Rename app.py to app.py.bak4

app.py → app.py.bak4

@@ -13,24 +13,36 @@ print("Initializing GPT-2 Tokenizer and Model...")
 tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
 model = GPT2LMHeadModel.from_pretrained('gpt2')
 
-# Global dataset loading
+from scipy.spatial import KDTree
+
+
+# Load your dataset
 print("Loading dataset...")
 dataset = load_dataset("visionlab/block-towers-10k-3s-trajectory-scale1", split='train')
 print("Dataset loaded successfully.")
 
 from scipy.spatial import KDTree
 
-# Assuming final_positions is a list of dicts with 'x', 'y', 'z'
+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']]
 positions_array = np.array([[p['x'], p['y'], p['z']] for p in final_positions])
 
 # Build a KD-tree
 tree = KDTree(positions_array)
 
-# Now, given a current_xyz as a list [x, y, z], you can find the nearest position quickly
+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:
         temp_placeholder = "<TEMP_ESCAPED_SINGLE_QUOTE>"
@@ -47,6 +59,7 @@ class SecondLifeNavigator:
         self.dataset = dataset
         print("SecondLifeNavigator initialized with the dataset.")
 
+    
     def determine_action_sequence(self, current_xyz):
         print(f"Determining action sequence for position: {current_xyz}")
         min_distance = float('inf')