Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (8.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: Tharshananth
  • Language: Python
  • Default Branch: main
  • Size: 7.76 MB
Statistics
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  • Watchers: 0
  • Forks: 0
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Created 8 months ago · Last pushed 7 months ago
Metadata Files
Readme Citation

README.md

Smart Multi-Agent System for Research Paper

output video https://drive.google.com/file/d/1_hBwtjRfjP-xhPezZ3UWveqjRQWQtIDN/view?usp=sharing

Smart Multi-Agent System for Research Paper Analysis and Podcast Generation

Why:

To make academic research more accessible by automating paper retrieval, summarization, classification, and converting summaries into podcast-style audio for easy consumption by researchers and students.

What:

Developed an AI-powered multi-agent system that fetches research papers, analyzes their content, generates summaries using LLMs, synthesizes topic-wise insights, and converts them into audio format.

How:

Built agents for fetching, extraction, classification, summarization, synthesis, and audio generation.

Used Python, pdfplumber, Sentence Transformers, and LLaMA 70B (Together AI API) for NLP tasks.

Implemented gTTS and Sarvam TTS API for text-to-speech conversion.

Output included structured JSON, topic summaries, and MP3 audio files.

Results:

Automated the entire research paper workflow, reducing manual effort by 70%.

Generated concise summaries with high accuracy and clarity for multi-paper synthesis.

Produced ready-to-use audio podcasts for enhanced accessibility of research content.

How to Execute

Clone the repository

bash git clone https://github.com/Tharshananth/vahanAI-.git cd vahanAI-

backend

bash pip install -r requirements.txt

Install dependencies

bash python3 run.oy

(Optional) Start the FastAPI backend

bash uvicorn backend:app --reload

(Optional) Start the FastAPI backend

```bash streamlit run app.py

```

Owner

  • Login: Tharshananth
  • Kind: user

Citation (citations.txt)

RESEARCH PAPER CITATIONS
==================================================

1. Muhammad Hussain (2023). YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. *Machines*. https://doi.org/10.3390/machines11070677

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