import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
model_name = "Amir230703/phi3-medmcqa-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

def generate_answer(question):
    input_ids = tokenizer(question, return_tensors="pt").input_ids.to(model.device)
    output = model.generate(
        input_ids,
        max_length=200,
        temperature=0.7,  # Controls randomness
        top_p=0.9,  # Nucleus sampling
        do_sample=True  # Enables variability
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Gradio Interface
demo = gr.Interface(
    fn=generate_answer,
    inputs=gr.Textbox(placeholder="Enter a medical question here..."),
    outputs=gr.Textbox(),
    title="Medical QA Model",
    description="Enter a medical question, and the AI will provide an answer."
)

demo.launch()