update app

app.py

@@ -3,7 +3,7 @@ import yfinance as yf
 import pandas as pd
 import numpy as np
 import torch
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer
 from datetime import datetime, timedelta
 import plotly.graph_objects as go
 import plotly.express as px
@@ -30,19 +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"""
-    # Use AutoModelForSeq2SeqLM and trust_remote_code=True for T5-based model
+    # FIX 1: Use AutoModelForSeq2SeqLM and trust_remote_code=True (correct for T5-based model)
     model = AutoModelForSeq2SeqLM.from_pretrained(
         "amazon/chronos-bolt-base",
         torch_dtype=torch.bfloat16,
         device_map="auto",
         trust_remote_code=True
     )
-    # FIX: Use AutoTokenizer with a combination of flags to bypass the problematic tiktoken conversion logic
-    tokenizer = AutoTokenizer.from_pretrained(
+    # FIX 2: Explicitly use T5Tokenizer (slow version) with trust_remote_code=True and use_fast=False
+    # This bypasses the faulty AutoTokenizer/fast conversion logic.
+    tokenizer = T5Tokenizer.from_pretrained(
         "amazon/chronos-bolt-base", 
         trust_remote_code=True,
-        use_fast=False,
-        force_download=True
+        use_fast=False
     )
     return model, tokenizer