Generating PDF Summaries Using ChatGPT with Python in Angular

 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.

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.

 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.

Step 4: Text Chunking with langchain

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 chunk_size and separator parameters help customize the splitting process to suit your needs.

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.

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.

Conclusion:

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.

Thank you for reading our blog! We hope you found it helpful. 

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