| import gradio as gr |
| from transformers import pipeline |
|
|
| summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base") |
|
|
| def predict(prompt): |
| summary = summarizer(prompt)[0]["summary_text"] |
| return summary |
|
|
| textbox = gr.Textbox(placeholder="Enter text to summarize", lines=6) |
| interface = gr.Interface(inputs=textbox, fn=predict, outputs="text", |
| title="Business Information Summarizer", |
| description="This web API presents an abstractive summary of the input text using a Large Language Model (LLM)", |
| allow_flagging="manual", flagging_options=["Useful", "Not Useful"]) |
|
|
| with gr.Blocks() as demo: |
| interface.launch() |
|
|
| demo.queue(concurrency_count=16) |
| demo.launch() |
|
|
