Delete show_embedding_sample.py

show_embedding_sample.py

@@ -1,48 +0,0 @@
-import json
-import numpy as np
-from PIL import Image
-from transformers import CLIPProcessor, CLIPModel
-
-def show_embedding_sample():
-    """
-    Show what an embedding looks like - just numbers, no visual info
-    """
-    print("🔍 Creating sample embedding to show what people would see...")
-    
-    # Load CLIP model
-    model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-    
-    # Process master.jpg
-    image = Image.open("master.jpg").convert('RGB')
-    inputs = processor(images=image, return_tensors="pt")
-    
-    with torch.no_grad():
-        features = model.get_image_features(**inputs)
-        embedding = features.squeeze().numpy()
-    
-    # Show what the embedding data looks like
-    print("\n📊 This is what people would see in the embedding file:")
-    print("=" * 60)
-    print(f"Embedding shape: {embedding.shape}")
-    print(f"First 10 values: {embedding[:10]}")
-    print(f"Data type: {type(embedding[0])}")
-    print("\nSample JSON structure:")
-    
-    sample_data = {
-        "filename": "sample_image.jpg",
-        "embedding": embedding[:20].tolist(),  # Just first 20 values for demo
-        "description": "Sample image embedding for demonstration"
-    }
-    
-    print(json.dumps(sample_data, indent=2))
-    print("\n💡 Key points:")
-    print("- Just 512 numbers (mathematical representation)")
-    print("- No visual information about the original image")
-    print("- Cannot reconstruct the original image from these numbers")
-    print("- Useful for similarity search and ML tasks")
-    print("- Completely privacy-safe")
-
-if __name__ == "__main__":
-    import torch
-    show_embedding_sample()