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Seamlessly Analyze PDF Content with OpenAI: Convert, Encode, and Extract Insights

Seamlessly Analyze PDF Content with OpenAI: Convert, Encode, and Extract Insights

Akash (SKY)'s photo
Akash (SKY)
·

4 min read

Introduction

Analyzing small PDFs can be a challenging task, but with OpenAI's advanced language models, it becomes much easier and more efficient. OpenAI can help you extract important information, summarize content, and interpret complex data from PDF files quickly. In this blog, we'll show you how to use OpenAI for small PDF analysis, providing practical steps to make the most out of this powerful tool.

Ways to Analyze PDFs

1. The OpenAI Way

2. The Vision-Based Approach

  • Convert the PDF to images.

  • Use OpenAI's vision capabilities to analyze the images.

The Vision-Based Approach

1. Import Required Libraries

First, we need to import the necessary libraries. These include pdf2image for converting the PDF to images, base64 for encoding the images, openai for interacting with OpenAI's API, and other standard libraries like json, requests, and os.

from pdf2image import convert_from_path
import base64
from openai import OpenAI
import json
import requests
import os

2. Define the PDF Path

Specify the path to the PDF file that you want to convert to images.

# Path to the PDF file
pdf_path = 'path to pdf'

3. Convert PDF to Images

Use the convert_from_path function from the pdf2image library to convert each page of the PDF into an image. The dpi parameter sets the resolution of the output images. For Windows users, you need to specify the path to the poppler library using poppler_path.

# Convert PDF to list of images
# For Windows download poppler and give the path for the poppler 
images = convert_from_path(pdf_path, dpi=40, poppler_path=r"path to poppler")

4. Encode Images in Base64

Define a function to encode an image file in base64 format. This will allow us to embed the image data directly in our request to the OpenAI API.

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

5. Prepare the Content for OpenAI API

Create a list to hold the content we will send to the OpenAI API. Start with a text prompt to guide the API on what to analyze.

content = [{
  "type": "text",
  "text": "Summarize the contents and explain it to a five year old"
}]

6. Save Images and Append to Content

Loop through the images generated from the PDF, save each one as a JPEG file, encode it in base64, and append the encoded image to the content list. After encoding, delete the temporary JPEG file.

# Save images to files
for i, image in enumerate(images):
    print(i)
    image.save(f'new_{i}.jpg', 'JPEG')
    encodedimage = encode_image(f'new_{i}.jpg')
    content.append({
        "type": "image_url",
        "image_url": {
            "url": f"data:image/jpeg;base64,{encodedimage}",
            "detail": "low"
        }
    })
    os.remove(f"new_{i}.jpg")

7. Send Content to OpenAI API

Instantiate the OpenAI client with your API key and send the content to the API for analysis. The response from the API will contain the analysis based on the text and images provided.

client = OpenAI(api_key='')
response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "user",
      "content": content
    }
  ]
)

8. Print the Response

Finally, print the response from the OpenAI API to see the analysis.

print(response.choices[0].message.content)

Summary

This tutorial demonstrates how to convert a PDF into images and analyze its content using OpenAI's API. This method is particularly helpful for extracting insights and summaries from small PDF documents. By converting each page of the PDF into images, encoding them in base64, and sending them to OpenAI, you can leverage advanced language models to gain valuable information quickly.

Key Points:

  • Efficient Analysis: Automates the process of analyzing PDF content, saving time and effort.

  • Image Encoding: Ensures that images from PDFs are correctly formatted for analysis.

  • Customization: Allows customization of the text prompt to guide the analysis according to specific needs.

Limitations:

  • Small PDFs: This approach is better suited for small PDF documents due to the potential complexity and size constraints of larger PDFs.

  • Verification Needed: Always verify the facts provided by ChatGPT, as the contents generated by the model may not always be accurate. Double-checking information is crucial to ensure reliability.

By understanding these points, users can effectively utilize this method while being aware of its best applications and limitations.

pdf-analyzeopenaiPython