add logging

app.py

@@ -1,11 +1,14 @@
 import functools
 import random
+import logging
 
 import gradio as gr
 import torch
 
 from fabric.generator import AttentionBasedGenerator
 
+logger = logging.getLogger(__name__)
+
 
 #model_name = "dreamlike-art/dreamlike-photoreal-2.0"
 model_name = ""
@@ -97,7 +100,9 @@ def generate_fn(
   try:
     if seed < 0:
       seed = random.randint(1,9999999999999999) #16 digits is an arbitrary limit
-    print("seed: ", seed)
+
+    logger.info(f"Generate images: {prompt=}, {neg_prompt=}, {len(liked)=}, {len(disliked)=}, {max_feedback_imgs=}, {batch_size=}, {seed=}")
+    logger.ingo(f"Params: {denoising_steps=}, {guidance_scale=}, {feedback_start=}, {feedback_end=}, {min_weight=}, {max_weight=}, {neg_scale=}")
 
     max_feedback_imgs = max(0, int(max_feedback_imgs))
     total_images = (len(liked) if liked else 0) + (len(disliked) if disliked else 0)
@@ -142,9 +147,11 @@ def generate_fn(
     except RuntimeError as err:
       if 'out of memory' in str(err):
         generator.reload()
+        logger.info(f"Ran out of memory trying to generate {batch_size=} images with {len(liked)=} and {len(disliked)=} feedback images.")
       raise
     return [(img, f"Image {i+1}") for i, img in enumerate(images)], images, seed
   except Exception as err:
+    logger.error(err)
     raise gr.Error(str(err))