import os
import requests
from typing import Optional

from fastapi import FastAPI, Header, HTTPException, BackgroundTasks
from fastapi.responses import FileResponse
from huggingface_hub.hf_api import HfApi

from .models import config, WebhookPayload

WEBHOOK_SECRET = os.getenv("WEBHOOK_SECRET")
HF_ACCESS_TOKEN = os.getenv("HF_ACCESS_TOKEN")
AUTOTRAIN_API_URL = "https://api.autotrain.huggingface.co"
AUTOTRAIN_UI_URL = "https://ui.autotrain.huggingface.co"


app = FastAPI()

@app.get("/")
async def home():
	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}


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}


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


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,
	)

NOTIFICATION_TEMPLATE = """\
🌸 Hello there!

Following an update of [{input_dataset}](https://huggingface.co/datasets/{input_dataset}), an automatic re-training of [{input_model}](https://huggingface.co/{input_model}) has been scheduled on AutoTrain!

Please review and approve the project [here]({ui_url}/{project_id}/trainings) to start the training job.

(This is an automated message)
"""