Advanced AI analysis for academic publications

BibAI Filter is a sophisticated desktop application designed for researchers and academics who need to efficiently filter large volumes of scholarly publications. Using state-of-the-art AI models, this tool analyzes titles, abstracts, and keywords from your Excel-based publication lists to identify the most relevant papers for your research topics.

• Seamless Data Import: Easily load Excel files (.xlsx or .xls) containing your publication databases
• Flexible Column Selection: Define which columns contain titles, abstracts, and keywords
• AI-Powered Analysis: Score publications based on relevance to your specified research topic using advanced AI models
  • Supported AI Providers: OpenAI, Anthropic, Google AI, DeepSeek, Mistral AI, Cohere, Azure OpenAI (with appropriate API keys)
• Smart Filtering: Filter publications based on a customizable relevance threshold
• Comprehensive Results: Export filtered publications to a new Excel file with original data and AI relevance scores
• Real-Time Progress Tracking: Monitor the filtering process with an intuitive progress indicator
• User-Friendly Interface: Clean and intuitive PyQt5-based interface for a smooth user experience

1. Clone the Repository

   git clone https://github.com/bcankara/BibAIFilter.git

   cd BibAIFilter

2. Create a Virtual Environment (Recommended)

   python -m venv .venv
   
   # On Linux/macOS
   source .venv/bin/activate
   
   # On Windows
   .venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

4. Launch the Application

   python main.py

1. Start the application

2. Configure AI Settings

   • Navigate to the "Settings" tab
   • Select your preferred AI Provider (e.g., OpenAI, Anthropic)
   • Enter your API Key for the selected provider
   • Choose an appropriate AI Model

3. Load and Filter Publications

   • Switch to the "Input & Filtering" tab
   • Click "Select Excel File" to load your publication database
   • Specify which columns contain Titles, Abstracts, and Keywords
   • Enter your Research Topic in the text field (e.g., "Quantum Computing in Cryptography")
   • Adjust the "Relevance Threshold" slider to set filtering sensitivity (value between 0 and 1)
   • Select an output location using "Choose Output File"
   • Start the process by clicking "Begin Filtering"

4. Review Results

   • When processing completes, the filtered results will be saved to your specified output file
   • The log area will show a summary of the operation

All dependencies are listed in the requirements.txt file. Key requirements include:

• Python 3.8+
• PyQt5
• pandas
• openpyxl
• xlrd
• openai
• anthropic
• google-generativeai
• requests

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please adhere to coding standards and clearly describe your changes.

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

API keys are sensitive information and should be handled securely. The application stores keys locally in the config/API_Settings.json file, which is excluded from version control.