As a researcher or scientific writer, you may often find yourself needing to quickly understand and summarize complex scientific articles. Luckily, there are tools that can help make this overwhelming process easier. In this blog post, we’ll look at how to use OpenAI’s GPT-3 language model and the Streamlit library to create a simple application for generating summaries of scientific articles.
Here's a step-by-step guide on how to build a scientific article summarizer using OpenAI and Streamlit:
- Set up your environment:
- Install the necessary libraries, including OpenAI's API wrapper and Streamlit.
- Obtain an API key from OpenAI to use their GPT 3.5 model.
- Fetch the scientific articles:
- Access the scientific articles either from a local database or through web scraping from trusted sources.
- Preprocess the articles:
- Clean the text to remove unnecessary characters, formatting, and citations.
- Split the articles into paragraphs or sentences for better summarization.
- Implement the summarization function:
- Utilize OpenAI's GPT-3.5 model to generate summaries.
- Pass the article text to the model and receive a summary in return.
- Build the Streamlit application:
- Create a Streamlit app with a simple user interface.
- Allow users to input the article they want to summarize.
- Display the summarized output:
- Process the user input and run it through the summarization function.
- Show the generated summary on the Streamlit app interface.
- Improve user experience:
- Add error handling for invalid inputs or failed API calls.
- Customize the Streamlit app with additional features like word limits, summarization quality adjustments, or multiple language support.
- Deploy the application:
- Deploy the Streamlit app on a web server so that users can access it from anywhere.