FINDA

FINDA is a web application that enhances interaction with a language model (LLM) assistant, designed to generate intelligent responses and conduct web searches to augment information delivery. This integration fetches additional data from Google, YouTube, and Wikipedia, enriching user interactions.

FINDA is built to provide a cutting-edge, intuitive interface where users can engage with an LLM assistant. The goal is to deliver not only direct answers but also extended information through seamless web searches.

• Chat Interface

  • Sleek, modern chat design enabling users to send messages through a text input field at the bottom.
  • Displays conversation history above, allowing users to follow the dialogue seamlessly.

• Assistant Instructions Input Field

  • Positioned at the top of the chat interface for users to guide the assistant's operations.

• Search Integration

  • Google Search: Integrated checkbox (enabled by default) adjacent to the message input to trigger searches based on LLM-generated keywords.
  • Additional Search Engines: Options to extend searches to YouTube and Wikipedia via checkboxes.

• Language Model Assistant Integration

  • Incorporates Groq as the LLM assistant, processing user inputs and formulating responses.
  • Extracts pertinent keywords from responses to trigger web searches.

• Search Initiation

  • Dynamically starts searches on Google, YouTube, and Wikipedia depending on user selection and extracted keywords.

• Display Search Results

  • Efficiently fetches and displays search results under the LLM’s responses within the chat interface.

• Crafted using the React.js framework, emphasizing responsiveness, accessibility, performance, and secure practices.

• Developed in Python, ensuring secure API interactions.
• Optimizes the extraction of keywords and initiates searches effectively.

• Ensure installation of Node.js, npm, Python, pip, and Git.

1. Clone the Repository

   git clone https://github.com/YOUR_GITHUB_USERNAME/YOUR_REPOSITORY_NAME.git
   cd YOUR_REPOSITORY_NAME

2. Setup Backend

   • Navigate to the backend directory

     cd backend

   • Create and activate a virtual environment

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

   • Install dependencies

     pip install -r requirements.txt

   • Download NLTK data packages

     python -m nltk.downloader punkt stopwords

   • Create a .env file with your API keys

     GROQ_API_KEY=your_groq_api_key
     GOOGLE_API_KEY=your_google_api_key
     SEARCH_ENGINE_ID=your_search_engine_id
     YOUTUBE_API_KEY=your_youtube_api_key
     

   • Run the backend server

     python app.py

3. Setup Frontend

   • Navigate to the frontend directory

     cd ../frontend

   • Install dependencies

     npm install

   • Start the frontend development server

     npm start

4. Open the Application

   Open your browser and go to http://localhost:3000 to use the FINDA application.

1. Enter your message in the chat input field at the bottom of the chat interface.
2. Optionally, provide instructions for the assistant in the top input field.
3. Select the search options (Google, YouTube, Wikipedia) as needed.
4. Press the "Send" button to receive a response from the LLM and see search results if applicable.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License. See the LICENSE file for details.

For any questions or suggestions, please open an issue or contact the repository owner.

Replace YOUR_GITHUB_USERNAME and YOUR_REPOSITORY_NAME with your actual GitHub username and repository name. This README file provides a comprehensive overview of the project, installation instructions, usage guidelines, and contribution information.# Getting Started with Create React App

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