Update app.py

app.py

@@ -1,5 +1,3 @@
-import warnings
-from transformers.utils import logging as transformers_logging
 import torch
 import yfinance as yf
 import matplotlib.pyplot as plt
@@ -9,23 +7,22 @@ import gradio as gr
 import logging
 from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
 
-# Suppress specific FutureWarnings from transformers library
-warnings.filterwarnings("ignore", category=FutureWarning, message=".*vocab_size.*")
-
-# Optionally, configure the transformers logging to avoid clutter
-transformers_logging.set_verbosity_error()
-
 # Configure logging to write to a file
 logging.basicConfig(filename='debug.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
 
-# Check if CUDA (GPU) is available and select the device accordingly
+# Check GPU availability and initialize device
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-logging.info(f"CUDA Available: {torch.cuda.is_available()}")
-logging.info(f"Device: {device}")
+print("CUDA Available:", torch.cuda.is_available())
+print("Using device:", device)
 
 # Load the ChartGemma model and processor
-model = PaliGemmaForConditionalGeneration.from_pretrained("ahmed-masry/chartgemma", torch_dtype=torch.float16).to(device)
-processor = AutoProcessor.from_pretrained("ahmed-masry/chartgemma")
+try:
+    model = PaliGemmaForConditionalGeneration.from_pretrained("ahmed-masry/chartgemma", torch_dtype=torch.float16).to(device)
+    processor = AutoProcessor.from_pretrained("ahmed-masry/chartgemma")
+    print("Model and processor loaded successfully.")
+except Exception as e:
+    print("Error loading model or processor:", e)
+    logging.error(f"Error loading model or processor: {e}")
 
 # Function to fetch stock data with different intervals
 def fetch_stock_data(ticker='TSLA', start='2023-01-01', end='2024-01-01', interval='1d'):
@@ -45,18 +42,11 @@ def create_stock_chart(data, ticker, filename='chart.png', timeframe='1d'):
         logging.debug(f"Creating chart for {ticker} with timeframe {timeframe} and saving to {filename}")
         title = f"{ticker.upper()} Price Data (Timeframe: {timeframe})"
         
-        # Set the title font size using rcParams
         plt.rcParams["axes.titlesize"] = 10
-        
-        # Define the style for the chart
         my_style = mpf.make_mpf_style(base_mpf_style='charles')
-        
         fig, axlist = mpf.plot(data, type='candle', style=my_style, volume=True, returnfig=True)
-        
-        # Set the title for the figure with padding
-        fig.suptitle(title, y=0.98)  # Adjust the y parameter to move the title down
-        
-        fig.savefig(filename, dpi=300)  # Increased DPI
+        fig.suptitle(title, y=0.98)
+        fig.savefig(filename, dpi=300)
         plt.close(fig)
 
         # Resize image to 3 times its original size