feat: debugger implementation test

app_v3.py

@@ -4,7 +4,10 @@ import subprocess
 import torch
 import spaces
 import os
+import datetime
+import io
 import moondream as md
+from datasets import load_dataset, Dataset, DatasetDict, Image as HFImage
 from diffusers.utils import load_image
 from diffusers.hooks import apply_group_offloading
 from diffusers import FluxControlNetModel, FluxControlNetPipeline, AutoencoderKL
@@ -18,6 +21,8 @@ from threading import Thread
 from typing import Generator
 # from peft import PeftModel, PeftConfig
 import gradio as gr
+from huggingface_hub import CommitScheduler, HfApi
+from debug import log_params, scheduler
 
 huggingface_token = os.getenv("HUGGINFACE_TOKEN")
 MAX_SEED = 1000000
@@ -65,6 +70,7 @@ def generate_image(prompt, scale, steps, control_image, controlnet_conditioning_
             control_guidance_start=0.0,
             control_guidance_end=guidance_end,
         ).images[0]
+
     return image
 
 def combine_caption_focus(caption, focus):
@@ -128,16 +134,19 @@ def process_image(control_image, user_prompt, system_prompt, scale, steps,
             seed=seed,
             guidance_end=guidance_end
         )
+        with scheduler.lock():
+            log_params(final_prompt, scale, steps, controlnet_conditioning_scale, guidance_scale, seed, guidance_end, control_image, image)
         yield f"Completed! Used prompt: {final_prompt}", image, final_prompt
     except Exception as e:
         yield f"Error: {str(e)}", None
-        raise
-        
+    
 def handle_outputs(outputs):
     if isinstance(outputs, dict) and outputs.get("__type__") == "update_caption":
         return outputs["caption"], None
     return outputs
 
+
+
 with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
     gr.Markdown("⚠️ WIP SPACE - UNFINISHED & BUGGY")
     with gr.Row():
@@ -158,23 +167,23 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
             guidance_scale = gr.Slider(1, 30, value=3.5, label="Guidance Scale")
             guidance_end = gr.Slider(0, 1, value=1.0, label="Guidance End")
     with gr.Row():
-        with gr.Accordion("Generation settings", open=False):
+        with gr.Accordion("Auto-Caption settings", open=False, visible=False):
             system_prompt = gr.Textbox(
                 lines=4, 
                 value="Write a straightforward caption for this image. Begin with the main subject and medium. Mention pivotal elements—people, objects, scenery—using confident, definite language. Focus on concrete details like color, shape, texture, and spatial relationships. Show how elements interact. Omit mood and speculative wording. If text is present, quote it exactly. Note any watermarks, signatures, or compression artifacts. Never mention what's absent, resolution, or unobservable details. Vary your sentence structure and keep the description concise, without starting with 'This image is…' or similar phrasing.",
                 label="System Prompt for Captioning",
-                visible=True  # Changed to visible
+                visible=False  # Changed to visible
             )
             temperature_slider = gr.Slider(
                 minimum=0.0, maximum=2.0, value=0.6, step=0.05,
                 label="Temperature",
                 info="Higher values make the output more random, lower values make it more deterministic.",
-                visible=True  # Changed to visible
+                visible=False  # Changed to visible
             )
             top_p_slider = gr.Slider(
                 minimum=0.0, maximum=1.0, value=0.9, step=0.01,
                 label="Top-p",
-                visible=True  # Changed to visible
+                visible=False  # Changed to visible
             )
             max_tokens_slider = gr.Slider(
                 minimum=1, maximum=2048, value=368, step=1,
@@ -184,7 +193,7 @@ with gr.Blocks(title="FLUX Turbo Upscaler", fill_height=True) as demo:
             )
         log_prompt = gr.Checkbox(value=True, label="Log", visible=False)  # Changed to visible
     
-    gr.Markdown("**Tips:** 8 steps is all you need!")
+    gr.Markdown("**Tips:** 8 steps is all you need! Incredibly powerful tool, usage instructions coming soon.")
 
     caption_state = gr.State()
     focus_state = gr.State()

debug.py

@@ -0,0 +1,58 @@
+# logging.py
+
+import os
+import uuid
+import time
+from huggingface_hub import CommitScheduler, HfApi
+from PIL import Image
+
+APP_VERSION = "0_3"
+HF_DATASET_REPO = "LPX55/upscaler_logs"  # Change to your dataset repo
+HF_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")   # Make sure this is set in your environment
+
+LOG_DIR = "logs_" + APP_VERSION
+IMAGE_DIR = os.path.join(LOG_DIR, "upscaler")
+
+LOG_FILE = os.path.join(LOG_DIR, f"{int(time.time())}-logs.csv")
+os.makedirs(IMAGE_DIR, exist_ok=True)
+
+
+scheduler = CommitScheduler(
+    repo_id=HF_DATASET_REPO,
+    repo_type="dataset",
+    folder_path=LOG_DIR,
+    every=5,
+    private=True,
+    token=HF_TOKEN,
+    allow_patterns=["*.csv", "images/*.png", "images/*.webp", "images/*.jpg", "images/*.jpeg"],
+    commit_message="DEBUG_MODE=1",
+    path_in_repo="v" + APP_VERSION
+
+)
+
+def log_params(
+    prompt, scale, steps, controlnet_conditioning_scale, guidance_scale, seed, guidance_end,
+    before_image, after_image, user=None
+):
+    before_id = str(uuid.uuid4()) + "_before.png"
+    after_id = str(uuid.uuid4()) + "_after.png"
+    before_path = os.path.join(IMAGE_DIR, before_id)
+    after_path = os.path.join(IMAGE_DIR, after_id)
+    before_image.save(before_path)
+    after_image.save(after_path)
+
+    is_new = not os.path.exists(LOG_FILE)
+    with open(LOG_FILE, "a", newline='') as f:
+        import csv
+        writer = csv.writer(f)
+        if is_new:
+            writer.writerow([
+                "timestamp", "user", "prompt", "scale", "steps", "controlnet_conditioning_scale",
+                "guidance_scale", "seed", "guidance_end", "before_image", "after_image"
+            ])
+        writer.writerow([
+            time.strftime("%Y-%m-%dT%H:%M:%S"),
+            user or "anonymous",
+            prompt, scale, steps, controlnet_conditioning_scale,
+            guidance_scale, seed, guidance_end, before_path, after_path
+        ])

requirements.txt

@@ -16,4 +16,6 @@ bitsandbytes
 pydantic==2.10.6
 attention_map_diffusers
 moondream
-peft
+peft
+datasets[vision]
+huggingface_hub