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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
import gradio as gr
# Load model
tokenizer = AutoTokenizer.from_pretrained('AyushSoni14/text-summarizer-model')
model = AutoModelForSeq2SeqLM.from_pretrained('AyushSoni14/text-summarizer-model')
tokenizer.model_max_length = 1024
# Config
gen_config = GenerationConfig(
max_length=150,
min_length=40,
length_penalty=2.0,
num_beams=4,
early_stopping=True
)
# Summarization function
def summarize(blog_post):
input = tokenizer(blog_post, max_length=1024, truncation=True, return_tensors='pt')
summary_ids = model.generate(input['input_ids'], generation_config=gen_config)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
# Gradio UI
iface = gr.Interface(
fn=summarize,
inputs=gr.Textbox(lines=15, label="Enter Text to Summarize"),
outputs=gr.Textbox(label="Summary"),
title="Text Summarizer",
description="Enter a long paragraph or blog post to get a summarized version."
)
iface.launch(share=True)
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