Update app.py

app.py

@@ -23,25 +23,34 @@ print("Dataset loaded successfully.")
 
 from scipy.spatial import KDTree
 
-print("Extracting positions and building KD-tree...")
-
-# Assuming final_positions is structured as shown
-final_positions = [position for item in dataset for position in item['data']['final_positions']]
+import time
+# Assuming positions_array is already defined
+# Extract 'final_positions' from each item in the dataset and compile them into a NumPy array
+final_positions = [position for item in dataset['final_positions'] for position in item]
 positions_array = np.array([[p['x'], p['y'], p['z']] for p in final_positions])
 
-# Build a KD-tree
-tree = KDTree(positions_array)
+def estimate_time_and_build_kdtree(data):
+    start_time = time.time()
+    print("Estimating time based on dataset size...")
+    
+    # Mock estimation logic for demonstration
+    estimated_time_seconds = len(data) * 0.0001  # Adjust based on your context
+    print(f"Estimated time to build KD-tree: {estimated_time_seconds:.2f} seconds")
+    
+    # Actual KD-tree construction
+    tree = KDTree(data)
+    
+    end_time = time.time()
+    actual_time_taken = end_time - start_time
+    print(f"KD-tree built in {actual_time_taken:.2f} seconds.")
+    return tree
+
+# Now, with positions_array defined, build the KD-tree and estimate time
+tree = estimate_time_and_build_kdtree(positions_array)
 
-print("KD-tree constructed successfully.")
 
-# Example current_xyz coordinate
-current_xyz = [1, 2, 3]  # Replace with actual values as needed
 
-print("Querying KD-tree for the nearest position...")
-distance, index = tree.query(current_xyz)
-nearest_position = final_positions[index]
 
-print(f"Nearest position found: {nearest_position} at distance {distance}")
 
 def safe_convert_single_to_double_quotes(s):
     try: