added logging to file

3d-generation-pipeline/.gitignore

@@ -3,3 +3,4 @@
 __pycache__
 images/
 models/
+logs/

3d-generation-pipeline/start_pipeline.py

@@ -1,6 +1,8 @@
 import os
 import argparse
 import asyncio
+import logging
+import time
 
 from pathlib import Path
 from datetime import datetime
@@ -18,6 +20,16 @@ PIPELINE_FOLDER = os.environ["PIPELINE_FOLDER"]
 def get_timestamp():
     return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
 
+def setup_logger(base_folder, timestamp):
+    log_dir = base_folder / Path("logs")
+    log_dir.mkdir(parents=True, exist_ok=True)
+    logging.basicConfig(
+        filename=log_dir / f"{timestamp}.log",
+        level=logging.INFO,
+        #format='%(asctime)s - %(message)s'
+        force=True
+    )
+
 
 async def main():
     parser = argparse.ArgumentParser(description="Text to 3D model pipeline")
@@ -34,16 +46,26 @@ async def main():
     else:
         image_generation_prompt = input_prompt
     
-    timestamp = get_timestamp()
     pipeline_folder = Path(PIPELINE_FOLDER)
+    timestamp = get_timestamp()
+    setup_logger(pipeline_folder, timestamp)
+    time_checkpoint = time.time()
+
     image_path = pipeline_folder / "images" / f"{timestamp}.jpg"
     # TODO: use Invoke AI or Cloudflare, depending on env var
     #text_to_image_cloudflare(image_generation_prompt, image_path)
     await text_to_image_invoke_ai(image_generation_prompt, image_path)
     
+    image_generation_time = time.time() - time_checkpoint
+    time_checkpoint = time.time()
+    logging.info(f"Image generation time: {round(image_generation_time, 1)} s")
     print(f"Generated image file: {image_path}")
+
     model_path = pipeline_folder / "models" / timestamp
     model_file = image_to_3d_api(image_path, model_path)
+
+    model_generation_time = time.time() - time_checkpoint
+    logging.info(f"Model generation time: {round(model_generation_time, 1)} s")
     print(f"Generated 3D model file: {model_file}")