from typing import Dict, List, Any
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
import base64
from io import BytesIO


class EndpointHandler():
    def __init__(self, path=""):
        # Preload all the elements you are going to need at inference.
        # pseudo:
        # self.model= load_model(path)
        self.model = AutoModelForVision2Seq.from_pretrained(path).to("cuda")
        self.processor = AutoProcessor.from_pretrained(path)

        # prompt = "<grounding>An image of"

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        
        prompt = data.pop("prompt")
        image_base64 = data.pop("image_base64")

        image_data = base64.b64decode(image_base64)
        image = Image.open(BytesIO(image_data))

        inputs = self.processor(text=prompt, images=image, return_tensors="pt").to("cuda")

        generated_ids = self.model.generate(
            pixel_values=inputs["pixel_values"],
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            image_embeds=None,
            image_embeds_position_mask=inputs["image_embeds_position_mask"],
            use_cache=True,
            max_new_tokens=128,
        )
        generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

        # Specify `cleanup_and_extract=False` in order to see the raw model generation.
        processed_text = self.processor.post_process_generation(generated_text, cleanup_and_extract=False)

        # print(processed_text)
        # `<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.`

        # By default, the generated  text is cleanup and the entities are extracted.
        processed_text, entities = self.processor.post_process_generation(generated_text)

        # print(processed_text)
        # `An image of a snowman warming himself by a fire.`
        return [{"processed_text": processed_text}]

        # print(entities)
        # `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]`