PaperStream transforms how you interact with academic papers, combining AI-powered summaries, interactive Q&A, and dynamic visual data insights directly from any uploaded PDF.

• Streamlit: For creating a sleek, user-friendly web interface.
• PyPDF2: To extract text from uploaded PDF files.
• google.generativeai: To access cutting-edge AI models for text summarization and interaction.
• Matplotlib and Seaborn: For generating statistical graphics.
• WordCloud: To visualize key terms from the documents.
• Dotenv: For managing environment variables securely.

PaperStream addresses the challenge of quickly digesting complex academic documents, enhancing accessibility through summaries, and interactive AI-driven exploration. It aids in visualizing key concepts and terms without reading the entire document, saving time and enhancing understanding.

1. Clone the repository and navigate to the project directory.
2. Install required dependencies using pip install -r requirements.txt.
3. Run the Streamlit application with streamlit run app.py.
4. Upload a PDF to view its content, generate summaries, and interact with the embedded AI.

Building PaperStream revealed the significant potential of integrating AI with document handling, pushing the boundaries of traditional reading methods.

• Implementing AI APIs in a web application.
• Advanced text manipulation and extraction techniques.
• Building interactive data visualizations dynamically based on user input.

• How to optimize PDF text extraction for better accuracy?
• Handling large PDF files without sacrificing performance.
• How to improve the AI model's summarization accuracy and relevance?
• How to reduce the LLM model's response time for better user experience?

https://techin510-final.streamlit.app/