from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from detecttracking.stream_pipeline import stream_pipeline
from PIL import Image
from io import BytesIO
import torch
import ast
import requests

# default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-VL-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)

# default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", attn_implementation="flash_attention_2")


def qwen_prompt(img_list, messages):
    # Preparation for inference
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    inputs = processor(
        text=[text],
        images=img_list,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")

    # Inference: Generation of the output
    generated_ids = model.generate(**inputs, max_new_tokens=256)
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, add_special_tokens=False, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )

    return output_text[0]


def get_best_image(track_imgs):
    img_frames = []
    for i in range(len(track_imgs)):
        content = {}
        content['type'] = 'image'
        content['min_pixels'] = 224 * 224
        content['max_pixels'] = 1280 * 28 * 28
        img_frames.append(content)

    messages = [
        {
            "role": "system",
            "content": "你是一个在超市工作的chatbot，你现在需要帮助顾客找到一张质量最好的商品图像。一个好的商品图像需要满足以下条件: \
            1. 文字清晰且连贯。\
            2. 商品图案清晰可识别。\
            3. 商品可提取的描述信息丰富。\
            基于以上条件，从多张图像中筛选出最好的图像，然后以dict输出该图像的索引信息，key为'index'。"
        },
        {
            "role": "system",
            "content": img_frames,
        },
    ]

    output_text = qwen_prompt(track_imgs, messages)
    output_dict = ast.literal_eval(output_text.strip('```python\n'))
    best_img = track_imgs[output_dict['index'] - 1]

    return best_img


def get_product_description(std_img, track_imgs):
    messages = [
        {
            "role": "system",
            "content": "你是一个在超市工作的chatbot，你现在需要提取商品的信息，信息需要按照以下python dict的格式输出: \
            {\
                'Text': 商品中提取出的文字信息,  \
                'Color': 商品的颜色,  \
                'Shape': 商品的形状,  \
                'Material': 商品的材质,  \
                'Category': 商品的类别,  \
                'is_Same': 如果比对的两件商品的['Text', 'Color', 'Shape', 'Material', 'Category']属性中至少有3个相同则输出True，\
                    否则输出False,  \
            } \
            "
        },
        {
            "role": "system",
            "content": [
                {
                    "type": "image",
                    "min_pixels": 224 * 224,
                    "max_pixels": 1280 * 28 * 28,
                },
            ],
        },
        {
            "role": "system",
            "content": [
                {
                    "type": "image",
                    "min_pixels": 224 * 224,
                    "max_pixels": 1280 * 28 * 28,
                },
            ],
        },
        {
            "role": "user",
            "content": "以python dict的形式输出第二张图像的比对信息。"
        }
    ]
    best_img = get_best_image(track_imgs)
    if std_img is not None:
        img_list = [std_img, best_img]
    else:
        img_list = [best_img, best_img]

    output_text = qwen_prompt(img_list, messages)
    contrast_pair = ast.literal_eval(output_text.strip('```python\n'))

    return contrast_pair


def item_analysis(stream_dict):
    track_imgs = stream_pipeline(stream_dict)
    std_img = None
    if stream_dict['goodsPic'] is not None:
        response = requests.get(stream_dict['goodsPic'])
        std_img = Image.open(BytesIO(response.content))
    description_dict = get_product_description(std_img, track_imgs)

    return description_dict


def main():
    # sample input dict
    stream_dict = {
        "goodsName": "优诺优丝黄桃果粒风味发酵乳",
        "measureProperty": 0,
        "qty": 1,
        "price": 25.9,
        "weight": 560,  # 单位克
        "barcode": "6931806801024",
        "video": "https://ieemoo-ai.obs.cn-east-3.myhuaweicloud.com/videos/20231009/04/04_20231009-082149_21f2ca35-f2c2-4386-8497-3e7a3b407f03_4901872831197.mp4",
        "goodsPic": "https://ieemoo-storage.obs.cn-east-3.myhuaweicloud.com/lhpic/6931806801024.jpg",
        "measureUnit": "组",
        "goodsSpec": "405g"
    }

    result = item_analysis(stream_dict)
    print(result)


if __name__ == "__main__":
    main()