app.py

import gradio as gr

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
 
# Load GPT-2 model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

def compare_claims(claim1, claim2):
    """
    Compare two insurance claims using GPT-2.
    """
    prompt = f"Compare these two health insurance claims:\n\nClaim 1: {claim1}\n\nClaim 2: {claim2}\n\nDifferences and similarities:"
    
    # Tokenize input
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    
    # Generate response
    gen_tokens = model.generate(
        input_ids,
        do_sample=True,
        temperature=0.7,  # Adjust creativity
        max_length=150,  # Limit output size
        pad_token_id=tokenizer.eos_token_id
    )

    # Decode and return output
    return tokenizer.decode(gen_tokens[0], skip_special_tokens=True)

def main():
    """
    Launch the Gradio interface for claim comparison.
    """
    # Define the Gradio interface
    interface = gr.Interface(
        fn=compare_claims,
        inputs=[
            gr.Textbox(label="claim1", placeholder="Enter first claim description..."),
            gr.Textbox(label="claim2", placeholder="Enter second claim description...")
        ],
        outputs="text",
        title="Claims Comparison",
        description="Enter two claims to compare their differences."
    )

    # Launch the Gradio app
    interface.launch()

if __name__ == "__main__":
    main()