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refactor: implement structured logging across multiple modules

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- Introduced logging functionality in cli.py, run_agent.py, scheduler.py, and various tool modules to replace print statements with structured logging.
- Enhanced error handling and informational messages to improve debugging and monitoring capabilities.
- Ensured consistent logging practices across the codebase, facilitating better traceability and maintenance.
This commit is contained in:
teknium1 2026-02-21 03:11:11 -08:00
parent b6247b71b5
commit a885d2f240
14 changed files with 303 additions and 303 deletions
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37
tools/image_generation_tool.py
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@ -29,6 +29,7 @@ Usage:
"""
import json
import logging
import os
import asyncio
import uuid
@ -37,6 +38,8 @@ from pathlib import Path
from typing import Dict, Any, Optional, Union
import fal_client
logger = logging.getLogger(__name__)
# Configuration for image generation
DEFAULT_MODEL = "fal-ai/flux-2-pro"
DEFAULT_ASPECT_RATIO = "landscape"
@ -89,7 +92,7 @@ DEBUG_DATA = {
# Create logs directory if debug mode is enabled
if DEBUG_MODE:
DEBUG_LOG_PATH.mkdir(exist_ok=True)
print(f"🐛 Image generation debug mode enabled - Session ID: {DEBUG_SESSION_ID}")
logger.debug("Image generation debug mode enabled - Session ID: %s", DEBUG_SESSION_ID)
def _log_debug_call(tool_name: str, call_data: Dict[str, Any]) -> None:
@ -130,10 +133,10 @@ def _save_debug_log() -> None:
with open(debug_filepath, 'w', encoding='utf-8') as f:
json.dump(DEBUG_DATA, f, indent=2, ensure_ascii=False)
print(f"🐛 Image generation debug log saved: {debug_filepath}")
logger.debug("Image generation debug log saved: %s", debug_filepath)
except Exception as e:
print(f"❌ Error saving image generation debug log: {str(e)}")
logger.error("Error saving image generation debug log: %s", e)
def _validate_parameters(
@ -221,7 +224,7 @@ async def _upscale_image(image_url: str, original_prompt: str) -> Dict[str, Any]
Dict[str, Any]: Upscaled image data or None if upscaling fails
"""
try:
print(f"🔍 Upscaling image with Clarity Upscaler...")
logger.info("Upscaling image with Clarity Upscaler...")
# Prepare arguments for upscaler
upscaler_arguments = {
@ -247,7 +250,7 @@ async def _upscale_image(image_url: str, original_prompt: str) -> Dict[str, Any]
if result and "image" in result:
upscaled_image = result["image"]
print(f"✅ Image upscaled successfully to {upscaled_image.get('width', 'unknown')}x{upscaled_image.get('height', 'unknown')}")
logger.info("Image upscaled successfully to %sx%s", upscaled_image.get('width', 'unknown'), upscaled_image.get('height', 'unknown'))
return {
"url": upscaled_image["url"],
"width": upscaled_image.get("width", 0),
@ -256,11 +259,11 @@ async def _upscale_image(image_url: str, original_prompt: str) -> Dict[str, Any]
"upscale_factor": UPSCALER_FACTOR
}
else:
print("❌ Upscaler returned invalid response")
logger.error("Upscaler returned invalid response")
return None
except Exception as e:
print(f"❌ Error upscaling image: {str(e)}")
logger.error("Error upscaling image: %s", e)
return None
@ -300,7 +303,7 @@ async def image_generate_tool(
# Validate and map aspect_ratio to actual image_size
aspect_ratio_lower = aspect_ratio.lower().strip() if aspect_ratio else DEFAULT_ASPECT_RATIO
if aspect_ratio_lower not in ASPECT_RATIO_MAP:
print(f"⚠️ Invalid aspect_ratio '{aspect_ratio}', defaulting to '{DEFAULT_ASPECT_RATIO}'")
logger.warning("Invalid aspect_ratio '%s', defaulting to '%s'", aspect_ratio, DEFAULT_ASPECT_RATIO)
aspect_ratio_lower = DEFAULT_ASPECT_RATIO
image_size = ASPECT_RATIO_MAP[aspect_ratio_lower]
@ -324,7 +327,7 @@ async def image_generate_tool(
start_time = datetime.datetime.now()
try:
print(f"🎨 Generating {num_images} image(s) with FLUX 2 Pro: {prompt[:80]}{'...' if len(prompt) > 80 else ''}")
logger.info("Generating %s image(s) with FLUX 2 Pro: %s", num_images, prompt[:80])
# Validate prompt
if not prompt or not isinstance(prompt, str) or len(prompt.strip()) == 0:
@ -356,11 +359,11 @@ async def image_generate_tool(
if seed is not None and isinstance(seed, int):
arguments["seed"] = seed
print(f"🚀 Submitting generation request to FAL.ai FLUX 2 Pro...")
print(f" Model: {DEFAULT_MODEL}")
print(f" Aspect Ratio: {aspect_ratio_lower} → {image_size}")
print(f" Steps: {validated_params['num_inference_steps']}")
print(f" Guidance: {validated_params['guidance_scale']}")
logger.info("Submitting generation request to FAL.ai FLUX 2 Pro...")
logger.info(" Model: %s", DEFAULT_MODEL)
logger.info(" Aspect Ratio: %s -> %s", aspect_ratio_lower, image_size)
logger.info(" Steps: %s", validated_params['num_inference_steps'])
logger.info(" Guidance: %s", validated_params['guidance_scale'])
# Submit request to FAL.ai
handler = await fal_client.submit_async(
@ -399,7 +402,7 @@ async def image_generate_tool(
formatted_images.append(upscaled_image)
else:
# Fall back to original image if upscaling fails
print(f"⚠️ Using original image as fallback")
logger.warning("Using original image as fallback")
original_image["upscaled"] = False
formatted_images.append(original_image)
@ -407,7 +410,7 @@ async def image_generate_tool(
raise ValueError("No valid image URLs returned from API")
upscaled_count = sum(1 for img in formatted_images if img.get("upscaled", False))
print(f"✅ Generated {len(formatted_images)} image(s) in {generation_time:.1f}s ({upscaled_count} upscaled)")
logger.info("Generated %s image(s) in %.1fs (%s upscaled)", len(formatted_images), generation_time, upscaled_count)
# Prepare successful response - minimal format
response_data = {
@ -428,7 +431,7 @@ async def image_generate_tool(
except Exception as e:
generation_time = (datetime.datetime.now() - start_time).total_seconds()
error_msg = f"Error generating image: {str(e)}"
print(f"❌ {error_msg}")
logger.error("%s", error_msg)
# Prepare error response - minimal format
response_data = {