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| import torch | |
| import pandas as pd | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| import gradio as gr | |
| # Initialize models and tokenizers for Healthcare and AI perspectives | |
| healthcare_model_name = 'facebook/bart-large-cnn' # Healthcare-focused model | |
| ai_model_name = 'facebook/bart-large-xsum' # AI-focused model | |
| 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 for both Healthcare and AI agents | |
| 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) | |
| def healthcare_agent(abstract): | |
| return generate_summary(abstract, healthcare_tokenizer, healthcare_model) | |
| def ai_agent(abstract): | |
| return generate_summary(abstract, ai_tokenizer, ai_model) | |
| # Interaction 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." | |
| # Combine both implications to provide a holistic view | |
| combined_implications = f"{healthcare_implication}\n\n{ai_implication}" | |
| return combined_implications | |
| # Function to process the CSV and generate results | |
| def process_and_generate_implications(csv_file): | |
| # Read the input CSV file containing titles and abstracts | |
| papers_df = pd.read_csv(csv_file.name, encoding='latin-1') | |
| # Check if 'title' and 'abstract' columns exist | |
| required_columns = ['title', 'abstract'] | |
| if not all(col.lower() in papers_df.columns.str.lower() for col in required_columns): | |
| return "The CSV must contain 'title' and 'abstract' columns." | |
| # Drop rows where title or abstract is missing | |
| papers_df = papers_df.dropna(subset=['title', 'abstract']) | |
| results = [] | |
| # Process each paper (row) in the CSV | |
| for _, row in papers_df.iterrows(): | |
| title = row['title'] | |
| abstract = str(row['abstract']) | |
| # Generate summaries using both agents | |
| healthcare_summary = healthcare_agent(abstract) | |
| ai_summary = ai_agent(abstract) | |
| # Generate the implications based on both summaries | |
| implications = generate_implications(healthcare_summary, ai_summary) | |
| # Store the results | |
| results.append({ | |
| "Title": title, | |
| "Abstract": abstract, | |
| "Healthcare Summary": healthcare_summary, | |
| "AI Summary": ai_summary, | |
| "Implications": implications | |
| }) | |
| # Convert results into a DataFrame | |
| results_df = pd.DataFrame(results) | |
| # Return the results as a CSV string for download | |
| return results_df.to_csv(index=False) | |
| # Define Gradio interface | |
| def create_interface(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## Research Paper Summarization and Implications") | |
| gr.Markdown("Upload a CSV file with 'title' and 'abstract' columns to generate healthcare and AI implications.") | |
| # Upload CSV file | |
| csv_input = gr.File(label="Upload CSV File", type="file") | |
| # Button to process the CSV and generate results | |
| output_csv = gr.File(label="Download Results CSV") | |
| # Process CSV and generate implications on button click | |
| csv_input.change(process_and_generate_implications, inputs=csv_input, outputs=output_csv) | |
| return demo | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch(debug=True) # Set debug=True to see detailed logs | |