from gevent import pywsgi
import dotenv
dotenv.load_dotenv(override=True)

import sys
import time
import argparse
import uvicorn
from typing import Union
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import openedai
import numpy as np
import onnxruntime as ort
import asyncio
from optimum.bettertransformer import BetterTransformer

app = openedai.OpenAIStub()
moderation = None
device = "cpu" if torch.cuda.is_available() else "cpu"
#device = "cpu"

labels = ['hate', 
          'hate_threatening', 
          'harassment', 
          'harassment_threatening', 
          'self_harm', 
          'self_harm_intent', 
          'self_harm_instructions',
          'sexual',
          'sexual_minors',
          'violence',
          'violence_graphic',
         ]

label2id = {l:i for i, l in enumerate(labels)}
id2label = {i:l for i, l in enumerate(labels)}
model_name = "/root/autodl-tmp/duanyu027/moderation_0628"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(labels),id2label=id2label, label2id=label2id, problem_type = "multi_label_classification")
model = torch.quantization.quantize_dynamic(
	model, {torch.nn.Linear}, dtype=torch.qint8
)
torch.set_num_threads(1)
class ModerationsRequest(BaseModel):
	model: str = "text-moderation-latest" # or "text-moderation-stable"
	input: Union[str, list[str]]

@app.post("/v1/moderations")
async def moderations(request: ModerationsRequest):
    results = {
        "id": f"modr-{int(time.time()*1e9)}",
        "model": "text-moderation-005",
        "results": [],
    }
    if isinstance(request.input, str):
        request.input = [request.input]
    
    thresholds = {
        "sexual": 0.1,
        "hate": 0.25,
        "harassment": 0.5,
        "self_harm": 0.25,
        "sexual_minors": 0.5,
        "hate_threatening": 0.2,
        "violence_graphic": 0.25,
        "self_harm_intent": 0.2,
        "self_harm_instructions": 0.25,
        "harassment_threatening": 0.1,
        "violence": 0.25,
    }

    for text in request.input:
        predictions = await predict(text, model, tokenizer)
        category_scores = {labels[i]: predictions[0][i].item() for i in range(len(labels))}
        detect = {key: score > thresholds[key] for key, score in category_scores.items()}
        detected = any(detect.values())

        results['results'].append({
            'flagged': detected,
            'categories': detect,
            'category_scores': category_scores,
        })

    return results
def sigmoid(x):
   return 1/(1 + np.exp(-x))
    
def parse_args(argv):
	parser = argparse.ArgumentParser(description='Moderation API')
	parser.add_argument('--host', type=str, default='0.0.0.0')
	parser.add_argument('--port', type=int, default=5002)
	parser.add_argument('--test-load', action='store_true')
	return parser.parse_args(argv)
    
async def predict(text, model, tokenizer):
    encoding = tokenizer.encode_plus(
        text,
        return_tensors='pt'
    )
    input_ids = encoding['input_ids'].to(device)
    attention_mask = encoding['attention_mask'].to(device)
    
    # 运行模型预测在独立的线程中
    def _predict():
        with torch.no_grad():
            outputs = model(input_ids, attention_mask=attention_mask)
        return torch.sigmoid(outputs.logits)
    
    loop = asyncio.get_running_loop()
    predictions = await loop.run_in_executor(None, _predict)
    
    # 清理 GPU 内存
    del input_ids
    del attention_mask
    torch.cuda.empty_cache()
    
    return predictions
# Main
if __name__ == "__main__":

	args = parse_args(sys.argv[1:])
	# start API
	print(f'Starting moderations[{device}] API on {args.host}:{args.port}', file=sys.stderr)
	app.register_model('text-moderations-latest', 'text-moderations-stable')
	app.register_model('text-moderations-005', 'text-moderations-ifmain')

	if not args.test_load:
		uvicorn.run(app, host=args.host, port=args.port)