chunker

[jmikedupont2]

To create a Streamlit service that breaks up text into chunks by entities and defines each entity, you can use Natural Language Processing (NLP) libraries like spaCy to identify entities and then display the text broken into those chunks. Here's a step-by-step guide:

1. Set up your Python environment:

Make sure you have Python installed, and install the necessary libraries:

pip install streamlit spacy
python -m spacy download en_core_web_sm

2. Create a Streamlit app script:

Create a Python script (e.g., entity_chunker.py) and add the following code:

import streamlit as st
import spacy

# Load the English NLP model from spaCy
nlp = spacy.load("en_core_web_sm")

# Streamlit app title
st.title("Entity-Based Text Chunker")

# Input field for user to enter text
text = st.text_area("Enter text to chunk")

# Button to trigger the chunking process
if st.button("Chunk Text"):
    if text:
        # Process the input text with spaCy
        doc = nlp(text)

        # Initialize variables to store entities and their respective chunks
        entities = {}

        # Iterate through entities in the document
        for ent in doc.ents:
            entity_text = ent.text
            start = ent.start_char
            end = ent.end_char

            # Extract the chunk of text for the entity
            entity_chunk = text[start:end]

            # Store the entity and its chunk
            entities[entity_text] = entity_chunk

        # Display entities and their chunks
        if entities:
            st.subheader("Entities and Their Chunks:")
            for entity, chunk in entities.items():
                st.write(f"Entity: {entity}")
                st.write(f"Chunk: {chunk}")
                st.write("---")
        else:
            st.warning("No entities found in the text.")
    else:
        st.warning("Please enter text to chunk")

3. Run the Streamlit app:

Navigate to the directory containing your script in the terminal and run:

streamlit run entity_chunker.py

This will start the Streamlit app locally.

4. Usage:

• Enter text in the text area.
• Click the "Chunk Text" button to trigger the chunking process.
• The app will identify entities in the text and display each entity along with its corresponding chunk of text.

5. Deploy the Streamlit app (optional):

You can deploy your Streamlit app to a platform like Streamlit Sharing, Heroku, or your own server for public access.

This example demonstrates how to create a simple Streamlit app for entity-based text chunking. Depending on your needs, you can enhance the app by adding more features, such as selecting specific entity types or providing additional context for each entity.

[jmikedupont2]

That sounds like an interesting idea. I can help you create a Streamlit app that can chunk a larger text or URL into parts for sharing with me. I found some web search results that might be useful for you to get started. Here are some of them:

• splitter · Streamlit: This is an example of a Streamlit app that can split a text into chunks using a Text Splitter. You can adjust the parameters such as chunk size, chunk overlap, and length function to customize the output. You can also paste your own text or use the default one provided.
• LangChain tutorial #3: Build a Text Summarization app - Streamlit: This is a tutorial on how to build a text summarization app using Streamlit and LangChain, a platform that provides access to large language models (LLMs). The app uses the document loader, text splitter, and summarization chain to process the input text and generate a summary. You can modify the code to suit your needs and use a different LLM model if you want.
• Streamlit Web App | Build Web Applications using Streamlit: This is a blog post that shows how to build a web app for machine learning using Streamlit. The app uses the Iris dataset and a random forest classifier to make predictions based on user input. You can learn how to set up the coding environment, construct the sidebar, plot charts, and deploy the app.

I hope these resources are helpful for you. If you have any questions or need more assistance, please let me know. I'm always happy to help you with your needs. 😊

Source: Conversation with Bing, 9/14/2023 (1) splitter · Streamlit. https://share.streamlit.io/app/langchain-text-splitter/. (2) LangChain tutorial #3: Build a Text Summarization app - Streamlit. https://blog.streamlit.io/langchain-tutorial-3-build-a-text-summarization-app/. (3) Streamlit Web App | Build Web Applications using Streamlit. https://www.analyticsvidhya.com/blog/2021/06/build-web-app-instantly-for-machine-learning-using-streamlit/. (4) LangChain tutorial #3: Build a Text Summarization app. https://medium.com/streamlit/langchain-tutorial-3-build-a-text-summarization-app-d67ca72f2190. (5) Layouts and Containers - Streamlit Docs. https://docs.streamlit.io/library/api-reference/layout.