Add video url in output

app.py

@@ -17,15 +17,21 @@ num_clusters = 10  # Choose the number of clusters
 kmeans = KMeans(n_clusters=num_clusters, random_state=42)
 kmeans.fit(encoded_images)
 
+def create_url_from_title(title: str, timestamp: int):
+    video_urls = load_dataset("eybro/video_urls")
+    df = video_urls['train'].to_pandas()
+    filtered = df[df['title'] == title]
+    base_url = df["url"][0]
+    return base_url + f"?t={timestamp}s"
+
+
 def find_nearest_neighbors(encoded_images, input_image, top_n=5):
     """
     Find the closest neighbors to the input image in the encoded image space.
-
     Args:
     encoded_images (np.ndarray): Array of encoded images (shape: (n_samples, n_features)).
     input_image (np.ndarray): The encoded input image (shape: (1, n_features)).
     top_n (int): The number of nearest neighbors to return.
-
     Returns:
     List of tuples: (index, distance) of the top_n nearest neighbors.
     """
@@ -79,7 +85,7 @@ def inference(image):
       print(im["label"], im["timestamp"])
 
     result_image = get_image(top4[0])
-    result = f"{result_image['label']} {result_image['timestamp']}"
+    result = f"{result_image['label']} {result_image['timestamp']} \n{create_url_from_title(result_image['label'], result_image['timestamp'])}"
 
     n=2
     plt.figure(figsize=(8, 8))