import torch
import torchvision

import cv2
import numpy as np
from models import monet as MoNet
import argparse
from utils.dataset.process import ToTensor, Normalize
import gradio as gr
import os

def load_image(img_path):
    if isinstance(img_path, str):
        d_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
    else:
        d_img = cv2.cvtColor(np.asarray(img_path),cv2.COLOR_RGB2BGR)
    d_img = cv2.resize(d_img, (224, 224), interpolation=cv2.INTER_CUBIC)
    d_img = cv2.cvtColor(d_img, cv2.COLOR_BGR2RGB)
    d_img = np.array(d_img).astype('float32') / 255
    d_img = np.transpose(d_img, (2, 0, 1))
    
    return d_img

def predict(image):
    parser = argparse.ArgumentParser()
    # model related
    parser.add_argument('--backbone', dest='backbone', type=str, default='vit_base_patch8_224',
                        help='The backbone for MoNet.')
    parser.add_argument('--mal_num', dest='mal_num', type=int, default=3, help='The number of the MAL modules.')
    config = parser.parse_args()

    model = MoNet.MoNet(config).cuda()
    model.load_state_dict(torch.load('best_model.pkl'))
    model.eval()

    trans = torchvision.transforms.Compose([Normalize(0.5, 0.5), ToTensor()])

    """Run a single prediction on the model"""
    img = load_image(image)
    img_tensor = trans(img).unsqueeze(0).cuda()
    iq = model(img_tensor).cpu().detach().numpy().tolist()[0]

    return "The image quality of the image is: {}".format(round(iq, 4))

os.system("wget -O best_model.pkl https://huggingface.co/Zevin2023/MoC-IQA/resolve/main/Koniq10K_570908.pkl")

interface = gr.Interface(fn=predict, inputs="image", outputs="text")
interface.launch()