Gemini LLM Applications: Text, Image, and Chat History

Welcome to the Gemini LLM Applications repository! This project demonstrates three innovative applications of the Gemini LLM (Large Language Model) for text-based question-answering, image-based analysis, and interactive chat history preservation. Built with Streamlit, these applications provide seamless integration with the Gemini API for dynamic and intelligent user interactions.

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Table of Contents

• Project Overview
• Features
• Installation
• Usage
  • Text-Based Q&A Application
  • Image-Based Analysis Application
  • Chat History Preservation Application
• Technologies Used
• Future Enhancements

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Project Overview

This repository contains three unique applications that showcase the capabilities of the Gemini LLM in various contexts:

1. Text-Based Q&A: Provides instant responses to user questions using natural language understanding.
2. Image-Based Analysis: Analyzes uploaded images and generates insights based on the image content and user-provided prompts.
3. Chat History Preservation: Maintains an interactive chat session with preserved history, enabling users to track their queries and responses seamlessly.

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Features

1. Text-Based Q&A Application

• Enter a query and receive instant, accurate answers.
• Powered by the Gemini LLM for robust text comprehension.
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2. Image-Based Analysis Application

• Upload an image (JPG, JPEG, PNG) and get detailed insights.
• Combines text prompts with image analysis for enhanced responses.
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3. Chat History Preservation Application

• Engage in a conversational experience with the Gemini LLM.
• Retain and display chat history for user convenience.
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Installation

To run the applications locally, follow these steps:

1. Create an API_KEY Update your API Key on ".env" file.
2. Create a virtual environment
3. Install dependencies: "pip install -r requirements.txt"
4. Set up the environment variables:
   • Create a .env file in the root directory.
   • Add your Google Gemini API key: "GOOGLE_API_KEY=your_api_key_here"
5. Run the desired application:
   • streamlit run app_text_based.py # For Text-Based Q&A
   • streamlit run app_image_based.py # For Image-Based Analysis
   • streamlit run app_chat_history.py # For Chat History Preservation

Usage

Text-Based Q&A Application

1. Launch the app.
2. Enter a question in the text input box.
3. Click the "Ask the Question" button to get an instant response.

Image-Based Analysis Application

1. Launch the app.
2. Upload an image (JPG, JPEG, PNG) using the file uploader.
3. Optionally, enter a text prompt for additional context.
4. Click "Tell about the image" to generate a response.

Chat History Preservation Application

1. Launch the app.
2. Enter a query in the text input box.
3. Click "Ask the question" to interact with the Gemini LLM.
4. View the entire chat history displayed at the bottom of the page.

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Technologies Used

• Python: Core programming language for development.
• Streamlit: Framework for building web applications.
• Google Generative AI: Gemini LLM API for natural language and image understanding.
• Pillow: Library for image processing.
• dotenv: For managing environment variables securely.

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Future Enhancements

• Support for multiple languages in the text-based Q&A application.
• Advanced image analysis with multimodal understanding.
• Options to download chat history in formats like PDF or JSON.
• User authentication for personalized experiences.

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Contact

For questions or feedback, reach out at:

• Ktrimalrao and trimalrao2004@gmail.com and www.linkedin.com/in/k-trimal-rao-397924253