added basic webhook adaptations

README.md

@@ -9,3 +9,8 @@ duplicated_from: huggingface-projects/auto-retrain
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+```bash
+ngrok http 6900
+uvicorn src.main:app --reload --port 6900
+```

poetry.lock

@@ -0,0 +1,724 @@
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+]

pyproject.toml

@@ -0,0 +1,19 @@
+[tool.poetry]
+name = "auto-retrain-argilla"
+version = "0.1.0"
+description = ""
+authors = ["david <[email protected]>"]
+readme = "README.md"
+packages = [{include = "auto_retrain_argilla"}]
+
+[tool.poetry.dependencies]
+python = "^3.10"
+fastapi = "^0.92.0"
+huggingface-hub = "^0.12.1"
+requests = "^2.28.2"
+uvicorn = {version = ">=0.17.0,<0.18.0", extras = ["standard"]}
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

src/main.py

@@ -1,12 +1,12 @@
 import os
-import requests
 from typing import Optional
 
-from fastapi import FastAPI, Header, HTTPException, BackgroundTasks
+import requests
+from fastapi import BackgroundTasks, FastAPI, Header, HTTPException
 from fastapi.responses import FileResponse
 from huggingface_hub.hf_api import HfApi
 
-from .models import config, WebhookPayload
+from .models import WebhookPayload, config
 
 WEBHOOK_SECRET = os.getenv("WEBHOOK_SECRET")
 HF_ACCESS_TOKEN = os.getenv("HF_ACCESS_TOKEN")
@@ -16,120 +16,121 @@ AUTOTRAIN_UI_URL = "https://ui.autotrain.huggingface.co"
 
 app = FastAPI()
 
+
 @app.get("/")
 async def home():
-	return FileResponse("home.html")
+    return FileResponse("home.html")
+
 
 @app.post("/webhook")
 async def post_webhook(
-		payload: WebhookPayload,
-		task_queue: BackgroundTasks,
-		x_webhook_secret:  Optional[str] = Header(default=None),
-	):
-	if x_webhook_secret is None:
-		raise HTTPException(401)
-	if x_webhook_secret != WEBHOOK_SECRET:
-		raise HTTPException(403)
-	if not (
-		payload.event.action == "update"
-		and payload.event.scope.startswith("repo.content")
-		and payload.repo.name == config.input_dataset
-		and payload.repo.type == "dataset"
-	):
-		# no-op
-		return {"processed": False}
-
-	task_queue.add_task(
-		schedule_retrain,
-		payload
-	)
-
-	return {"processed": True}
+    payload: WebhookPayload,
+    task_queue: BackgroundTasks,
+    x_webhook_secret: Optional[str] = Header(default=None),
+):
+    if x_webhook_secret is None:
+        raise HTTPException(401)
+    if x_webhook_secret != WEBHOOK_SECRET:
+        raise HTTPException(403)
+    print(payload)
+    if not (
+        payload.event.action == "update"
+        and payload.event.scope.startswith("repo.content")
+        and payload.repo.name == config.input_dataset
+        and payload.repo.type == "dataset"
+    ):
+        # no-op
+        return {"processed": False}
+
+    task_queue.add_task(schedule_retrain, payload)
+
+    return {"processed": True}
 
 
 def schedule_retrain(payload: WebhookPayload):
-	# Create the autotrain project
-	try:
-		project = AutoTrain.create_project(payload)
-		AutoTrain.add_data(project_id=project["id"])
-		AutoTrain.start_processing(project_id=project["id"])
-	except requests.HTTPError as err:
-		print("ERROR while requesting AutoTrain API:")
-		print(f"  code: {err.response.status_code}")
-		print(f"  {err.response.json()}")
-		raise
-	# Notify in the community tab
-	notify_success(project["id"])
-
-	return {"processed": True}
+    # Create the autotrain project
+    try:
+        project = AutoTrain.create_project(payload)
+        AutoTrain.add_data(project_id=project["id"])
+        AutoTrain.start_processing(project_id=project["id"])
+    except requests.HTTPError as err:
+        print("ERROR while requesting AutoTrain API:")
+        print(f"  code: {err.response.status_code}")
+        print(f"  {err.response.json()}")
+        raise
+    # Notify in the community tab
+    notify_success(project["id"])
+
+    return {"processed": True}
 
 
 class AutoTrain:
-	@staticmethod
-	def create_project(payload: WebhookPayload) -> dict:
-		project_resp = requests.post(
-			f"{AUTOTRAIN_API_URL}/projects/create",
-			json={
-				"username": config.target_namespace,
-				"proj_name": f"{config.autotrain_project_prefix}-{payload.repo.headSha[:7]}",
-				"task": 18, # image-multi-class-classification
-				"config": {
-					"hub-model": config.input_model,
-					"max_models": 1,
-					"language": "unk",
-				}
-			},
-			headers={
-				"Authorization": f"Bearer {HF_ACCESS_TOKEN}"
-			}
-		)
-		project_resp.raise_for_status()
-		return project_resp.json()
-
-	@staticmethod
-	def add_data(project_id:int):
-		requests.post(
-			f"{AUTOTRAIN_API_URL}/projects/{project_id}/data/dataset",
-			json={
-				"dataset_id": config.input_dataset,
-				"dataset_split": "train",
-				"split": 4,
-				"col_mapping": {
-					"image": "image",
-					"label": "target",
-				}
-			},
-			headers={
-				"Authorization": f"Bearer {HF_ACCESS_TOKEN}",
-			}
-		).raise_for_status()
-
-	@staticmethod
-	def start_processing(project_id: int):
-		resp = requests.post(
-			f"{AUTOTRAIN_API_URL}/projects/{project_id}/data/start_processing",
-			headers={
-				"Authorization": f"Bearer {HF_ACCESS_TOKEN}",
-			}
-		)
-		resp.raise_for_status()
-		return resp
+    @staticmethod
+    def create_project(payload: WebhookPayload) -> dict:
+        project_resp = requests.post(
+            f"{AUTOTRAIN_API_URL}/projects/create",
+            json={
+                "username": config.target_namespace,
+                "proj_name": (
+                    f"{config.autotrain_project_prefix}-{payload.repo.headSha[:7]}"
+                ),
+                "task": 18,  # image-multi-class-classification
+                "config": {
+                    "hub-model": config.input_model,
+                    "max_models": 1,
+                    "language": "unk",
+                },
+            },
+            headers={"Authorization": f"Bearer {HF_ACCESS_TOKEN}"},
+        )
+        project_resp.raise_for_status()
+        return project_resp.json()
+
+    @staticmethod
+    def add_data(project_id: int):
+        requests.post(
+            f"{AUTOTRAIN_API_URL}/projects/{project_id}/data/dataset",
+            json={
+                "dataset_id": config.input_dataset,
+                "dataset_split": "train",
+                "split": 4,
+                "col_mapping": {
+                    "image": "image",
+                    "label": "target",
+                },
+            },
+            headers={
+                "Authorization": f"Bearer {HF_ACCESS_TOKEN}",
+            },
+        ).raise_for_status()
+
+    @staticmethod
+    def start_processing(project_id: int):
+        resp = requests.post(
+            f"{AUTOTRAIN_API_URL}/projects/{project_id}/data/start_processing",
+            headers={
+                "Authorization": f"Bearer {HF_ACCESS_TOKEN}",
+            },
+        )
+        resp.raise_for_status()
+        return resp
 
 
 def notify_success(project_id: int):
-	message = NOTIFICATION_TEMPLATE.format(
-		input_model=config.input_model,
-		input_dataset=config.input_dataset,
-		project_id=project_id,
-		ui_url=AUTOTRAIN_UI_URL,
-	)
-	return HfApi(token=HF_ACCESS_TOKEN).create_discussion(
-		repo_id=config.input_dataset,
-		repo_type="dataset",
-		title="✨ Retraining started!",
-		description=message,
-		token=HF_ACCESS_TOKEN,
-	)
+    message = NOTIFICATION_TEMPLATE.format(
+        input_model=config.input_model,
+        input_dataset=config.input_dataset,
+        project_id=project_id,
+        ui_url=AUTOTRAIN_UI_URL,
+    )
+    return HfApi(token=HF_ACCESS_TOKEN).create_discussion(
+        repo_id=config.input_dataset,
+        repo_type="dataset",
+        title="✨ Retraining started!",
+        description=message,
+        token=HF_ACCESS_TOKEN,
+    )
+
 
 NOTIFICATION_TEMPLATE = """\
 🌸 Hello there!