revert accelerate, add webhook

app.py

@@ -2,10 +2,7 @@ import collections
 import heapq
 import json
 import os
-
-# https://huggingface.co/docs/accelerate/main/en/basic_tutorials/troubleshooting#logging
-# https://pypi.org/project/accelerate/
-from accelerate.logging import get_logger
+import logging
 
 import gradio as gr
 import numpy as np
@@ -15,10 +12,11 @@ from open_clip import create_model, get_tokenizer
 from torchvision import transforms
 
 from templates import openai_imagenet_template
+from huggingface_hub import webhook_endpoint, WebhookPayload
 
-# log_format = "[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s"
-# logging.basicConfig(level=logging.INFO, format=log_format)
-logger = get_logger(__name__, log_level="INFO")
+log_format = "[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s"
+logging.basicConfig(level=logging.INFO, format=log_format)
+logger = logging.getLogger()
 
 hf_token = os.getenv("HF_TOKEN")
 
@@ -155,8 +153,6 @@ def open_domain_classification(img, rank: int) -> dict[str, float]:
 
     return {name: output[name] for name in topk_names}
 
-
-
 @torch.no_grad()
 def api_classification(img, rank: int): #  -> dict[str, float]:
     """
@@ -185,7 +181,7 @@ def api_classification(img, rank: int): #  -> dict[str, float]:
 
     logger.info(">>>>")
     logger.info(probs[0])
-    return probs[0]
+    return {"message": probs[0]}
     # topk_names = heapq.nlargest(k, output, key=output.get)
     # return {name: output[name] for name in topk_names}
 
@@ -193,6 +189,13 @@ def api_classification(img, rank: int): #  -> dict[str, float]:
 def change_output(choice):
     return gr.Label(num_top_classes=k, label=ranks[choice], show_label=True, value=None)
 
+@webhook_endpoint
+# https://huggingface.co/docs/huggingface_hub/guides/webhooks_server
+async def trigger_test(payload: WebhookPayload):
+    logger.info(payload)
+    if payload.repo.type == "dataset" and payload.event.action == "update":
+        logger.info("oo")
+    return {"message": "hello"}
 
 if __name__ == "__main__":
     logger.info("Starting.")
@@ -332,3 +335,5 @@ if __name__ == "__main__":
 
     app.queue(max_size=20)
     app.launch()
+
+# app.py

requirements.txt

@@ -1,5 +1,4 @@
 open_clip_torch
 torchvision
 torch
-gradio
-accelerate
+gradio