import gradio as gr
from PIL import Image, ImageEnhance
import numpy as np
import torch
import torchvision.transforms as transforms
from torchvision.models import resnet34

# Load a pre-trained ResNet model
model = resnet34(pretrained=True)

# Define the upscaling function
def upscale_image(image, upscale_factor=2, sharpness=1.0, contrast=1.0, brightness=1.0):
    # Resize the image
    width, height = image.size
    new_size = (int(width * upscale_factor), int(height * upscale_factor))
    upscaled_image = image.resize(new_size, Image.BICUBIC)
    
    # Apply sharpness, contrast, and brightness adjustments
    upscaled_image = ImageEnhance.Sharpness(upscaled_image).enhance(sharpness)
    upscaled_image = ImageEnhance.Contrast(upscaled_image).enhance(contrast)
    upscaled_image = ImageEnhance.Brightness(upscaled_image).enhance(brightness)
    
    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)

    # For HDR simulation and tone mapping, we're using simple brightness adjustments
    enhancer = ImageEnhance.Brightness(image)
    image = enhancer.enhance(hdr_intensity)

    # Simulate color grading by adjusting contrast
    enhancer = ImageEnhance.Contrast(image)
    image = enhancer.enhance(color_grading)

    return image

# Process function for Gradio
def process_image(image, upscale=False, upscale_factor=2, sharpness=1.0, contrast=1.0, brightness=1.0,
                  remaster=False, color_range=1.0, hdr_intensity=1.0, tone_mapping=1.0, color_grading=1.0):
    if upscale:
        image = upscale_image(image, upscale_factor, sharpness, contrast, brightness)
    
    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")
            sharpness = gr.Slider(0.5, 2.0, value=1.0, label="Sharpness")
            contrast = gr.Slider(0.5, 2.0, value=1.0, label="Contrast")
            brightness = gr.Slider(0.5, 2.0, value=1.0, label="Brightness")
        
        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")
            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, sharpness, contrast, brightness,
                remaster_checkbox, color_range, hdr_intensity, tone_mapping, color_grading],
        outputs=image_output
    )

demo.launch()