CONTRIBUTION.md —

Deployment

Local Deployment

To run the application locally, follow the “Usage” instructions above.

Cloud Deployment

This application can be deployed to various cloud platforms for public access. Here are some options:

Streamlit Sharing

Streamlit provides a free cloud service for sharing your Streamlit applications. Follow the instructions in the Streamlit Sharing documentation to deploy your application.

Cloud Services (e.g., AWS, GCP, Azure)

You can also deploy the application to various cloud services like AWS, Google Cloud Platform (GCP), or Microsoft Azure. Follow the respective documentation for deploying Python applications on your preferred cloud service.

License

This project is licensed under the MIT License. ```

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