from pathlib import Path

from skimage import io as io_img
import io

import requests
import torch
from PIL import Image
import numpy as np
from huggingface_hub import snapshot_download

from LLAVA_Biovil.llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria, remap_to_uint8
from LLAVA_Biovil.llava.model.builder import load_pretrained_model
from LLAVA_Biovil.llava.conversation import SeparatorStyle, conv_vicuna_v1

from LLAVA_Biovil.llava.constants import IMAGE_TOKEN_INDEX
from utils import create_chest_xray_transform_for_inference

def load_model_from_huggingface(repo_id, model_filename):
    # Download model files
    model_path = snapshot_download(repo_id=repo_id, revision="main")
    model_path = Path(model_path) / model_filename

    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_base='liuhaotian/llava-v1.5-7b',
                                                                           model_name="llava-v1.5-7b-task-lora_radialog_instruct_llava_biovil_unfrozen_2e-5_5epochs_v5_checkpoint-21000", load_8bit=False, load_4bit=False)

    return tokenizer, model, image_processor, context_len

if __name__ == '__main__':
    # config = None
    # model_path = "/home/guests/chantal_pellegrini/RaDialog_LLaVA/LLAVA/checkpoints/llava-v1.5-7b-task-lora_radialog_instruct_llava_biovil_unfrozen_2e-5_5epochs_v5/checkpoint-21000" #TODO hardcoded in huggingface repo probably
    # model_name = get_model_name_from_path(model_path)
    tokenizer, model, image_processor, context_len = load_model_from_huggingface(repo_id="Chantal/RaDialog-interactive-radiology-report-generation", model_filename="model")
    model.config.tokenizer_padding_side = "left"

    findings = "edema, pleural effusion" #TODO should these come from chexpert classifier? Or not needed for this demo/test?

    conv = conv_vicuna_v1.copy()
    REPORT_GEN_PROMPT = f"<image>. Predicted Findings: {findings}. You are to act as a radiologist and write the finding section of a chest x-ray radiology report for this X-ray image and the given predicted findings. Write in the style of a radiologist, write one fluent text without enumeration, be concise and don't provide explanations or reasons."
    print("USER: ", REPORT_GEN_PROMPT)
    conv.append_message("USER", REPORT_GEN_PROMPT)
    conv.append_message("ASSISTANT", None)
    text_input = conv.get_prompt()

    # get the image
    vis_transforms_biovil = create_chest_xray_transform_for_inference(512, center_crop_size=448)
    sample_img_path = "https://openi.nlm.nih.gov/imgs/512/10/10/CXR10_IM-0002-2001.png?keywords=Calcified%20Granuloma" #TODO find good image

    response = requests.get(sample_img_path)
    image = Image.open(io.BytesIO(response.content))
    image = remap_to_uint8(np.array(image))
    image = Image.fromarray(image).convert("L")
    image_tensor = vis_transforms_biovil(image).unsqueeze(0)

    image_tensor = image_tensor.to(model.device, dtype=torch.bfloat16)
    input_ids = tokenizer_image_token(text_input, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)

    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    stopping_criteria = KeywordsStoppingCriteria([stop_str], tokenizer, input_ids)

    # generate a report
    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            do_sample=False,
            use_cache=True,
            max_new_tokens=300,
            stopping_criteria=[stopping_criteria],
            pad_token_id=tokenizer.pad_token_id
        )

    pred = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip().replace("</s>", "")
    print("ASSISTANT: ", pred)

    # add prediction to conversation
    conv.messages.pop()
    conv.append_message("ASSISTANT", pred)
    conv.append_message("USER", "Translate this report to easy language for a patient to understand.")
    conv.append_message("ASSISTANT", None)
    text_input = conv.get_prompt()
    print("USER: ", "Translate this report to easy language for a patient to understand.")

    # generate easy language report
    input_ids = tokenizer_image_token(text_input, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)
    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            do_sample=False,
            use_cache=True,
            max_new_tokens=300,
            stopping_criteria=[stopping_criteria],
            pad_token_id=tokenizer.pad_token_id
        )

    pred = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip().replace("</s>", "")
    print("ASSISTANT: ", pred)