Hugging Face's logo

Spaces:
Pupolina
/
Academic_Research_Assistant
Running

App Files Community
Academic_Research_Assistant / README.md
Pupolina's picture
Pupolina
Update README.md
85d190f verified
preview code
|
raw
history blame
4.87 kB
metadata
title: Academic Research Assistant
emoji: 😻
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.5.0
app_file: main.py
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

AI-Powered Academic Research Assistant

The AI-Powered Academic Research Assistant is a comprehensive, online chatbot designed to assist students and researchers in crafting high-quality academic works. This application leverages advanced AI technology to generate, refine, and analyze academic writing, allowing users to maintain formal style, grammatical precision, and logical coherence throughout their work.

Features

The main features of the AI-Powered Academic Research Assistant include:

  • Text Generation: The chatbot assists in generating contextually relevant academic text based on prompts or partial content already provided by the user. This helps users build on their ideas and expand on specific topics.
  • Grammar Correction: Uses a T5 Base Grammar Correction Model to ensure grammatical accuracy and clarity in the user's writing.
  • Formal Style Analysis: Evaluates the academic tone and formal style of the text, ensuring adherence to scholarly writing conventions via a specialized Style Transformer.

Technical Architecture

The application’s architecture is built to provide fast, reliable, and high-quality academic assistance using the following components:

Language Models and Components

  • Qwen2.5 1.5B Instruct: The main large language model (LLM) used for text generation, capable of producing nuanced and detailed academic content.
  • T5 Base Grammar Correction Model: Ensures grammatical correctness by identifying and correcting grammar issues in the user's input text.
  • Style Transformer: Preserves and enhances the formal academic style, adapting responses to align with scholarly writing conventions.
  • RAG (Retrieval-Augmented Generation) System: A vector database that consists of a carefully curated collection of academic works, which serves as a knowledge base for the LLM. This dataset provides the model with a rich source of context and reference, ensuring responses are both accurate and relevant (Base Dataset).
  • Routing Approach: Optimizes performance by directing specific types of user queries to the most suitable processing components within the application.

Front-End Interface

The front end of the application is designed for intuitive interaction, using:

  • Gradio API: Simplifies the creation of an interactive and user-friendly front-end.
  • Deployment on Hugging Face Spaces: Makes the app easily accessible online as a Hugging Face demo, enabling users to experience the chatbot’s features with minimal setup.

Installation and Usage

To use the AI-Powered Academic Research Assistant you can:

  1. Try the application online following the Demo link and accessing the deployed app via Hugging Face Spaces.

  2. Run local instance:

git clone https://github.com/Pupolina7/ResearchAssistant

pip install -r requirements.txt

python main.py

Future Enhancements

To expand the functionality and user experience of the AI-Powered Academic Research Assistant, the following features are planned for future updates:

  • Exclude Page Jumps: Address the current limitations in Gradio’s handling of real-time text updates within the response generation box, reducing interruptions and improving the flow of content as it’s generated. This feature will be implemented as soon as Gradio offers an update to support it.
  • 'Stop Generation' Button: Introduce a button that allows users to halt the text generation process mid-response, offering more control over the interaction.
  • File Content Writing: Enable the assistant to directly write generated or improved content into user-uploaded files, making it easier to incorporate edits without manual copy-pasting.
  • Markdown Support: Enhance the app’s capabilities to interpret and generate Markdown (MD) formatted content, ensuring compatibility with popular text editors and document formats.
  • LaTeX Support: Add support for LaTeX, enabling researchers in fields like mathematics, physics, and engineering to input and output complex equations and scientific notations seamlessly.
  • Improvement Hints: Provide contextual hints and suggestions for enhancing the clarity, coherence, or academic rigor of the user’s text, offering actionable feedback to elevate the quality of writing.