Update app.py

app.py

@@ -12,8 +12,6 @@ from tqdm import tqdm # For download progress bar
 import spaces
 import functools
 from constants import updated_upscaler_dict as UPSCALER_DICT_GUI
-
-# --- New Official Implementation Imports ---
 from stablepy import load_upscaler_model
 
 # --- New Global Constants ---
@@ -24,10 +22,10 @@ DIRECTORY_UPSCALERS = "upscalers"
 # Set your Hugging Face Write Token as an environment variable
 # export HF_TOKEN_ORG="hf_YourTokenHere"
 HF_TOKEN_ORG = os.getenv("HF_TOKEN_ORG")
-DATASET_REPO_ID = "TestOrganizationPleaseIgnore/test"
+DATASET_REPO_ID = "TestOrganizationPleaseIgnore/upscale_board_data"
 DATASET_FILENAME = "upscaler_preferences.csv"
 LOCAL_CSV_PATH = "upscaler_preferences_local.csv" # Local backup for safety
-PUSH_THRESHOLD = 10 # Push after this many new votes
+PUSH_THRESHOLD = 2 # Push after this many new votes
 
 # --- Helper Functions for New Implementation ---
 def download_model(directory, url):
@@ -66,21 +64,6 @@ def download_model(directory, url):
         print(f"Error downloading model: {e}")
         return None
 
-def extract_exif_data(image):
-    """Placeholder function to extract EXIF data. Can be expanded later."""
-    # In a real implementation, you would use a library like piexif
-    # and return the exif bytes. For now, it does nothing.
-    return None
-
-# def on_gpu_configurable(duration=60):
-#     def decorator(func):
-#         @functools.wraps(func)
-#         @spaces.GPU(duration=duration)
-#         def wrapper(*args, **kwargs):
-#             return func(*args, **kwargs)
-#         return wrapper
-#     return decorator
-
 
 class UpscalerApp:
     def __init__(self, repo_id, filename, local_path, push_threshold):
@@ -291,7 +274,6 @@ class UpscalerApp:
         reveal_text = f"Thank you! Your preference for **{choice}** has been recorded.\n\n- **Image A was:** {model_a}\n- **Image B was:** {model_b}"
         return reveal_text, gr.Button(interactive=False), gr.Button(interactive=False)
 
-    # @on_gpu_configurable(duration=59)
     def playground_upscale(self, image, upscaler_name, upscaler_size, tile, tile_overlap, half):
         if image is None or upscaler_name is None: return None
         return self.process_upscale(image, upscaler_name, upscaler_size, tile, tile_overlap, half)
@@ -312,8 +294,10 @@ class UpscalerApp:
             with gr.Tab("Blind Test Comparison"):
                 gr.Markdown("Upload an image, compare the results, and select your favorite. Your vote is recorded to rank the models.")
                 gr.Markdown(
-                    "> **Disclaimer:** This application **does not store your uploaded images**."
-                    " It only anonymously records which upscaler you prefer so we can rank them."
+                    "> **Disclaimer:** This application **does not store your uploaded images**. "
+                    "It only anonymously records which upscaler you prefer to rank them. "
+                    "The collected statistics are publicly available at "
+                    "[upscaler_preferences.csv](https://huggingface.co/datasets/TestOrganizationPleaseIgnore/upscale_board_data/blob/main/upscaler_preferences.csv)."
                 )
                 model_a_state = gr.State("")
                 model_b_state = gr.State("")