GPT 3.5/4/4o-mini Powered Document Summarizer

This is a tool that takes a text document (PDF or TXT) or YouTube transcript and generates a concise summary using GPT-4O-Mini, GPT-4 or GPT-3.5-turbo. It can accurately summarize hundreds of pages of text. It's built with Python and Streamlit and leverages the langchain library for text processing. While the final output is generated with the latest GPT family model from OpenAI, GPT-4O-Mini (one of the LLMs that powers ChatGPT), only a small portion of the overall document is used in the prompts. Before any call is made to either LLM, the document is separated into small sections that contain the majority of the meaning of the document.

Summarize your documents here (no API key required): https://gpt-document-summarizer.streamlit.app/

Features

• Supports PDF and TXT file formats
• Utilizes GPT-4 or GPT-3.5-turbo for generating summaries
• Automatic clustering of the input document to identify key sections
• Customizable number of clusters for the summarization process

Usage

1. Launch the Streamlit app by running streamlit run main.py
2. Upload a document (TXT or PDF) to summarize.
3. Enter your OpenAI API key if the free usage cap has been hit.
4. Choose whether to use GPT-4 for the summarization (recommended, requires GPT-4 API access).
5. Click the "Summarize" button and wait for the result.

Modules

• main.py: Streamlit app main file
• utils.py: Contains utility functions for document loading, token counting, and summarization
• streamlit_app_utils.py: Contains utility functions specifically for the Streamlit app

Main Functions

• main(): Entry point for the Streamlit app
• process_summarize_button(): Processes the "Summarize" button click and displays the generated summary
• validate_input(): Validates user input and displays warnings for invalid inputs
• validate_doc_size(): Validates the document size for token limits

Utility Functions

• doc_loader(): Loads a document from a file path
• token_counter(): Counts the number of tokens in a text string
• doc_to_text(): Converts a langchain Document object to a text string
• doc_to_final_summary(): Generates the final summary for a given document
• summary_prompt_creator(): Creates a summary prompt list for the langchain summarize chain
• pdf_to_text(): Converts a PDF file to a text string
• check_gpt_4(): Checks if the user has access to GPT-4
• token_limit(): Checks if a document has more tokens than a specified maximum
• token_minimum(): Checks if a document has more tokens than a specified minimum