app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
import random
import re

# Load model and tokenizer
# model_name = "rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B"
model_name = "rgb2gbr/BioXP-0.5B-MedMCQA"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load dataset
dataset = load_dataset("openlifescienceai/medmcqa")

# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()

def get_random_question():
    """Get a random question from the dataset"""
    index = random.randint(0, len(dataset['train']) - 1)
    question_data = dataset['train'][index]
    return (
        question_data['question'],
        question_data['opa'],
        question_data['opb'],
        question_data['opc'],
        question_data['opd'],
        question_data.get('cop', None),  # Correct option (0-3)
        question_data.get('exp', None)   # Explanation
    )

def extract_answer(prediction: str) -> tuple:
    """Extract answer and reasoning from model output"""
    # Try to find the answer part
    answer_match = re.search(r"Answer:\s*([A-D])", prediction, re.IGNORECASE)
    answer = answer_match.group(1).upper() if answer_match else "Not found"
    
    # Try to find reasoning part
    reasoning = ""
    if "Reasoning:" in prediction:
        reasoning = prediction.split("Reasoning:")[-1].strip()
    elif "Explanation:" in prediction:
        reasoning = prediction.split("Explanation:")[-1].strip()
    
    return answer, reasoning

def predict(question: str, option_a: str, option_b: str, option_c: str, option_d: str, 
           correct_option: int = None, explanation: str = None,
           temperature: float = 0.6, top_p: float = 0.9, max_tokens: int = 20):
    # Format the prompt
    prompt = f"Question: {question}\n\nOptions:\nA. {option_a}\nB. {option_b}\nC. {option_c}\nD. {option_d}\n\nAnswer:"
    
    # Tokenize and generate
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            # pad_token_id=tokenizer.eos_token_id
        )
    
    # Get prediction
    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
    model_answer, model_reasoning = extract_answer(prediction)
    
    # Format output with evaluation if available
    output = prediction
    if correct_option is not None:
        correct_letter = chr(65 + correct_option)  # Convert 0-3 to A-D
        is_correct = model_answer == correct_letter
        output += f"\n\n---\nEvaluation:\n"
        output += f"Correct Answer: {correct_letter}\n"
        output += f"Model's Answer: {model_answer}\n"
        output += f"Result: {'✅ Correct' if is_correct else '❌ Incorrect'}\n"
        if explanation:
            output += f"\nExpert Explanation:\n{explanation}"
    
    return output

# Create Gradio interface with Blocks for more control
with gr.Blocks(title="Medical MCQ Predictor") as demo:
    gr.Markdown("# Medical MCQ Predictor")
    gr.Markdown("Get a random medical question or enter your own question and options.")
    
    with gr.Row():
        with gr.Column():
            # Input fields
            question = gr.Textbox(label="Question", lines=3, interactive=True)
            option_a = gr.Textbox(label="Option A", interactive=True)
            option_b = gr.Textbox(label="Option B", interactive=True)
            option_c = gr.Textbox(label="Option C", interactive=True)
            option_d = gr.Textbox(label="Option D", interactive=True)
            
            # Generation parameters
            with gr.Accordion("Generation Parameters", open=False):
                temperature = gr.Slider(
                    minimum=0.1,
                    maximum=1.0,
                    value=0.6,
                    step=0.1,
                    label="Temperature",
                    info="Higher values make output more random, lower values more focused"
                )
                top_p = gr.Slider(
                    minimum=0.1,
                    maximum=1.0,
                    value=0.9,
                    step=0.1,
                    label="Top P",
                    info="Higher values allow more diverse tokens, lower values more focused"
                )
                max_tokens = gr.Slider(
                    minimum=10,
                    maximum=512,
                    value=20,
                    step=32,
                    label="Max Tokens",
                    info="Maximum length of the generated response"
                )
            
            # Hidden fields for correct answer and explanation
            correct_option = gr.Number(visible=False)
            expert_explanation = gr.Textbox(visible=False)
            
            # Buttons
            with gr.Row():
                predict_btn = gr.Button("Predict", variant="primary")
                random_btn = gr.Button("Get Random Question", variant="secondary")
            
            # Output
            output = gr.Textbox(label="Model's Answer", lines=10)
    
    # Set up button actions
    predict_btn.click(
        fn=predict,
        inputs=[
            question, option_a, option_b, option_c, option_d,
            correct_option, expert_explanation,
            temperature, top_p, max_tokens
        ],
        outputs=output
    )
    
    random_btn.click(
        fn=get_random_question,
        inputs=[],
        outputs=[question, option_a, option_b, option_c, option_d, correct_option, expert_explanation]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()