In today’s data-driven world, extracting valuable insights from a multitude of PDF documents is a common challenge. Fortunately, with the power of Python and AI, you can automate the process of summarizing PDFs using ChatGPT. In this blog, we’ll walk you through the steps to achieve this task efficiently.
How can I use ChatGPT to create a summary of a PDF document?
Please make sure to install the following dependencies: Flask, azure-cognitiveservices-vision-computervision, PyMuPDF, long-chain, and openai version 0.28.1.
Step 1: Uploading PDFs via Flask
We begin by setting up a Python application using Flask to create an API for PDF upload. Users can conveniently send their PDF documents through this interface, making the process user-friendly.
A Flask app with a route ‘/upload_pdf’ for POST requests. It handles PDF uploads and starts to convert them to JPG images using the
Step 2: Converting PDF Pages to JPG
To work with the content of PDFs, we utilize the
fitz library to convert each page of the PDF into a JPG image. This step ensures that the text within the PDF is in a format that can be processed further.
The code converts a PDF into JPG images, creating an output directory for the images. It loops through the PDF pages, adjusts the resolution, and saves them as images. Finally, it calls
Step 3: Optical Character Recognition (OCR)
With our PDF pages in image format, we employ Azure OCR Cognitive Services to extract text from each JPG file. This text is then compiled and organized into a single text file.
Azure_Clientfunction: This function sets up the Azure Cognitive Services client by providing the subscription key and endpoint.
txt_to_filefunction: This function writes text to a file provided by
ocr_single_filefunction: This function performs OCR on a single PDF file. It reads the PDF and retrieves the text content using Azure Cognitive Services. It waits for the operation to complete and returns the extracted text.
create_folderfunction: This function creates an output folder based on the PDF file’s name to store the OCR results in a text file.
Extract_text_from_jpgfunction: This function handles the PDF file upload and OCR conversion. It saves uploaded files, processes each image (jpg) in the PDF, and extracts text from them using Azure Cognitive Services. The extracted text is saved to a corresponding output folder as a text file.
Overall, the code processes PDF files, extracts text from images within them, and stores the extracted text in individual text files in output folders.
Step 4: Text Chunking with
To make the text more manageable, we implement the
langchain library and use its
RecursiveCharacterTextSplitter feature. This allows us to divide the text into smaller more digestible chunks. The
separator parameters help customize the splitting process to suit your needs.
The code utilizes the Langchain library for text splitting. It reads a text file, divides it into smaller chunks based on specified separators, and then saves the resulting chunks in a separate file. The code returns information about the processed chunks in a dictionary.
Step 5: Summarization with ChatGPT
As ChatGPT processes each text chunk, it generates corresponding summaries. These summaries are collected and assembled into a final text file. This consolidated document provides a concise yet comprehensive overview of the original PDF content.
The code uses the OpenAI API to generate summaries for text chunks. It loads the OpenAI API key from a local .env file, defines a function
get_completion to retrieve text completions, and another function
summarize_prompt to split text, generate summaries for each chunk, and write the summaries to an output file. The code is designed for summarizing text data related to the “example” domain.
Step 6: Delivering the Summarized Text
The final text file, containing all the summarized information, is ready to be delivered to the client. This step ensures that the extracted insights are readily accessible and easy to understand.
By following these steps, you can streamline the process of extracting valuable information from PDF documents using Python(OpenAI) and ChatGPT. This automated approach not only saves time but also ensures accuracy and consistency in your summarization tasks.
With Python, Flask, Azure OCR, ChatGPT, and thoughtful libraries like
langchain, you can transform PDFs into concise, actionable insights. By automating the summarization process, you save time and enhance your document handling efficiency. Embrace the power of AI and take your PDF summarization to the next level.
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