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, T5TokenizerFast
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Keep import for dependency check
 from datetime import datetime, timedelta
 import plotly.graph_objects as go
 import plotly.express as px
@@ -29,20 +29,15 @@ from config import IDX_STOCKS, TECHNICAL_INDICATORS, PREDICTION_CONFIG
 # Load Chronos-Bolt model
 @spaces.GPU(duration=120)
 def load_model():
-    """Load the Amazon Chronos-Bolt model for time series forecasting"""
-    # FIX: Use AutoModelForSeq2SeqLM and trust_remote_code=True (correct for T5-based model)
+    """Load the Amazon Chronos-Bolt model for time series forecasting. Tokenizer loading is skipped to bypass fatal error."""
     model = AutoModelForSeq2SeqLM.from_pretrained(
         "amazon/chronos-bolt-base",
         torch_dtype=torch.bfloat16,
         device_map="auto",
         trust_remote_code=True
     )
-    # FINAL FIX: Explicitly use T5TokenizerFast (Fast Tokenizer) with trust_remote_code=True.
-    # This bypasses the T5Tokenizer (Slow Tokenizer) fallback logic that caused the TypeError: not a string.
-    tokenizer = T5TokenizerFast.from_pretrained(
-        "amazon/chronos-bolt-base", 
-        trust_remote_code=True
-    )
+    # CRITICAL FIX: Skip tokenizer loading (it is not used in predict_prices anyway)
+    tokenizer = None 
     return model, tokenizer
 
 # Initialize model
@@ -78,6 +73,7 @@ def analyze_stock(symbol, prediction_days=30):
     signals = generate_trading_signals(data, indicators)
     
     # Make predictions using Chronos-Bolt
+    # Passing the (now None) tokenizer argument to maintain compatibility with utils.py signature
     predictions = predict_prices(data, model, tokenizer, prediction_days)
     
     # Create charts