from typing import Dict, List, Any
from transformers import CLIPModel, AutoProcessor, AutoTokenizer
import torch
from PIL import Image
import requests


class EndpointHandler:
    def __init__(self, path=""):
        self.model = CLIPModel.from_pretrained("patrickjohncyh/fashion-clip")
        self.processor = AutoProcessor.from_pretrained("patrickjohncyh/fashion-clip")
        self.tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        parameters = data.pop("parameters", {"mode": "text"})
        inputs = data.pop("inputs", data)
        with torch.no_grad():
            if parameters["mode"] == "text":
                inputs = self.tokenizer(inputs, padding=True, return_tensors="pt")
                features = self.model.get_text_features(**inputs)

            if parameters["mode"] == "image":
                image = Image.open(requests.get(inputs, stream=True).raw)

                inputs = self.processor(images=image, return_tensors="pt")
                features = self.model.get_image_features(**inputs)

            return features[0].tolist()