import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
import os
from flask import Flask, request, jsonify
from io import BytesIO

# 设备配置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"当前使用的设备: {device}")

class ImageClassifier:
    def __init__(self, model_path: str):
        self.processor = AutoImageProcessor.from_pretrained(model_path)
        self.model = AutoModelForImageClassification.from_pretrained(model_path)
        self.model = self.model.to(device)
        
        if torch.cuda.device_count() > 1:
            print(f"使用 {torch.cuda.device_count()} 块GPU")
            self.model = torch.nn.DataParallel(self.model)
            
        self.id2label = self.model.module.config.id2label if hasattr(self.model, 'module') else self.model.config.id2label

    def predict_single_image(self, image) -> dict:
        """预测单张图片，返回置信度最高的结果"""
        try:
            # 处理图片
            inputs = self.processor(images=image, return_tensors="pt").to(device)
            
            with torch.no_grad():
                outputs = self.model(**inputs)
                
            logits = outputs.logits
            probs = torch.nn.functional.softmax(logits, dim=1)
            # 获取置信度最高的预测结果
            max_prob, max_idx = probs.max(dim=1)
            class_idx = max_idx.item()
            
            return {
                "status": "success",
                "top_prediction": {
                    "class_id": class_idx,
                    "class_name": self.id2label[class_idx],
                    "confidence": max_prob.item()
                }
            }
        except Exception as e:
            return {
                "status": "error",
                "message": str(e)
            }

# 初始化服务
app = Flask(__name__)
MODEL_PATH = os.environ.get("MODEL_PATH", "/model")  # 模型路径（环境变量或默认路径）
classifier = ImageClassifier(MODEL_PATH)

@app.route('/v1/private/s782b4996', methods=['POST'])
def predict_single():
    """接收单张图片并返回最高置信度预测结果"""
    # 检查是否有图片上传
    if 'image' not in request.files:
        return jsonify({"status": "error", "message": "请求中未包含图片"}), 400
    
    image_file = request.files['image']
    try:
        # 读取图片
        image = Image.open(BytesIO(image_file.read())).convert("RGB")
        # 预测
        result = classifier.predict_single_image(image)
        return jsonify(result)
    except Exception as e:
        return jsonify({"status": "error", "message": str(e)}), 500

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({"status": "healthy", "device": str(device)}), 200

if __name__ == "__main__":
    app.run(host='0.0.0.0', port=80, debug=False)