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Scientific PDF Analysis Tool

A Python application that processes scientific papers in PDF format and generates detailed scientific analyses using AI models (OpenAI GPT or DeepSeek).

Features

  • Text extraction from PDF files
  • Scientific analysis using AI models (OpenAI GPT-4/3.5 or DeepSeek)
  • Multiple AI provider support
  • Batch PDF processing
  • Automatic result reporting
  • Error handling and retry mechanism
  • Configurable model selection

Installation

  1. Clone the project:
git clone [repository-url]
  1. Install required packages:
pip install -r requirements.txt
  1. Create a .env file in the project root directory:
    • Copy .env.example to .env
    • Add your API keys (OpenAI and/or DeepSeek)
    • Select your preferred API provider and model

Example .env file:

# API Configuration
OPENAI_API_KEY=your-openai-api-key-here
DEEPSEEK_API_KEY=your-deepseek-api-key-here

# Model Selection
API_PROVIDER=openai     # Options: openai, deepseek
MODEL_NAME=gpt-4       # OpenAI options: gpt-4, gpt-3.5-turbo
                      # DeepSeek options: deepseek-chat, deepseek-coder

You can get your API keys from:

Usage

  1. Place your PDF files in the Papers directory:

    • The program will process all PDF files in this directory
    • Files are processed sequentially in alphabetical order
    • Each PDF is analyzed independently

  2. Run the program:

python gpt_paper.py
  1. Check results in the Result directory:
    • For each PDF, a corresponding .txt file is created
    • Output files are named as: [original_pdf_name]_output.txt
    • Results include detailed scientific analysis in a structured format

Important Notes

API Token Usage

  • Each PDF analysis consumes API tokens
  • Token consumption depends on:
    • Length of the PDF document
    • Selected model (GPT-4 costs more than GPT-3.5-turbo)
    • Selected API provider (pricing varies between OpenAI and DeepSeek)
    • Number of API calls made
  • Monitor your API usage at:

Processing Time

  • Processing time varies based on:
    • PDF size and complexity
    • Number of files being processed
    • API response time
    • Rate limiting and retry mechanisms
    • Selected API provider and model

Best Practices

  • Start with a small number of PDFs to test the system
  • Monitor the console output for processing status
  • Keep PDFs in English for best results
  • Ensure PDFs are text-searchable (not scanned images)
  • Check your API keys have sufficient credits before processing large batches
  • Compare results between different models and providers

Output Format

The analysis report generated for each PDF includes:

  1. Article Citation

    • Title
    • Authors
    • Journal
    • Volume and Issue
    • Publication Date
    • DOI
    • Publisher

  2. Research Purpose and Hypothesis

    • Research Topic
    • Hypothesis/Problem Statement

  3. Participants and Study Area

    • Participant Information
    • Study Area

  4. Methodology

    • Data Collection Method
    • Tools/Instruments Used
    • Data Analysis Method

  5. Results

    • Key Findings
    • Statistical Results

  6. Authors' Recommendations and Discussion

    • Research Success Status
    • Authors' Recommendations and Future Research

  7. Scientific Contribution and Strengths/Weaknesses

    • Strengths
    • Weaknesses

  8. Summary and Scientific Evaluation

Author

Burak Can KARA Email: burakcankara@gmail.com

License

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