app.py

import gradio as gr
from transformers import pipeline

# Load BioGPT model
bio_gpt = pipeline("text-generation", model="microsoft/biogpt")

def medical_chatbot(query):
    try:
        result = bio_gpt(
            query, 
            max_length=300,  # Increased length for full response
            num_return_sequences=5, 
            temperature=5.9,   # Higher temperature for diverse responses
            top_p=0.95         # Uses nucleus sampling instead of fixed top_k
        )
        generated_text = result[0]["generated_text"]  # Full response without trimming
        return generated_text
    except Exception as e:
        print(f"Error: {e}")
        return "An error occurred."

# Create Gradio interface
iface = gr.Interface(
    fn=medical_chatbot,
    inputs=gr.Textbox(placeholder="Ask me a medical question..."),
    outputs="text",
    title="Medical Chatbot"
)

iface.launch(share=True)