Update app.py

app.py

@@ -10,20 +10,17 @@ import matplotlib.pyplot as plt
 from huggingface_hub import hf_hub_download
 from PIL import Image
 
-# Download and load model and encoded images
 model_path = hf_hub_download(repo_id="eybro/autoencoder", filename="autoencoder_model.keras", repo_type='model')
 data_path = hf_hub_download(repo_id="eybro/encoded_images", filename="X_encoded_compressed.npy", repo_type='dataset')
 
 autoencoder = load_model(model_path)
 encoded_images = np.load(data_path)
 
-# Load and split dataset
 dataset = load_dataset("eybro/images")
 split_dataset = dataset['train'].train_test_split(test_size=0.2, seed=42)  # 80% train, 20% test
 dataset['train'] = split_dataset['train']
 dataset['test'] = split_dataset['test']
 
-# Example images
 example_images = {
     "Example 1": "example_2.png",
     "Example 2": "examples/example_1.png"
@@ -32,9 +29,7 @@ example_images = {
 def create_url_from_title(title: str, timestamp: int):
     video_urls = load_dataset("eybro/video_urls")
     df = video_urls['train'].to_pandas()
-    print(df.to_string())
     filtered = df[df['title'] == title]
-    print(filtered)
     base_url = filtered.iloc[0, :]["url"]
     return base_url + f"&t={timestamp}s"
 
@@ -89,17 +84,9 @@ def inference(user_image=None, selected_example=None):
     else:
         return "Please upload an image or select an example image."
 
-    # input_image = process_image(image)
-
     nearest_neighbors = find_nearest_neighbors(encoded_images, input_image, top_n=5)
     
-    # Print the results
-    print("Nearest neighbors (index, distance):")
-    for neighbor in nearest_neighbors:
-        print(neighbor)
-    
     top4 = [int(i[0]) for i in nearest_neighbors[:4]]
-    print(f"top 4: {top4}")
     
     for i in top4:
       im = get_image(i)
@@ -109,24 +96,6 @@ def inference(user_image=None, selected_example=None):
     url = create_url_from_title(result_image['label'], result_image['timestamp'])
     result = f"{result_image['label']} {result_image['timestamp']} \n{url}"
     
-    n=2
-    plt.figure(figsize=(8, 8))
-    for i, (image1, image2) in enumerate(zip(top4[:2], top4[2:])):
-        ax = plt.subplot(2, n, i + 1)
-        image1 = get_image(image1)["image"]
-        image2 = get_image(image2)["image"]
-    
-        plt.imshow(image1)
-        plt.gray()
-        ax.get_xaxis().set_visible(False)
-        ax.get_yaxis().set_visible(False)
-    
-        ax = plt.subplot(2, n, i + 1 + n)
-        plt.imshow(image2)
-        plt.gray()
-        ax.get_xaxis().set_visible(False)
-        ax.get_yaxis().set_visible(False)
-    
     return result
 
 def load_example(example_name):