Boost Your Productivity with Langchain's PDF Summarizer App

Table of Contents

1. Introduction
2. Installation
3. Setting up OpenAI API
4. Building the PDF Summarizer
   • Extracting text from the PDF files
   • Loading and splitting the text
   • Creating the summarization chain
   • Summarizing the files
5. Creating the Streamlit application
   • Allowing users to upload PDF files
   • Generating summaries
6. Conclusion

Introduction

In this tutorial, we will learn how to build a simple multiple PDF summarization application using Python, OpenAI, and Streamlit. This application allows users to upload two or more PDF documents and generates a summary for each document. We will walk through the code and demonstrate how the application works.

Installation

Before we begin, make sure You have the necessary packages installed. We will be using OpenAI, Streamlit, and the PyPDF2 library. If you don't have them installed, you can install them by running the following commands:

pip install openai
pip install streamlit
pip install PyPDF2

Once the installations are complete, we can proceed to set up the OpenAI API.

Setting up OpenAI API

To use the OpenAI API, you need an API key. If you don't have one, you can sign up for an OpenAI account and obtain the API key. Once you have your API key, you need to set it up as an environment variable. This allows your code to access the API key securely without hardcoding it. Here's how you can set up the environment variable:

1. Import the os module: import os
2. Set the environment variable: os.environ['OPENAI_API_KEY'] = 'your_api_key'

Now that we have set up the API key, we can proceed to build the PDF summarizer.

Building the PDF Summarizer

The PDF summarizer consists of several steps: extracting text from the PDF files, loading and splitting the text, creating the summarization chain, and summarizing the files.

Extracting text from the PDF files

First, we need to extract the text from the PDF files. We will Create a function called summarizer_pdf_from_folder that takes a folder path as input. This function will iterate through the PDF files in the folder, create temporary files to hold the extracted text, and load the PDF files into memory.

Loading and splitting the text

After extracting the text, we need to load it into memory and split it into smaller chunks. We will use the PyPDF2 library to load the PDF files and split the text into paragraphs or sentences.

Creating the summarization chain

Next, we create a summarization chain using the OpenAI API. We pass in the extracted text as input and set the temperature parameter to control the randomness of the output. A higher temperature value generates more random output, while a lower value produces more focused and deterministic output.

Summarizing the files

Using the summarization chain, we summarize each file by calling the load_summarization_chain function. The function takes the loaded text as input and returns a summary. We append each summary to a list of summaries.

Now that we have built the PDF summarizer, let's proceed to create the Streamlit application.

Creating the Streamlit application

The Streamlit application allows users to upload PDF files and generate summaries. We will create a user interface that consists of a file uploader and a generate summary button.

Allowing users to upload PDF files

We use the st.file_uploader function to allow users to upload PDF files. We set the accept_multiple_files parameter to True so that users can upload more than one file.

Generating summaries

Once the PDF files are uploaded, we generate the summaries when the user clicks on the generate summary button. We call the summarizer_pdf_from_folder function with the uploaded PDF files as input. We iterate through the list of summaries and display them using the st.write function.

Conclusion

In this tutorial, we have learned how to build a simple multiple PDF summarization application using Python, OpenAI, and Streamlit. We have explored the steps involved in building the PDF summarizer and creating the Streamlit application. With this application, users can easily summarize multiple PDF documents with just a few clicks.

Now that you have the knowledge to build your own PDF summarization application, feel free to explore and customize it according to your requirements. Happy coding!

Highlights

• Learn how to build a simple multiple PDF summarization application
• Use Python, OpenAI, and Streamlit
• Extract text from PDF files
• Summarize PDF documents automatically
• Create a user-friendly interface for uploading files and generating summaries

FAQ

Q: Can I upload more than two PDF files? A: Yes, you can upload as many PDF files as you want using the file uploader.

Q: Can I customize the summarization process? A: Yes, you can adjust the temperature parameter to control the randomness of the output and fine-tune the summarization process according to your needs.

Q: Can I summarize other types of documents, not just PDFs? A: Currently, the application supports only PDF files. However, you can modify the code to handle other document formats by using appropriate libraries.

Q: Is the summarization process accurate? A: The accuracy of the summarization process depends on various factors, including the quality of the PDF files, the length of the documents, and the language used. It is recommended to review and refine the generated summaries for accuracy.