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| title: Research Companion | |
| emoji: 🏢 | |
| colorFrom: pink | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 5.5.0 | |
| app_file: app.py | |
| pinned: false | |
| short_description: AI tool turning Academic papers into podcasts | |
| # AI Research Companion - Transforming Research Papers into Podcasts | |
| ## Overview | |
| The AI Research Companion is an innovative tool designed to make academic research more accessible. It transforms complex, text-heavy research papers into audio podcasts, enabling users to consume academic content in a more engaging and convenient way. | |
| This project was initially developed during the Smart India Hackathon (SIH) in 2023 to address the overwhelming challenge of managing and understanding a large number of research papers. It leverages large language models (LLMs) to extract relevant text, generate readable transcripts, and convert these into audio podcasts. | |
| ## Features | |
| - **Text Extraction:** Extracts content from uploaded PDFs to create clean, readable text. | |
| - **Transcript Generation:** Uses AI to generate a coherent transcript from the extracted text. | |
| - **TTS (Text-to-Speech):** Converts the refined transcript into an audio file. | |
| - **Editable Transcript:** Users can modify the transcript before converting it into audio, allowing for better control over the final output. | |
| - **Audio Output:** Listen to the final generated podcast from the research paper. | |
| ## Development Status | |
| The tool is still under development with plans to: | |
| - Integrate web search capabilities to find related research. | |
| - Explore additional Text-to-Speech engines to enhance the audio output. | |
| ## Requirements | |
| - Python 3.7 or higher | |
| - Gradio | |
| - Various AI/LLM APIs (configured in the `config` directory) | |
| - Edge TTS for audio generation | |
| ## Setup Instructions | |
| 1. Clone this repository to your local machine: | |
| ```bash | |
| git clone <repository_url> | |
| ``` | |
| 2. Install the required dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Set up API keys for the LLM models in the `config` directory. | |
| ## Usage | |
| 1. **Upload PDF:** Start by uploading a research paper in PDF format. | |
| 2. **Select Model:** Choose the text model for processing the document. | |
| 3. **Text Preview:** Preview the extracted text before proceeding. | |
| 4. **Transcript Preview:** Review the generated transcript and make edits if needed. | |
| 5. **TTS Output:** After finalizing the transcript, generate the audio podcast from the text. | |
| ## Note: | |
| This tool uses APIs for LLMs, but if GPUs are available, you can easily switch the API base to local models like "ollama" for enhanced performance. | |
| ## Acknowledgements | |
| Special thanks to [yasserrmd](https://huggingface.co./spaces/yasserrmd/NotebookLlama) for inspiring the structured prompts that guide this project. | |
| ## License | |
| This project is open source under the MIT License. Feel free to contribute and improve the tool. |