app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import os
from huggingface_hub import login
from toy_dataset_eval import evaluate_toy_dataset
from mmlu_eval_original import evaluate_mmlu_batched
import spaces
import pandas as pd

# Read token and login
hf_token = os.getenv("HF_TOKEN_READ_WRITE")
if hf_token:
    login(hf_token)
else:
    print("⚠️ No HF_TOKEN_READ_WRITE found in environment")

# ---------------------------------------------------------------------------
# 1. Model and tokenizer setup and Loading
# ---------------------------------------------------------------------------
model_name = "mistralai/Mistral-7B-v0.1"
tokenizer = None
model = None
model_loaded = False

@spaces.GPU
def load_model():
    """Loads the Mistral model and tokenizer and updates the load status."""
    global tokenizer, model, model_loaded
    try:
        if tokenizer is None:
            tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
        if model is None:
            model = AutoModelForCausalLM.from_pretrained(
                model_name,
                token=hf_token,
                torch_dtype=torch.float16
            )
            model.to('cuda')
        model_loaded = True
        return "✅ Model Loaded!"
    except Exception as e:
        model_loaded = False
        return f"❌ Model Load Failed: {str(e)}"
# ---------------------------------------------------------------------------
# 2. Toy Evaluation
# ---------------------------------------------------------------------------
@spaces.GPU (duration=120)
def run_toy_evaluation():
    """Runs the toy dataset evaluation."""
    if not model_loaded:
        load_model()

    if not model_loaded:
        return "⚠️ Model not loaded. Please load the model first."
    
    results = evaluate_toy_dataset(model, tokenizer)
    return results  # Ensure load confirmation is shown before results
    
# ---------------------------------------------------------------------------
# 3. MMLU Evaluation call
# ---------------------------------------------------------------------------
@spaces.GPU(duration=120)  # Allow up to 2 minutes for full evaluation
def run_mmlu_evaluation(all_subjects, num_subjects, num_shots, all_questions, num_questions):
    """
    Runs the MMLU evaluation with the specified parameters.
    
    Args:
        all_subjects (bool): Whether to evaluate all subjects
        num_subjects (int): Number of subjects to evaluate (1-57)
        num_shots (int): Number of few-shot examples (0-5)
        all_questions (bool): Whether to evaluate all questions per subject
        num_questions (int): Number of examples per subject (1-20 or -1 for all)
    """

    if not model_loaded:
        load_model()

    if not model_loaded:
        return "⚠️ Model not loaded. Please load the model first."

    # Convert num_subjects to -1 if all_subjects is True
    if all_subjects:
        num_subjects = -1
        
    # Convert num_questions to -1 if all_questions is True
    if all_questions:
        num_questions = -1

    # Run evaluation
    results = evaluate_mmlu(
        model, 
        tokenizer,
        num_subjects=num_subjects,
        num_questions=num_questions,
        num_shots=num_shots, 
        auto_batch_size=True
    )

    # Format results
    overall_acc = results["overall_accuracy"]
    min_subject, min_acc = results["min_accuracy_subject"]
    max_subject, max_acc = results["max_accuracy_subject"]
    
    # Create DataFrame from results table
    results_df = pd.DataFrame(results["full_accuracy_table"])
    
    # Format the report
    report = (
        f"### Overall Results\n"
        f"* Overall Accuracy: {overall_acc:.3f}\n"
        f"* Best Performance: {max_subject} ({max_acc:.3f})\n"
        f"* Worst Performance: {min_subject} ({min_acc:.3f})\n\n"
        f"### Detailed Results Table\n"
        f"{results_df.to_markdown()}\n"
    )

    return report
    
# ---------------------------------------------------------------------------
# 4. Gradio Interface
# ---------------------------------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# Mistral-7B on MMLU - Evaluation Demo")
    gr.Markdown("""
    This demo evaluates Mistral-7B on the MMLU Dataset.
    """)

    # Load Model Section
    with gr.Row():
        load_button = gr.Button("Load Model", variant="primary")
        load_status = gr.Textbox(label="Model Status", interactive=False)

    # Toy Dataset Evaluation Section
    gr.Markdown("### Toy Dataset Evaluation")
    with gr.Row():
        eval_toy_button = gr.Button("Run Toy Evaluation", variant="primary")
        toy_output = gr.Textbox(label="Results")
        toy_plot = gr.HTML(label="Visualization and Details")
    
    # MMLU Evaluation Section
    gr.Markdown("### MMLU Evaluation")
    
    with gr.Row():
        all_subjects_checkbox = gr.Checkbox(
            label="Evaluate All Subjects",
            value=False,  # Default is unchecked
            info="When checked, evaluates all 57 MMLU subjects"
        )
        num_subjects_slider = gr.Slider(
            minimum=1,
            maximum=57,
            value=10,  # Default is 10 subjects
            step=1,
            label="Number of Subjects",
            info="Number of subjects to evaluate (1-57). They will be loaded in alphabetical order.",
            interactive=True
        )
    
    with gr.Row():
        num_shots_slider = gr.Slider(
            minimum=0,
            maximum=5,
            value=5,  # Default is 5 few-shot examples
            step=1,
            label="Number of Few-shot Examples",
            info="Number of examples to use for few-shot learning (0-5). They will be loaded in alphabetical order."
        )
    
    with gr.Row():
        all_questions_checkbox = gr.Checkbox(
            label="Evaluate All Questions",
            value=False,  # Default is unchecked
            info="When checked, evaluates all available questions for each subject"
        )
        num_questions_slider = gr.Slider(
            minimum=1,
            maximum=20,
            value=10,  # Default is 10 questions
            step=1,
            label="Questions per Subject",
            info="Choose a subset of questions (1-20), or click the checkbox for All Questions",
            interactive=True
        )
    
    with gr.Row():
        eval_mmlu_button = gr.Button("Run MMLU Evaluation", variant="primary")
        results_output = gr.Markdown(label="Evaluation Results")
    
    # Connect components
    load_button.click(fn=load_model, inputs=None, outputs=load_status)
    
    # Connect toy evaluation
    eval_toy_button.click(
        fn=run_toy_evaluation,
        inputs=None,
        outputs=[toy_output, toy_plot]
    )
    
    # Update num_subjects_slider interactivity based on all_subjects checkbox
    def update_subjects_slider(checked):
        if checked:
            return gr.update(value=57, interactive=False)
        else:
            return gr.update(interactive=True)
    
    all_subjects_checkbox.change(
        fn=update_subjects_slider,
        inputs=[all_subjects_checkbox],
        outputs=[num_subjects_slider]
    )
    
    # Update num_questions_slider interactivity based on all_questions checkbox
    def update_questions_slider(checked):
        if checked:
            return gr.update(interactive=False)
        else:
            return gr.update(interactive=True)
    
    all_questions_checkbox.change(
        fn=update_questions_slider,
        inputs=[all_questions_checkbox],
        outputs=[num_questions_slider]
    )
    
    # Connect MMLU evaluation button
    eval_mmlu_button.click(
        fn=run_mmlu_evaluation,
        inputs=[
            all_subjects_checkbox,
            num_subjects_slider,
            num_shots_slider,
            all_questions_checkbox,
            num_questions_slider
        ],
        outputs=results_output
    )

demo.launch()