add logging

.gitignore

@@ -0,0 +1,5 @@
+.env
+.vscode/settings.json
+app/__pycache__/
+__pycache__/
+logs/app.log

app.py

@@ -1,23 +1,54 @@
+from app.logger_config import logger as logging
+import os
 import gradio as gr
 import spaces
 import torch
 
-@spaces.GPU
+
+
+logging.info("-----------info------------")
+logging.debug("-----------debug------------")
+
+@spaces.GPU(progress=gr.Progress(track_tqdm=True))
 def greet(name):
-    # Détection du GPU
-    if torch.cuda.is_available():
-        device_name = torch.cuda.get_device_name(0)
+    logging.debug("=== Start of greet() ===")
+
+    # Check GPU availability
+    has_gpu = torch.cuda.is_available()
+    logging.debug(f"GPU available: {has_gpu}")
+
+    if has_gpu:
         device = torch.device("cuda")
+        device_name = torch.cuda.get_device_name(0)
+        memory_allocated = torch.cuda.memory_allocated(0) / (1024 ** 2)
+        memory_reserved = torch.cuda.memory_reserved(0) / (1024 ** 2)
+        memory_total = torch.cuda.get_device_properties(0).total_memory / (1024 ** 2)
+        capability = torch.cuda.get_device_capability(0)
+        current_device = torch.cuda.current_device()
+
+        # Detailed GPU logs
+        logging.debug(f"GPU name          : {device_name}")
+        logging.debug(f"Current device ID : {current_device}")
+        logging.debug(f"CUDA capability   : {capability}")
+        logging.debug(f"Memory allocated  : {memory_allocated:.2f} MB")
+        logging.debug(f"Memory reserved   : {memory_reserved:.2f} MB")
+        logging.debug(f"Total memory      : {memory_total:.2f} MB")
     else:
-        device_name = "CPU (aucun GPU détecté)"
         device = torch.device("cpu")
+        device_name = "CPU (no GPU detected)"
+        logging.debug("No GPU detected, using CPU")
 
-    # Crée un tenseur sur GPU ou CPU
+    # Create tensor
     tensor = torch.tensor([len(name)], dtype=torch.float32, device=device)
+    logging.debug(f"Tensor created: {tensor}")
+
+    # Optional GPU cleanup
+    if has_gpu:
+        torch.cuda.empty_cache()
+        logging.debug("GPU cache cleared")
 
-    # Retourne une info lisible
-    result = f"Tensor: {tensor.cpu().numpy()} | Device: {device_name}"
-    return result
+    logging.debug("=== End of greet() ===")
+    return f"Tensor: {tensor.cpu().numpy()} | Device: {device_name}"
 
 demo = gr.Interface(
     fn=greet,

app/logger_config.py

@@ -0,0 +1,59 @@
+import os
+from dotenv import load_dotenv, find_dotenv
+load_dotenv(find_dotenv())
+# logger_config.py
+import logging
+from logging.handlers import RotatingFileHandler
+import os
+# from nemo.utils.nemo_logging import Logger
+
+# nemo_logger = Logger()
+# nemo_logger.remove_stream_handlers()
+
+DEBUG = os.getenv("DEBUG", "false").lower() == "true"
+print(DEBUG)
+# Create "logs" directory if it doesn't exist
+os.makedirs("logs", exist_ok=True)
+
+logger = logging.getLogger("app_logger")  # unique name
+logger.setLevel(logging.DEBUG if DEBUG else logging.INFO)
+
+# Avoid duplicate handlers
+if not logger.handlers:
+    formatter = logging.Formatter("%(asctime)s | %(levelname)s | %(module)s | %(message)s")
+
+
+    # Console handler
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(logging.DEBUG if DEBUG else logging.INFO)
+    console_handler.setFormatter(formatter)
+    logger.addHandler(console_handler)
+
+    # File handler
+    file_handler = RotatingFileHandler("logs/app.log", maxBytes=2 * 1024 * 1024, backupCount=5)
+    file_handler.setLevel(logging.DEBUG if DEBUG else logging.INFO)
+    file_handler.setFormatter(formatter)
+    logger.addHandler(file_handler)
+
+    # Startup message
+    logger.debug("Logger initialized (DEBUG)") if DEBUG else logger.info("Logger initialized (Production Mode)")
+# --- Logger Silencing Function ---
+
+def silence_logger(logger_name: str | None, level=logging.ERROR):
+    """Sets the log level and removes StreamHandlers for a given logger."""
+    target_logger = logging.getLogger(logger_name)
+    target_logger.setLevel(level)
+    
+    for handler in target_logger.handlers[:]:
+        if isinstance(handler, logging.StreamHandler):
+            target_logger.removeHandler(handler)
+    
+    # Ensure messages don't propagate to the parent (root) logger
+    target_logger.propagate = False
+
+# silence_logger(None, logging.CRITICAL) 
+# logger.propagate = True 
+
+# silence_logger("nemo_logger", logging.CRITICAL)
+# silence_logger("pytorch_lightning", logging.ERROR)
+

requirements.txt

@@ -1,3 +1,4 @@
 gradio
 spaces
-torch
+torch
+python-dotenv