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| import os | |
| import json | |
| import numpy as np | |
| from datasets import load_dataset | |
| from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline | |
| import torch | |
| from sklearn.metrics import f1_score | |
| import re | |
| from collections import Counter | |
| import string | |
| from huggingface_hub import login | |
| import gradio as gr | |
| import pandas as pd | |
| from datetime import datetime | |
| def normalize_answer(s): | |
| """Normalize answer for evaluation""" | |
| def remove_articles(text): | |
| return re.sub(r'\b(a|an|the)\b', ' ', text) | |
| def white_space_fix(text): | |
| return ' '.join(text.split()) | |
| def remove_punc(text): | |
| exclude = set(string.punctuation) | |
| return ''.join(ch for ch in text if ch not in exclude) | |
| def lower(text): | |
| return text.lower() | |
| return white_space_fix(remove_articles(remove_punc(lower(s)))) | |
| def f1_score_qa(prediction, ground_truth): | |
| """Calculate F1 score for QA""" | |
| prediction_tokens = normalize_answer(prediction).split() | |
| ground_truth_tokens = normalize_answer(ground_truth).split() | |
| if len(prediction_tokens) == 0 or len(ground_truth_tokens) == 0: | |
| return int(prediction_tokens == ground_truth_tokens) | |
| common = Counter(prediction_tokens) & Counter(ground_truth_tokens) | |
| num_same = sum(common.values()) | |
| if num_same == 0: | |
| return 0 | |
| precision = 1.0 * num_same / len(prediction_tokens) | |
| recall = 1.0 * num_same / len(ground_truth_tokens) | |
| f1 = (2 * precision * recall) / (precision + recall) | |
| return f1 | |
| def exact_match_score(prediction, ground_truth): | |
| """Calculate exact match score""" | |
| return normalize_answer(prediction) == normalize_answer(ground_truth) | |
| def evaluate_model(): | |
| # Authenticate with Hugging Face using the token | |
| hf_token = os.getenv("EVAL_TOKEN") | |
| if hf_token: | |
| try: | |
| login(token=hf_token) | |
| print("✓ Authenticated with Hugging Face") | |
| except Exception as e: | |
| print(f"⚠ Warning: Could not authenticate with HF token: {e}") | |
| else: | |
| print("⚠ Warning: EVAL_TOKEN not found in environment variables") | |
| print("Loading model and tokenizer...") | |
| model_name = "AvocadoMuffin/roberta-cuad-qa-v2" | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token) | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_name, token=hf_token) | |
| qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer) | |
| print("✓ Model loaded successfully") | |
| return qa_pipeline, hf_token | |
| except Exception as e: | |
| print(f"✗ Error loading model: {e}") | |
| return None, None | |
| def run_evaluation(num_samples, progress=gr.Progress()): | |
| """Run evaluation and return results for Gradio interface""" | |
| # Load model | |
| qa_pipeline, hf_token = evaluate_model() | |
| if qa_pipeline is None: | |
| return "❌ Failed to load model", "", "" | |
| progress(0.1, desc="Loading CUAD dataset...") | |
| # Load dataset | |
| try: | |
| dataset = load_dataset("cuad", trust_remote_code=True, token=hf_token) | |
| test_data = dataset["test"] | |
| except Exception as e: | |
| try: | |
| dataset = load_dataset("theatticusproject/cuad", trust_remote_code=True, token=hf_token) | |
| test_data = dataset["test"] | |
| except Exception as e2: | |
| return f"❌ Error loading dataset: {e2}", "", "" | |
| # Limit samples | |
| num_samples = min(num_samples, len(test_data)) | |
| test_subset = test_data.select(range(num_samples)) | |
| progress(0.2, desc=f"Starting evaluation on {num_samples} samples...") | |
| # Initialize metrics | |
| exact_matches = [] | |
| f1_scores = [] | |
| predictions = [] | |
| # Run evaluation | |
| for i, example in enumerate(test_subset): | |
| progress((0.2 + 0.7 * i / num_samples), desc=f"Processing sample {i+1}/{num_samples}") | |
| try: | |
| context = example["context"] | |
| question = example["question"] | |
| answers = example["answers"] | |
| # Get model prediction | |
| result = qa_pipeline(question=question, context=context) | |
| predicted_answer = result["answer"] | |
| # Get ground truth answers | |
| if answers["text"] and len(answers["text"]) > 0: | |
| ground_truth = answers["text"][0] if isinstance(answers["text"], list) else answers["text"] | |
| else: | |
| ground_truth = "" | |
| # Calculate metrics | |
| em = exact_match_score(predicted_answer, ground_truth) | |
| f1 = f1_score_qa(predicted_answer, ground_truth) | |
| exact_matches.append(em) | |
| f1_scores.append(f1) | |
| predictions.append({ | |
| "Sample_ID": i+1, | |
| "Question": question[:100] + "..." if len(question) > 100 else question, | |
| "Predicted_Answer": predicted_answer, | |
| "Ground_Truth": ground_truth, | |
| "Exact_Match": em, | |
| "F1_Score": round(f1, 3), | |
| "Confidence": round(result["score"], 3) | |
| }) | |
| except Exception as e: | |
| continue | |
| progress(0.9, desc="Calculating final metrics...") | |
| # Calculate final metrics | |
| avg_exact_match = np.mean(exact_matches) * 100 | |
| avg_f1_score = np.mean(f1_scores) * 100 | |
| # Create results summary | |
| results_summary = f""" | |
| # 📊 CUAD Model Evaluation Results | |
| ## 🎯 Overall Performance | |
| - **Model**: AvocadoMuffin/roberta-cuad-qa-v2 | |
| - **Dataset**: CUAD (Contract Understanding Atticus Dataset) | |
| - **Samples Evaluated**: {len(exact_matches)} | |
| - **Evaluation Date**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | |
| ## 📈 Metrics | |
| - **Exact Match Score**: {avg_exact_match:.2f}% | |
| - **F1 Score**: {avg_f1_score:.2f}% | |
| ## 🔍 Performance Analysis | |
| - **High Confidence Predictions**: {len([p for p in predictions if p['Confidence'] > 0.8])} ({len([p for p in predictions if p['Confidence'] > 0.8])/len(predictions)*100:.1f}%) | |
| - **Perfect Matches**: {len([p for p in predictions if p['Exact_Match'] == 1])} ({len([p for p in predictions if p['Exact_Match'] == 1])/len(predictions)*100:.1f}%) | |
| - **High F1 Scores (>0.8)**: {len([p for p in predictions if p['F1_Score'] > 0.8])} ({len([p for p in predictions if p['F1_Score'] > 0.8])/len(predictions)*100:.1f}%) | |
| """ | |
| # Create detailed results DataFrame | |
| df = pd.DataFrame(predictions) | |
| # Save results | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| results_file = f"cuad_evaluation_results_{timestamp}.json" | |
| detailed_results = { | |
| "model_name": "AvocadoMuffin/roberta-cuad-qa-v2", | |
| "dataset": "cuad", | |
| "num_samples": len(exact_matches), | |
| "exact_match_score": avg_exact_match, | |
| "f1_score": avg_f1_score, | |
| "evaluation_date": datetime.now().isoformat(), | |
| "predictions": predictions | |
| } | |
| with open(results_file, "w") as f: | |
| json.dump(detailed_results, f, indent=2) | |
| progress(1.0, desc="✅ Evaluation completed!") | |
| return results_summary, df, results_file | |
| def create_gradio_interface(): | |
| """Create Gradio interface for CUAD evaluation""" | |
| with gr.Blocks(title="CUAD Model Evaluator", theme=gr.themes.Soft()) as demo: | |
| gr.HTML(""" | |
| <div style="text-align: center; padding: 20px;"> | |
| <h1>🏛️ CUAD Model Evaluation Dashboard</h1> | |
| <p>Evaluate your CUAD (Contract Understanding Atticus Dataset) Question Answering model</p> | |
| <p><strong>Model:</strong> AvocadoMuffin/roberta-cuad-qa-v2</p> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.HTML("<h3>⚙️ Evaluation Settings</h3>") | |
| num_samples = gr.Slider( | |
| minimum=10, | |
| maximum=500, | |
| value=100, | |
| step=10, | |
| label="Number of samples to evaluate", | |
| info="Choose between 10-500 samples (more samples = more accurate but slower)" | |
| ) | |
| evaluate_btn = gr.Button( | |
| "🚀 Start Evaluation", | |
| variant="primary", | |
| size="lg" | |
| ) | |
| gr.HTML(""" | |
| <div style="margin-top: 20px; padding: 15px; background-color: #f0f0f0; border-radius: 8px;"> | |
| <h4>📋 What this evaluates:</h4> | |
| <ul> | |
| <li><strong>Exact Match</strong>: Percentage of perfect predictions</li> | |
| <li><strong>F1 Score</strong>: Token-level overlap between prediction and ground truth</li> | |
| <li><strong>Confidence</strong>: Model's confidence in its predictions</li> | |
| </ul> | |
| </div> | |
| """) | |
| with gr.Column(scale=2): | |
| gr.HTML("<h3>📊 Results</h3>") | |
| results_summary = gr.Markdown( | |
| value="Click '🚀 Start Evaluation' to begin...", | |
| label="Evaluation Summary" | |
| ) | |
| gr.HTML("<hr>") | |
| with gr.Row(): | |
| gr.HTML("<h3>📋 Detailed Results</h3>") | |
| with gr.Row(): | |
| detailed_results = gr.Dataframe( | |
| headers=["Sample_ID", "Question", "Predicted_Answer", "Ground_Truth", "Exact_Match", "F1_Score", "Confidence"], | |
| label="Sample-by-Sample Results", | |
| interactive=False, | |
| wrap=True | |
| ) | |
| with gr.Row(): | |
| download_file = gr.File( | |
| label="📥 Download Complete Results (JSON)", | |
| visible=False | |
| ) | |
| # Event handlers | |
| evaluate_btn.click( | |
| fn=run_evaluation, | |
| inputs=[num_samples], | |
| outputs=[results_summary, detailed_results, download_file], | |
| show_progress=True | |
| ).then( | |
| lambda: gr.update(visible=True), | |
| outputs=[download_file] | |
| ) | |
| # Footer | |
| gr.HTML(""" | |
| <div style="text-align: center; margin-top: 30px; padding: 20px; color: #666;"> | |
| <p>🤖 Powered by Hugging Face Transformers & Gradio</p> | |
| <p>📚 CUAD Dataset by The Atticus Project</p> | |
| </div> | |
| """) | |
| return demo | |
| if __name__ == "__main__": | |
| print("CUAD Model Evaluation with Gradio Interface") | |
| print("=" * 50) | |
| # Check if CUDA is available | |
| if torch.cuda.is_available(): | |
| print(f"✓ CUDA available: {torch.cuda.get_device_name(0)}") | |
| else: | |
| print("! Running on CPU") | |
| # Create and launch Gradio interface | |
| demo = create_gradio_interface() | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=True, | |
| debug=True | |
| ) |