webhooks test

app.py

@@ -1,97 +1,18 @@
 import gradio as gr
-import random
-import time
-import tqdm
-from datasets import load_dataset
-import shutil
-from uuid import uuid4
+from huggingface_hub import WebhooksServer, WebhookPayload
 
-with gr.Blocks() as demo:
-    with gr.Row():
-        text = gr.Textbox()
-        textb = gr.Textbox()
-    with gr.Row():
-        load_set_btn = gr.Button("Load Set")
-        load_nested_set_btn = gr.Button("Load Nested Set")
-        load_random_btn = gr.Button("Load Random")
-        clean_imgs_btn = gr.Button("Clean Images")
-        wait_btn = gr.Button("Wait")
-        do_all_btn = gr.Button("Do All")
-        track_tqdm_btn = gr.Button("Bind TQDM")
-        bind_internal_tqdm_btn = gr.Button("Bind Internal TQDM")
+from model import model
 
-    text2 = gr.Textbox()
+with gr.Blocks() as ui:
+    progress = gr.Progress(track_tqdm=True)
 
-    # track list
-    def load_set(text, text2, progress=gr.Progress()):
-        imgs = [None] * 24
-        for img in progress.tqdm(imgs, desc="Loading from list"):
-            time.sleep(0.1)
-        return "done"
-    load_set_btn.click(load_set, [text, textb], text2)
+# 2. Create WebhooksServer with custom UI and secret
+app = WebhooksServer(ui=ui, webhook_secret="test")
 
-    # track nested list
-    def load_nested_set(text, text2, progress=gr.Progress()):
-        imgs = [[None] * 8] * 3
-        for img_set in progress.tqdm(imgs, desc="Nested list"):
-            time.sleep(2)
-            for img in progress.tqdm(img_set, desc="inner list"):
-                time.sleep(0.1)
-        return "done"
-    load_nested_set_btn.click(load_nested_set, [text, textb], text2)
+@app.add_webhook
+async def train(payload: WebhookPayload):
+    print("Received payload:", payload.dict())
+    return payload.dict()
 
-    # track iterable of unknown length
-    def load_random(data, progress=gr.Progress()):
-        def yielder():
-            for i in range(0, random.randint(15, 20)):
-                time.sleep(0.1)
-                yield None
-        for img in progress.tqdm(yielder()):
-            pass
-        return "done"
-    load_random_btn.click(load_random, {text, textb}, text2)
-
-    # manual progress
-    def clean_imgs(text, progress=gr.Progress()):
-        progress(0.2, desc="Collecting Images")
-        time.sleep(1)
-        progress(0.5, desc="Cleaning Images")
-        time.sleep(1.5)
-        progress(0.8, desc="Sending Images")
-        time.sleep(1.5)
-        return "done"
-    clean_imgs_btn.click(clean_imgs, text, text2)
-
-    # no progress
-    def wait(text):
-        time.sleep(4)
-        return "done"
-    wait_btn.click(wait, text, text2)
-
-    # multiple progressions
-    def do_all(data, progress=gr.Progress()):
-        load_set(data[text], data[textb], progress)
-        load_random(data, progress)
-        clean_imgs(data[text], progress)
-        progress(None)
-        wait(text)
-        return "done"
-    do_all_btn.click(do_all, {text, textb}, text2)
-
-    def track_tqdm(data, progress=gr.Progress(track_tqdm=True)):
-        for i in tqdm.tqdm(range(5), desc="outer"):
-            for j in tqdm.tqdm(range(4), desc="inner"):
-                time.sleep(1)
-        return "done"
-    track_tqdm_btn.click(track_tqdm, {text, textb}, text2)
-
-    def bind_internal_tqdm(data, progress=gr.Progress(track_tqdm=True)):
-        outdir = "__tmp/" + str(uuid4())
-        load_dataset("beans", split="train", cache_dir=outdir)
-        shutil.rmtree(outdir)
-        return "done"
-    bind_internal_tqdm_btn.click(bind_internal_tqdm, {text, textb}, text2)
-
-
-if __name__ == "__main__":
-    demo.queue(concurrency_count=20).launch()
+# 5. Start server (optional)
+app.run()

local-requirements.txt

@@ -0,0 +1,8 @@
+datasets
+huggingface-hub>=0.12.1
+protobuf<4
+click<8.1
+pydantic~=1.0
+gradio[oauth]==3.50.2
+uvicorn>=0.14.0
+spaces==0.17.0

model.py

@@ -0,0 +1,16 @@
+from sentence_transformers import SentenceTransformer, models
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+word_embedding_model = models.Transformer("BAAI/bge-base-en-v1.5", max_seq_length=512)
+
+word_embedding_model.tokenizer.add_tokens(['[TURN]'], special_tokens=True)
+word_embedding_model.tokenizer.truncation_side = 'left'
+word_embedding_model.auto_model.resize_token_embeddings(len(word_embedding_model.tokenizer))
+
+pooling_model = models.Pooling(
+    word_embedding_model.get_word_embedding_dimension(), pooling_mode="cls"
+)
+
+model = SentenceTransformer(modules=[word_embedding_model, pooling_model], device=device)