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
@@ -30,15 +30,16 @@ 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"""
-    # Using AutoModelForSeq2SeqLM for T5-based model, with trust_remote_code=True
+    # FIX 1: 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
     )
-    # Adding use_fast=False to bypass the problematic tokenizer conversion
-    tokenizer = AutoTokenizer.from_pretrained(
+    # FIX 2: Explicitly use T5Tokenizer (slow version for T5 architecture)
+    # and set trust_remote_code=True and use_fast=False to bypass problematic AutoTokenizer conversion
+    tokenizer = T5Tokenizer.from_pretrained(
         "amazon/chronos-bolt-base", 
         trust_remote_code=True,
         use_fast=False