update app

app.py

@@ -30,18 +30,19 @@ from config import IDX_STOCKS, TECHNICAL_INDICATORS, PREDICTION_CONFIG
 @spaces.GPU(duration=120)
 def load_model():
     """Load the Amazon Chronos-Bolt model for time series forecasting"""
-    # FIX 1: Use AutoModelForSeq2SeqLM and trust_remote_code=True for T5-based model
+    # Use AutoModelForSeq2SeqLM and trust_remote_code=True for T5-based model
     model = AutoModelForSeq2SeqLM.from_pretrained(
         "amazon/chronos-bolt-base",
         torch_dtype=torch.bfloat16,
         device_map="auto",
         trust_remote_code=True
     )
-    # FIX 2: Use AutoTokenizer and rely SOLELY on trust_remote_code=True
-    # This forces the loading of the custom ChronosTokenizer class, bypassing the conflicting T5 conversion logic.
+    # FIX: Use AutoTokenizer with a combination of flags to bypass the problematic tiktoken conversion logic
     tokenizer = AutoTokenizer.from_pretrained(
         "amazon/chronos-bolt-base", 
-        trust_remote_code=True
+        trust_remote_code=True,
+        use_fast=False,
+        force_download=True
     )
     return model, tokenizer