import os, yaml
import gradio as gr
import requests
import argparse

from PIL import Image

import numpy as np
import torch
from transformers import AutoModelForCausalLM

from huggingface_hub import hf_hub_download


## InstructIR Plugin ##
from insir_models import instructir
from insir_text.models import LanguageModel, LMHead

hf_hub_download(repo_id="marcosv/InstructIR", filename="im_instructir-7d.pt", local_dir="./")
hf_hub_download(repo_id="marcosv/InstructIR", filename="lm_instructir-7d.pt", local_dir="./")

CONFIG     = "eval5d.yml"
LM_MODEL   = "lm_instructir-7d.pt"
MODEL_NAME = "im_instructir-7d.pt"

def dict2namespace(config):
    namespace = argparse.Namespace()
    for key, value in config.items():
        if isinstance(value, dict):
            new_value = dict2namespace(value)
        else:
            new_value = value
        setattr(namespace, key, new_value)
    return namespace


# parse config file
with open(os.path.join(CONFIG), "r") as f:
    config = yaml.safe_load(f)

cfg = dict2namespace(config)

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
ir_model = instructir.create_model(input_channels =cfg.model.in_ch, width=cfg.model.width, enc_blks = cfg.model.enc_blks, 
                            middle_blk_num = cfg.model.middle_blk_num, dec_blks = cfg.model.dec_blks, txtdim=cfg.model.textdim)
ir_model = ir_model.to(device)
print ("IMAGE MODEL CKPT:", MODEL_NAME)
ir_model.load_state_dict(torch.load(MODEL_NAME, map_location="cpu"), strict=True)

os.environ["TOKENIZERS_PARALLELISM"] = "false"
LMODEL = cfg.llm.model
language_model = LanguageModel(model=LMODEL)
lm_head = LMHead(embedding_dim=cfg.llm.model_dim, hidden_dim=cfg.llm.embd_dim, num_classes=cfg.llm.nclasses)
lm_head = lm_head.to(device)

print("LMHEAD MODEL CKPT:", LM_MODEL)
lm_head.load_state_dict(torch.load(LM_MODEL, map_location="cpu"), strict=True)

def process_img(image, prompt=None):
    if prompt is None:
        prompt = chat("How to improve the quality of the image?", [], image, None, None, None)
        prompt += "Please help me improve its quality!"
        print(prompt)
    img = np.array(image)
    img = img / 255.
    img = img.astype(np.float32)
    y = torch.tensor(img).permute(2,0,1).unsqueeze(0).to(device)

    lm_embd = language_model(prompt)
    lm_embd = lm_embd.to(device)

    with torch.no_grad():
        text_embd, deg_pred = lm_head(lm_embd)
        x_hat = ir_model(y, text_embd)

    restored_img = x_hat.squeeze().permute(1,2,0).clamp_(0, 1).cpu().detach().numpy()
    restored_img = np.clip(restored_img, 0. , 1.)

    restored_img = (restored_img * 255.0).round().astype(np.uint8)  # float32 to uint8
    return Image.fromarray(restored_img) #(image, Image.fromarray(restored_img))

## InstructIR Plugin ##
model = AutoModelForCausalLM.from_pretrained("q-future/co-instruct-preview", 
                                             trust_remote_code=True, 
                                             torch_dtype=torch.float16, 
                                             attn_implementation="eager",
                                             device_map={"":"cuda:0"})

def chat(message, history, image_1, image_2, image_3, image_4):
    print(history)
    if history:
        if image_1 is not None and image_2 is None:
            past_message = "USER: The input image: <|image|>" + history[0][0] + " ASSISTANT:" + history[0][1]
            for i in range((len(history) - 1)):
                past_message += "USER:" +history[i][0] + " ASSISTANT:" + history[i][1] + "</s>"
            message = past_message + "USER:" + message + " ASSISTANT:"
            images = [image_1]
        if image_1 is not None and image_2 is not None:
            if image_3 is None:
                past_message = "USER: The first image: <|image|>\nThe second image: <|image|>" + history[0][0] + " ASSISTANT:" + history[0][1] + "</s>"
                for i in range((len(history) - 1)):
                    past_message += "USER:" + history[i][0] + " ASSISTANT:" + history[i][1] + "</s>"
                message = past_message + "USER:" + message + " ASSISTANT:"
                images = [image_1, image_2]
            else:
                if image_4 is None:
                    past_message = "USER: The first image: <|image|>\nThe second image: <|image|>\nThe third image:<|image|>" + history[0][0] + " ASSISTANT:" + history[0][1] + "</s>"
                    for i in range((len(history) - 1)):
                        past_message += "USER:" + history[i][0] + " ASSISTANT:" + history[i][1] + "</s>"
                    message = past_message + "USER:" + message + " ASSISTANT:"
                    images = [image_1, image_2, image_3]
                else:
                    past_message = "USER: The first image: <|image|>\nThe second image: <|image|>\nThe third image:<|image|>\nThe fourth image:<|image|>" + history[0][0] + " ASSISTANT:" + history[0][1] + "</s>"
                    for i in range((len(history) - 1)):
                        past_message += "USER:" + history[i][0] + " ASSISTANT:" + history[i][1] + "</s>"
                    message = past_message + "USER:" + message + " ASSISTANT:"
                    images = [image_1, image_2, image_3, image_4]
    else:  
        if image_1 is not None and image_2 is None:
            message = "USER: The input image: <|image|>" + message + " ASSISTANT:"
            images = [image_1]
        if image_1 is not None and image_2 is not None:
            if image_3 is None:
                message = "USER: The first image: <|image|>\nThe second image: <|image|>" + message + " ASSISTANT:"
                images = [image_1, image_2]
            else:
                if image_4 is None:
                    message = "USER: The first image: <|image|>\nThe second image: <|image|>\nThe third image:<|image|>" + message + " ASSISTANT:"
                    images = [image_1, image_2, image_3]
                else:
                    message = "USER: The first image: <|image|>\nThe second image: <|image|>\nThe third image:<|image|>\nThe fourth image:<|image|>" + message + " ASSISTANT:"
                    images = [image_1, image_2, image_3, image_4]
    
    print(message)
    
    return model.tokenizer.batch_decode(model.chat(message, images, max_new_tokens=600).clamp(0, 100000))[0].split("ASSISTANT:")[-1]

#### Image,Prompts examples
examples = [
    ["Which part of the image is relatively clearer, the upper part or the lower part? Please analyze in details.", "examples/sausage.jpg", None],
    ["Which image is noisy, and which one is with motion blur? Please analyze in details.", "examples/211.jpg", "examples/frog.png"],
            ["What is the problem in this image, and how to fix it? Please answer my questions one by one.", "examples/lol_748.png", None],
]

#<h1 align="center"><a href="https://github.com/Q-Future/Q-Instruct"><img src="https://github.com/Q-Future/Q-Instruct/blob/main/q_instruct_logo.png?raw=true", alt="Q-Instruct (mPLUG-Owl-2)" border="0" style="margin: 0 auto; height: 85px;" /></a> </h1>


title = "Co-Instruct-Plus🧑‍🏫🖌️"
with gr.Blocks(title="Co-Instruct-Plus🧑‍🏫🖌️") as demo:
    title_markdown = ("""


<div align="center">Built upon <strong>Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models</strong></div>

<div align="center">Build upin Co-Instruct: <strong>Towards Open-ended Visual Quality Comparison</strong></div>   

<div align="center">Co-Instruct is the Upgraded Version of Q-Instruct with Multi-image (up to 4, same as GPT-4V) Support! We also support <a href='https://huggingface.co/marcosv/InstructIR'>InstructIR</a> as PLUGIN!</div>
<h5 align="center"> Please find our more accurate visual scoring demo on <a href='https://huggingface.co/spaces/teowu/OneScorer'>[OneScorer]</a>!</h2>
<div align="center">
    <div style="display:flex; gap: 0.25rem;" align="center">
        <a href='https://github.com/Q-Future/Q-Instruct'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
        <a href="https://Q-Instruct.github.io/Q-Instruct/fig/Q_Instruct_v0_1_preview.pdf"><img src="https://img.shields.io/badge/Technical-Report-red"></a>
        <a href='https://github.com/Q-Future/Q-Instruct/stargazers'><img src='https://img.shields.io/github/stars/Q-Future/Q-Instruct.svg?style=social'></a>
    </div>
</div>
""")
    gr.Markdown(title_markdown)
    with gr.Row():
            input_img_1 = gr.Image(type='pil', label="Image 1 (First image)")
            input_img_2 = gr.Image(type='pil', label="Image 2 (Second image)")
            input_img_3 = gr.Image(type='pil', label="Image 3 (Third image)")
            input_img_4 = gr.Image(type='pil', label="Image 4 (Third image)")
    with gr.Row():
        with gr.Column(scale=2):
            gr.ChatInterface(fn = chat, additional_inputs=[input_img_1, input_img_2, input_img_3, input_img_4], theme="Soft", examples=examples)
        with gr.Column(scale=1):
            input_image_ir = gr.Image(type="pil", label="Image for Auto Restoration")
            output_image_ir = gr.Image(type="pil", label="Output of Auto Restoration")
            gr.Interface(
                fn=process_img,
                inputs=[input_image_ir],
                outputs=[output_image_ir],
                examples=["examples/gopro.png", "examples/noise50.png", "examples/lol_748.png"],
            )
    demo.launch(share=True)