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import gradio as gr
import torch
from transformers import BartTokenizer, BartForConditionalGeneration
# Initialize tokenizers and models for both healthcare and AI
healthcare_model_name = 'facebook/bart-large-cnn'
ai_model_name = 'facebook/bart-large-xsum'
healthcare_tokenizer = BartTokenizer.from_pretrained(healthcare_model_name)
ai_tokenizer = BartTokenizer.from_pretrained(ai_model_name)
healthcare_model = BartForConditionalGeneration.from_pretrained(healthcare_model_name)
ai_model = BartForConditionalGeneration.from_pretrained(ai_model_name)
# Summarization function
def generate_summary(text, tokenizer, model):
inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True, padding="max_length")
with torch.no_grad():
outputs = model.generate(inputs["input_ids"], max_length=150, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Functions for each agent
def healthcare_agent(abstract):
return generate_summary(abstract, healthcare_tokenizer, healthcare_model)
def ai_agent(abstract):
return generate_summary(abstract, ai_tokenizer, ai_model)
# Function to generate implications based on both agents' insights
def generate_implications(healthcare_summary, ai_summary):
healthcare_implication = f"Healthcare Implications: {healthcare_summary} The healthcare sector can leverage these findings to improve patient care and treatment outcomes."
ai_implication = f"AI Implications: {ai_summary} These insights can further enhance AI models, making them more applicable in real-world healthcare scenarios."
combined_implications = f"{healthcare_implication}\n\n{ai_implication}"
return combined_implications
# Gradio Interface function
def summarize_and_generate_implications(abstract):
healthcare_summary = healthcare_agent(abstract)
ai_summary = ai_agent(abstract)
implications = generate_implications(healthcare_summary, ai_summary)
return healthcare_summary, ai_summary, implications
# Creating the Gradio interface
interface = gr.Interface(
fn=summarize_and_generate_implications,
inputs=gr.Textbox(label="Abstract", placeholder="Enter the abstract of a research paper..."),
outputs=[
gr.Textbox(label="Healthcare Summary"),
gr.Textbox(label="AI Summary"),
gr.Textbox(label="Implications")
],
live=True,
title="Research Paper Summarization and Implications",
description="This app generates summaries for healthcare and AI domains and provides implications for each."
)
# Launch the Gradio interface
interface.launch(share=True) # share=True will generate a public link
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