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# Imports
import gradio as gr
from transformers import AutoModel, AutoProcessor
import torch
import requests
from PIL import Image
from io import BytesIO
from transformers import pipeline
# Import a specific model
model_name = "facebook/bart-large-cnn"
# Create a summarization pipeline with Facebook Bart
summarizer = pipeline("summarization", model=model_name)
# Define a function that takes input and returns summary
def summarize(input_text):
summary = summarizer(input_text, max_length=100, min_length=10, do_sample=False)
return summary[0]["summary_text"]
# Create a Gradio interface
interface = gr.Interface(
fn=summarize, # the function to wrap
inputs=gr.Textbox(lines=10, label="Input Text"),
outputs=gr.Textbox(label="Summary")
)
# Launch the interface
interface.launch() |