BocchiChen / IDS721-project9-xc202

Mini Project 9 - Streamlit App with a Hugging Face Model

This project is a language model application built using Streamlit and Hugging Face Transformers libraries, allowing users to generate text interactively with pretrained models. It features model selection, streamlined user interface, and can be applied in various domains like creative writing, education, and AI assistants. With continuous improvement plans, it aims to provide a seamless text generation experience for users.

Code Overview

1. Imports:

   • Imported the pipeline function from the Streamlit library and the Hugging Face Transformers library.

2. Load Model:

   • Loaded a text generation model using the pipeline("text-generation", model="openai-gpt") statement, specifying the model as OpenAI GPT.

3. Main Function:

   • Defined a main function to build the Streamlit application.
   • Configured the page layout and styling using st.set_page_config.
   • Added the application title and a horizontal line using st.title and st.markdown respectively.
   • Provided a description of the application using st.write.
   • Created a sidebar for user input with st.sidebar.
     • Added a subheader and a text area for users to input text.
   • Added a button to trigger text generation using st.button.
   • Upon button click, generated text using the pre-trained language model and displayed it using st.write.

4. Run the App:

   • Used if __name__ == "__main__": to start the main function of the application.

Project Setup

Requirements:

• Streamlit: To create the interactive web application.
• Transformers: For utilizing pretrained language models.
• TensorFlow: Required for TensorFlow-based models or operations.
• tf-keras: Necessary for Keras functionalities in TensorFlow.

Installation:

You can install the required libraries using pip:

pip install streamlit transformers tensorflow tf-keras

Usage:

Once the libraries are installed, you can start the Streamlit application locally by running:

streamlit run streamlit_app.py

You can use the app function by accessing the following Web URL:

Project Deployment on Streamlit

1. Access the official Streamlit website https://streamlit.io/ and signin to your account.

2. Create a new app connecting to the GitHub account.

3. Deploy the app, the process can be slow.

4. Once the app has been deployed, you can access the streamlit application using the web url: https://ids721-project9-xc202-byao4pryw27go8ilsnklhn.streamlit.app/