https://github.com/di37/research-paper-analyst
A Streamlit-powered web application that leverages the Gemini model’s extensive 2 million token context window to analyze lengthy research papers (PDFs) and answer follow-up questions in real-time.
https://github.com/di37/research-paper-analyst
chatbot google-gemini google-generative-ai large-language-models natural-language-processing research-assistant streamlit
Last synced: 6 months ago
JSON representation
A Streamlit-powered web application that leverages the Gemini model’s extensive 2 million token context window to analyze lengthy research papers (PDFs) and answer follow-up questions in real-time.
- Host: GitHub
- URL: https://github.com/di37/research-paper-analyst
- Owner: di37
- License: mit
- Created: 2024-12-21T00:43:15.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-21T02:25:25.000Z (10 months ago)
- Last Synced: 2025-02-14T09:40:23.742Z (8 months ago)
- Topics: chatbot, google-gemini, google-generative-ai, large-language-models, natural-language-processing, research-assistant, streamlit
- Language: Python
- Homepage:
- Size: 364 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI powered - Research Paper Analyst
This application uses the Gemini model to provide in-depth analysis of research papers (in PDF format). By leveraging Gemini’s extended context window—capable of handling up to 2 million tokens—this tool can retain and reference large sections of a research paper at once, enabling a more comprehensive understanding and more accurate responses to user queries.
## Features
- **PDF Upload & Analysis:** Upload a PDF research paper and initiate automatic analysis.
- **Real-Time Streaming Responses:** View analysis results and Q&A responses as they are generated.
- **Follow-Up Queries:** After analysis, ask questions about the paper's content.
- **Model Selection:** Choose from a list of available Gemini models for processing.
- **API Key Configuration:** Provide your Gemini API key to authenticate and use the chosen model.
## Requirements
- Python 3.8 or higher
- [Streamlit](https://streamlit.io/) for the web interface
- Gemini-related Python packages (as defined in `requirements.txt` or the project’s `utilities` module)
- A Gemini API Key
## Installation
1. **Clone the Repository:**
```bash
git clone https://github.com/di37/research-paper-analyst
cd research-paper-analyst
```
2. **Install Dependencies:**
If there is a `requirements.txt` file provided:
```bash
pip install -r requirements.txt
```
Make sure all required packages such as `streamlit` and Gemini utilities are installed. If not, install them individually:
```bash
pip install streamlit
# ... any other required packages
```
3. **Set Up Your Gemini API Key:**
This project requires a Google API key for authenticated requests which you can get from [Google AI Studio](ai.google.dev). To set this up:
1. **Locate the `.env` File:**
Copy the provided `.env.example` file to `.env` and open it in your preferred text editor.
```bash
cp .env.example .env
```
2. **Add Your Gemini API Key:**
In your newly created `.env` file, replace the placeholder value with your actual Google API key:
```env
GOOGLE_API_KEY=your_actual_google_api_key_here
```
3. **Save and Verify:**
Once saved, the application will load your API key from the `.env` file at runtime. Ensure you keep your key secure and never commit `.env` files containing sensitive information to version control.
---
By following the steps above, you can ensure the Gemini API key is properly configured before running the application.
## Usage
### Running the Application
1. **Run the Application:**
```bash
streamlit run main.py
```
This will start the Streamlit app on a local server. The terminal output will display a local URL (e.g., `http://localhost:8501`).
2. **Open in Browser:**
Open the displayed URL in your web browser. You’ll see the app’s interface.
3. **Configure the Application:**
- In the sidebar, select a Gemini model from the dropdown.
- Enter your Gemini API key.
- Upload a PDF file that you want to analyze.
4. **Analyze the Paper:**
Click the **"Analyze Paper"** button after uploading your PDF. The app will:
- Initialize the selected Gemini model.
- Process your PDF and display analysis results in real-time.
5. **Ask Questions:**
After analysis, a chat interface is available at the bottom of the main page. Type a question related to the analyzed paper and press **Enter**. The app will provide a streamed response.
6. **Clear Chat (Optional):**
To clear all previous messages and start fresh, click the **"Clear Chat"** button in the sidebar.
### Screenshots
## Development Notes
- The main user interface logic resides in `main.py`.
- Gemini-specific logic is encapsulated in a separate module (e.g., `gemini_interface.py`).
- Utilities such as model initialization, PDF encoding, and constant definitions (like `GEMINI_MODELS`) are found in the `utilities` module.
- For local development, ensure that paths and imports are configured correctly.
## Contributing
If you would like to contribute:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Open a Pull Request with a detailed description of your changes.
## License
This project is provided under the terms specified by the repository owner (add your license details here, e.g., MIT, Apache 2.0, GPL-3.0, etc.). If no license is specified, the default is "All Rights Reserved."
---
**Disclaimer:** This application relies on the Gemini model and its associated API. Ensure you have the necessary permissions and adhere to the API provider’s usage and billing policies.