Create app.py

app.py

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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