Create app.py

app.py

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+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()