import gradio as gr
from PIL import Image, ImageEnhance
import torch
import torch.nn.functional as F
from torchvision import transforms
from torchvision.models import resnet34
from torchvision.models.segmentation import deeplabv3_resnet50
import numpy as np
import cv2

# Load a pre-trained ResNet model for remastering
resnet_model = resnet34(pretrained=True)
resnet_model.eval()

# Load a pre-trained DeepLab model for segmentation (optional for advanced remastering)
deeplab_model = deeplabv3_resnet50(pretrained=True)
deeplab_model.eval()

# Define the upscaling function using super-resolution techniques
def upscale_image(image, upscale_factor=2):
    # Convert the image to a tensor and upscale it using a neural network
    preprocess = transforms.Compose([
        transforms.ToTensor(),
        transforms.Lambda(lambda x: x.unsqueeze(0))
    ])
    img_tensor = preprocess(image)
    upscaled_tensor = F.interpolate(img_tensor, scale_factor=upscale_factor, mode='bicubic', align_corners=False)
    upscaled_image = transforms.ToPILImage()(upscaled_tensor.squeeze())
    
    return upscaled_image

# Define the remastering function
def remaster_image(image, color_range=1.0, sharpness=1.0, hdr_intensity=1.0, tone_mapping=1.0, color_grading=1.0):
    # Adjust color range
    enhancer = ImageEnhance.Color(image)
    image = enhancer.enhance(color_range)

    # Adjust sharpness
    enhancer = ImageEnhance.Sharpness(image)
    image = enhancer.enhance(sharpness)

    # Apply a simulated HDR effect using tone mapping
    enhancer = ImageEnhance.Brightness(image)
    image = enhancer.enhance(hdr_intensity)
    
    enhancer = ImageEnhance.Contrast(image)
    image = enhancer.enhance(color_grading)
    
    # Optional: Use segmentation to remaster specific regions
    input_tensor = transforms.ToTensor()(image).unsqueeze(0)
    with torch.no_grad():
        output = deeplab_model(input_tensor)['out'][0]
    output_predictions = output.argmax(0)
    
    # Process each segmented region (e.g., sky, water) differently (optional)
    # Example: Apply a slight blur to the sky region to create a dreamy effect
    mask = output_predictions.byte().cpu().numpy()
    segmented_image = np.array(image)
    segmented_image[mask == 15] = cv2.GaussianBlur(segmented_image[mask == 15], (5, 5), 0)
    
    final_image = Image.fromarray(segmented_image)
    
    return final_image

# Process function for Gradio
def process_image(image, upscale=False, upscale_factor=2, remaster=False, color_range=1.0, sharpness=1.0, hdr_intensity=1.0, tone_mapping=1.0, color_grading=1.0):
    if upscale:
        image = upscale_image(image, upscale_factor)
    
    if remaster:
        image = remaster_image(image, color_range, sharpness, hdr_intensity, tone_mapping, color_grading)
    
    return image

# Gradio UI
with gr.Blocks() as demo:
    with gr.Row():
        image_input = gr.Image(label="Upload Image", type="pil")
        image_output = gr.Image(label="Output Image")
    
    with gr.Row():
        with gr.Group():
            gr.Markdown("### Upscaling Options")
            upscale_checkbox = gr.Checkbox(label="Apply Upscaling")
            upscale_factor = gr.Slider(1, 8, value=2, label="Upscale Factor")
        
        with gr.Group():
            gr.Markdown("### Remastering Options")
            remaster_checkbox = gr.Checkbox(label="Apply Remastering")
            color_range = gr.Slider(0.5, 2.0, value=1.0, label="Dynamic Color Range")
            sharpness = gr.Slider(0.5, 2.0, value=1.0, label="Sharpness")
            hdr_intensity = gr.Slider(0.5, 2.0, value=1.0, label="HDR Intensity")
            tone_mapping = gr.Slider(0.5, 2.0, value=1.0, label="Tone Mapping")
            color_grading = gr.Slider(0.5, 2.0, value=1.0, label="Color Grading")
    
    process_button = gr.Button("Process Image")
    
    process_button.click(
        process_image,
        inputs=[image_input, upscale_checkbox, upscale_factor, remaster_checkbox, color_range, sharpness, hdr_intensity, tone_mapping, color_grading],
        outputs=image_output
    )

demo.launch()