Update README.md

README.md

@@ -11,171 +11,3 @@ app_file: app.py
 pinned: true
 ---
 
-
-import numpy as np
-import time
-from pathlib import Path
-
-# Import your PyLaia implementation
-from htrflow.models.teklia.pylaia import PyLaia
-from htrflow.utils.imgproc import read
-
-NORMAL_IMAGE_PATH = "examples/images/lines/A0068699_00021_region0_line1.jpg"
-
-
-def create_test_images(base_image_path, num_images=100):
-    """Create test images - mix of real and synthetic variations."""
-    images = []
-    
-    # Load the real image
-    real_image = read(base_image_path)
-    
-    # Create test images
-    for i in range(num_images):
-        if i % 2 == 0:
-            # Use the real image
-            images.append(real_image.copy())
-        else:
-            # Create a slightly modified version (add some noise)
-            noisy_image = real_image.copy()
-            noise = np.random.normal(0, 10, real_image.shape).astype(np.uint8)
-            noisy_image = np.clip(noisy_image.astype(np.int16) + noise, 0, 255).astype(np.uint8)
-            images.append(noisy_image)
-    
-    return images
-
-
-def benchmark_pylaia(model_name="Teklia/pylaia-belfort", num_images=100):
-    """Benchmark PyLaia with different chunk sizes."""
-    
-    print(f"\n{'='*80}")
-    print(f"PyLaia Chunking Performance Benchmark")
-    print(f"{'='*80}")
-    print(f"Model: {model_name}")
-    print(f"Number of test images: {num_images}")
-    
-    # Initialize model
-    print("\nInitializing model...")
-    model = PyLaia(model_name)
-    print(f"Device: {model.device}")
-    
-    # Create test images
-    print(f"\nCreating {num_images} test images...")
-    test_images = create_test_images(NORMAL_IMAGE_PATH, num_images)
-    
-    # Test different chunk sizes
-    chunk_sizes = [1, 5, 10, 20, 50, 100]
-    results = {}
-    
-    print(f"\n{'='*80}")
-    print("Running benchmarks...")
-    print(f"{'='*80}")
-    
-    for chunk_size in chunk_sizes:
-        if chunk_size > num_images:
-            continue
-            
-        print(f"\nTesting chunk_size={chunk_size}...")
-        
-        # Warm-up run
-        print("  Warm-up run...")
-        _ = model._predict(test_images[:min(5, num_images)], chunk_size=chunk_size)
-        
-        # Actual timing
-        print("  Timing run...")
-        start_time = time.time()
-        
-        predictions = model._predict(
-            test_images,
-            batch_size=8,
-            temperature=1.0,
-            chunk_size=chunk_size
-        )
-        
-        end_time = time.time()
-        elapsed_time = end_time - start_time
-        
-        results[chunk_size] = {
-            'time': elapsed_time,
-            'images_per_second': num_images / elapsed_time,
-            'ms_per_image': (elapsed_time / num_images) * 1000,
-            'predictions': predictions
-        }
-        
-        print(f"  ✓ Completed in {elapsed_time:.2f}s")
-        print(f"  Speed: {results[chunk_size]['images_per_second']:.2f} images/second")
-        print(f"  Time per image: {results[chunk_size]['ms_per_image']:.2f}ms")
-    
-    # Print summary table
-    print(f"\n{'='*80}")
-    print("PERFORMANCE SUMMARY")
-    print(f"{'='*80}")
-    print(f"{'Chunk Size':>12} | {'Total Time':>10} | {'Images/sec':>12} | {'ms/image':>10} | {'Speedup':>10}")
-    print(f"{'-'*12}-+-{'-'*10}-+-{'-'*12}-+-{'-'*10}-+-{'-'*10}")
-    
-    baseline_time = results[1]['time'] if 1 in results else list(results.values())[0]['time']
-    
-    for chunk_size in sorted(results.keys()):
-        data = results[chunk_size]
-        speedup = baseline_time / data['time']
-        print(f"{chunk_size:>12} | {data['time']:>10.2f}s | {data['images_per_second']:>12.2f} | "
-              f"{data['ms_per_image']:>10.2f} | {speedup:>10.2f}x")
-    
-    # Verify consistency
-    print(f"\n{'='*80}")
-    print("Verifying result consistency...")
-    baseline_texts = [r.texts[0] for r in results[1]['predictions']] if 1 in results else None
-    
-    all_consistent = True
-    for chunk_size, data in results.items():
-        if baseline_texts and chunk_size != 1:
-            chunk_texts = [r.texts[0] for r in data['predictions']]
-            if chunk_texts != baseline_texts:
-                print(f"  ✗ Results mismatch for chunk_size={chunk_size}")
-                all_consistent = False
-    
-    if all_consistent:
-        print("  ✓ All chunk sizes produced identical results")
-    
-    # Find optimal chunk size
-    optimal_chunk = min(results.keys(), key=lambda k: results[k]['time'])
-    optimal_speedup = baseline_time / results[optimal_chunk]['time']
-    
-    print(f"\n{'='*80}")
-    print(f"🚀 OPTIMAL CONFIGURATION")
-    print(f"{'='*80}")
-    print(f"Chunk size: {optimal_chunk}")
-    print(f"Processing time: {results[optimal_chunk]['time']:.2f}s")
-    print(f"Speed: {results[optimal_chunk]['images_per_second']:.2f} images/second")
-    print(f"Speedup: {optimal_speedup:.2f}x")
-    
-    return results
-
-
-def quick_test():
-    """Quick test with 20 images for faster results."""
-    print("\n" + "="*80)
-    print("QUICK TEST (20 images)")
-    print("="*80)
-    
-    benchmark_pylaia(num_images=20)
-
-
-def full_test():
-    """Full test with 100 images."""
-    print("\n" + "="*80)
-    print("FULL TEST (100 images)")
-    print("="*80)
-    
-    benchmark_pylaia(num_images=100)
-
-
-if __name__ == "__main__":
-    # Run quick test first
-    quick_test()
-    
-    # Uncomment to run full test
-    # full_test()
-    
-    # Or run with custom parameters
-    # benchmark_pylaia(model_name="Teklia/pylaia-belfort", num_images=50)