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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch
# Load grammar correction model
grammar_model_name = "prithivida/grammar_error_correcter_v1"
grammar_tokenizer = AutoTokenizer.from_pretrained(grammar_model_name)
grammar_model = AutoModelForSeq2SeqLM.from_pretrained(grammar_model_name)
# Load summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
# Load title generation model (T5)
title_model_name = "t5-base"
title_tokenizer = AutoTokenizer.from_pretrained(title_model_name)
title_model = AutoModelForSeq2SeqLM.from_pretrained(title_model_name)
def polish_abstract(text):
# --- 1. Grammar Correction ---
grammar_input = grammar_tokenizer("gec: " + text, return_tensors="pt", max_length=512, truncation=True)
grammar_output = grammar_model.generate(**grammar_input, max_length=512)
corrected_text = grammar_tokenizer.decode(grammar_output[0], skip_special_tokens=True)
# --- 2. Summary Generation ---
summary_text = summarizer(corrected_text, max_length=120, min_length=40, do_sample=False)[0]['summary_text']
# --- 3. Title Generation ---
title_prompt = "generate title: " + corrected_text
title_inputs = title_tokenizer(title_prompt, return_tensors="pt", max_length=512, truncation=True)
title_output = title_model.generate(**title_inputs, max_length=20, num_beams=5)
generated_title = title_tokenizer.decode(title_output[0], skip_special_tokens=True)
return corrected_text, summary_text, generated_title
# Gradio interface
iface = gr.Interface(
fn=polish_abstract,
inputs=gr.Textbox(lines=12, label="Paste Your Abstract"),
outputs=[
gr.Textbox(label="✅ Grammar-Corrected Abstract"),
gr.Textbox(label="🪄 Concise Summary"),
gr.Textbox(label="📘 AI-Suggested Title"),
],
title="🧠 AI Abstract & Title Polisher",
description="This tool corrects grammar, summarizes, and suggests a title for biomedical abstracts."
)
iface.launch()
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