| import os |
| os.environ["TRANSFORMERS_NO_TF"] = "1" |
| os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" |
|
|
| import warnings |
| warnings.filterwarnings("ignore", category=FutureWarning) |
|
|
| from transformers import pipeline, logging |
| logging.set_verbosity_error() |
|
|
| import gradio as gr |
|
|
| model = pipeline( |
| "summarization", |
| model="cnicu/t5-small-booksum", |
| framework="pt" |
| ) |
|
|
| def predict(text): |
| summary = model(text, max_length=50, min_length=25, do_sample=False) |
| return summary[0]['summary_text'] |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("## Text Summarization with Hugging Face Transformers") |
| input_text = gr.Textbox(label="Input Text", lines=10) |
| output_text = gr.Textbox(label="Summary", lines=5) |
| summarize_button = gr.Button("Summarize") |
| |
| summarize_button.click(predict, inputs=input_text, outputs=output_text) |
|
|
| demo.launch() |
