toy_dataset_eval.py

import torch
import evaluate
import re
import base64
import io
import matplotlib.pyplot as plt
from transformers import AutoModelForCausalLM, AutoTokenizer
import spaces  # Assuming this is a custom or predefined library for GPU handling

# ---------------------------------------------------------------------------
# 1. Simple Test Dataset to Run GPU Calls On
# ---------------------------------------------------------------------------
test_data = [
    {"question": "What is 2+2?", "answer": "4"},
    {"question": "What is 3*3?", "answer": "9"},
    {"question": "What is 10/2?", "answer": "5"},
]

# ---------------------------------------------------------------------------
# 2. Load metric
# ---------------------------------------------------------------------------
accuracy_metric = evaluate.load("accuracy")

# ---------------------------------------------------------------------------
# 4. Inference helper functions
# ---------------------------------------------------------------------------
@spaces.GPU
def generate_answer(question, model, tokenizer):
    """
    Generates an answer using Mistral's instruction format.
    """    
    # Mistral instruction format
    prompt = f"""<s>[INST] {question}. Provide only the numerical answer. [/INST]"""
    
    inputs = tokenizer(prompt, return_tensors="pt").to('cuda')
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=50,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id
        )
    text_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
    # Remove the original question from the output
    return text_output.replace(question, "").strip()

def parse_answer(model_output):
    """
    Extract numeric answer from model's text output.
    """
    # Look for numbers (including decimals)
    match = re.search(r"(-?\d*\.?\d+)", model_output)
    if match:
        return match.group(1)
    return model_output.strip()


@spaces.GPU(duration=120)  # Allow up to 2 minutes for full evaluation
def evaluate_toy_dataset(model, tokenizer):
    predictions = []
    references = []
    raw_outputs = []  # Store full model outputs for display
    
    for sample in test_data:
        question = sample["question"]
        reference_answer = sample["answer"]
        
        # Model inference
        model_output = generate_answer(question, model, tokenizer)
        predicted_answer = parse_answer(model_output)
        
        predictions.append(predicted_answer)
        references.append(reference_answer)
        raw_outputs.append({
            "question": question,
            "model_output": model_output,
            "parsed_answer": predicted_answer,
            "reference": reference_answer
        })
    
    # Normalize answers
    def normalize_answer(ans):
        return str(ans).lower().strip()
    
    norm_preds = [normalize_answer(p) for p in predictions]
    norm_refs = [normalize_answer(r) for r in references]
    
    # Compute accuracy
    results = accuracy_metric.compute(predictions=norm_preds, references=norm_refs)
    accuracy = results["accuracy"]
    
    # Create visualization
    fig, ax = plt.subplots(figsize=(8, 6))
    correct_count = sum(p == r for p, r in zip(norm_preds, norm_refs))
    incorrect_count = len(test_data) - correct_count
    
    bars = ax.bar(["Correct", "Incorrect"], 
                 [correct_count, incorrect_count],
                 color=["#2ecc71", "#e74c3c"])
    
    # Add value labels on bars
    for bar in bars:
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width()/2., height,
                f'{int(height)}',
                ha='center', va='bottom')
    
    ax.set_title("Evaluation Results")
    ax.set_ylabel("Count")
    ax.set_ylim([0, len(test_data) + 0.5])
    
    # Convert plot to base64
    buf = io.BytesIO()
    plt.savefig(buf, format="png", bbox_inches='tight', dpi=300)
    buf.seek(0)
    plt.close(fig)
    data = base64.b64encode(buf.read()).decode("utf-8")
    
    # Create detailed results HTML
    details_html = """
    <div style="margin-top: 20px;">
        <h3>Detailed Results:</h3>
        <table style="width:100%; border-collapse: collapse;">
            <tr style="background-color: #f5f5f5;">
                <th style="padding: 8px; border: 1px solid #ddd;">Question</th>
                <th style="padding: 8px; border: 1px solid #ddd;">Model Output</th>
                <th style="padding: 8px; border: 1px solid #ddd;">Parsed Answer</th>
                <th style="padding: 8px; border: 1px solid #ddd;">Reference</th>
            </tr>
    """
    
    for result in raw_outputs:
        details_html += f"""
            <tr>
                <td style="padding: 8px; border: 1px solid #ddd;">{result['question']}</td>
                <td style="padding: 8px; border: 1px solid #ddd;">{result['model_output']}</td>
                <td style="padding: 8px; border: 1px solid #ddd;">{result['parsed_answer']}</td>
                <td style="padding: 8px; border: 1px solid #ddd;">{result['reference']}</td>
            </tr>
        """
    
    details_html += "</table></div>"
    
    full_html = f"""
    <div>
        <img src="data:image/png;base64,{data}" style="width:100%; max-width:600px;">
        {details_html}
    </div>
    """
    
    return f"Accuracy: {accuracy:.2f}", full_html