app.py

import gradio as gr
from transformers import pipeline

print(gr. __version__)

def summarize_article(article, model_name, max_length, temperature, top_k, top_p):
    summarizer = pipeline("summarization", model=model_name)
    top_k = int(round(top_k))
    max_length = int(round(max_length))
    summary = summarizer(article, max_length=max_length, min_length=30, do_sample=True, temperature=temperature, top_k=top_k, top_p=top_p)
    return summary[0]['summary_text']

iface = gr.Interface(
    fn=summarize_article,
    inputs=[
        "text", 
        gr.inputs.Dropdown(["Falconsai/text_summarization", "Other Models..."], label="Select Model"),
        gr.inputs.Number(default=100, label="Max-Length"),
        gr.inputs.Slider(minimum=0.1, maximum=1.999, default=0.7, label="Temperature"), 
        gr.inputs.Number(default=50, label="Top-k"),
        gr.inputs.Number(default=0.9, label="Top-p")
    ],
    outputs="text",
    title="Text Summarization with Hyperparameters",
    description="Enter an article, select a model, and adjust hyperparameters for summarization."
)

iface.launch(debug=True)