Update app.py

app.py

@@ -8,32 +8,22 @@ import uuid
 # Global initialization
 tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
 model = GPT2LMHeadModel.from_pretrained('gpt2')
+from datasets import load_dataset
 
 class SecondLifeNavigator:
-    def __init__(self, dataset_path, corrade_api_key='nucleus'):
-        self.dataset = self.load_dataset(dataset_path)
-        self.corrade_api_key = corrade_api_key
-
-    def load_dataset(self, dataset_path):
-        data = []
-        with open(dataset_path, newline='') as csvfile:
-            reader = csv.DictReader(csvfile)
-            for row in reader:
-                # Assuming 'x', 'y', 'z' are columns in your CSV and you want to store them as a vector dict
-                vector = {'x': float(row['x']), 'y': float(row['y']), 'z': float(row['z'])}
-                entry = {
-                    "id": row['id'],
-                    "vector": vector,
-                    "action": row['action']
-                }
-                data.append(entry)
-        return data
+    def __init__(self):
+        self.dataset = self.load_dataset()
+        
+    def load_dataset(self):
+        # Loads the dataset directly from Hugging Face
+        dataset = load_dataset("visionlab/block-towers-10k-3s-trajectory-scale1")
+        return dataset
 
     def send_command_to_corrade(self, corrade_endpoint, command, parameters):
         command_data = {
             "command": command,
             "group": "e269893f-a570-0087-930e-6ba2a0b77f9c",
-            "password": self.corrade_api_key,
+            "password": "nucleus",
         }
         command_data.update(parameters)