import gradio as gr
import requests
import time
import json
import base64
import os
from io import BytesIO
import html
import re
from PIL import Image  # Add this import



class Prodia:
    def __init__(self, api_key, base=None):
        self.base = base or "https://api.prodia.com/v1"
        self.headers = {
            "X-Prodia-Key": api_key
        }
    
    def generate(self, params):
        response = self._post(f"{self.base}/sd/generate", params)
        return response.json()
    
    def transform(self, params):
        response = self._post(f"{self.base}/sd/transform", params)
        return response.json()
    
    def controlnet(self, params):
        response = self._post(f"{self.base}/sd/controlnet", params)
        return response.json()
    
    def get_job(self, job_id):
        response = self._get(f"{self.base}/job/{job_id}")
        return response.json()

    def wait(self, job):
        job_result = job

        while job_result['status'] not in ['succeeded', 'failed']:
            time.sleep(0.25)
            job_result = self.get_job(job['job'])

        return job_result

    def list_models(self):
        response = self._get(f"{self.base}/sd/models")
        return response.json()

    def list_samplers(self):
        response = self._get(f"{self.base}/sd/samplers")
        return response.json()

    def list_loras(self):
        response = self._get(f"{self.base}/sd/loras")
        return response.json()

    def _post(self, url, params):
        headers = {
            **self.headers,
            "Content-Type": "application/json"
        }
        response = requests.post(url, headers=headers, data=json.dumps(params))

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response

    def _get(self, url):
        response = requests.get(url, headers=self.headers)

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response


def image_to_base64(image):
    # Convert the image to bytes
    buffered = BytesIO()
    image.save(buffered, format="PNG")  # You can change format to PNG if needed
    
    # Encode the bytes to base64
    img_str = base64.b64encode(buffered.getvalue())

    return img_str.decode('utf-8')  # Convert bytes to string


def remove_id_and_ext(text):
    text = re.sub(r'\[.*\]$', '', text)
    extension = text[-12:].strip()
    if extension == "safetensors":
        text = text[:-13]
    elif extension == "ckpt":
        text = text[:-4]
    return text


def get_data(text):
    results = {}
    patterns = {
        'prompt': r'(.*)',
        'negative_prompt': r'Negative prompt: (.*)',
        'steps': r'Steps: (\d+),',
        'seed': r'Seed: (\d+),',
        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)', 
        'model': r'Model:\s*([^\s,]+)',
        'cfg_scale': r'CFG scale:\s*([\d\.]+)',
        'size': r'Size:\s*([0-9]+x[0-9]+)',
        }
    for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
        match = re.search(patterns[key], text)
        if match:
            results[key] = match.group(1)
        else:
            results[key] = None
    if results['size'] is not None:
        w, h = results['size'].split("x")
        results['w'] = w
        results['h'] = h
    else:
        results['w'] = None
        results['h'] = None
    return results


def send_to_txt2img(image):

    result = {tabs: gr.update(selected="t2i")}

    try:
        text = image.info['parameters']
        data = get_data(text)
        result[prompt] = gr.update(value=data['prompt'])
        result[negative_prompt] = gr.update(value=data['negative_prompt']) if data['negative_prompt'] is not None else gr.update()
        result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update()
        result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update()
        result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update()
        result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update()
        result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update()
        result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update()
        if model in model_names:
            result[model] = gr.update(value=model_names[model])
        else:
            result[model] = gr.update()
        return result

    except Exception as e:
        print(e)

        return result


prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
model_list = prodia_client.list_models()
model_names = {}

for model_name in model_list:
    name_without_ext = remove_id_and_ext(model_name)
    model_names[name_without_ext] = model_name


def add_watermark(image_path, watermark_path, position, scale=1.5):
    base_image = Image.open(image_path)
    watermark = Image.open(watermark_path).convert("RGBA")

    # Resize the watermark
    watermark = watermark.resize((int(watermark.width * scale), int(watermark.height * scale)), Image.LANCZOS)

    # Calculate the position
    if position == 'bottom_right':
        position = (base_image.width - watermark.width, base_image.height - watermark.height)

    # Paste the watermark
    base_image.paste(watermark, position, watermark)
    return base_image

def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, lora, batch_size, batch_count):
    images = []
    for _ in range(batch_count):
        for _ in range(batch_size):  # Loop for batch size
            result = prodia_client.generate({
                "prompt": prompt,
                "negative_prompt": negative_prompt,
                "model": model,
                "steps": steps,
                "sampler": sampler,
                "cfg_scale": cfg_scale,
                "upscale": True,
                "width": width,
                "height": height,
                "seed": seed,
                "lora": lora
            })

            job = prodia_client.wait(result)
            print(job)  # Debugging: print the job dictionary

            if "imageUrl" not in job:
                raise KeyError(f"'imageUrl' not found in job response: {job}")

            image_url = job["imageUrl"]
            print(f"Downloading image from URL: {image_url}")  # Debugging: print the image URL

            # Download the image
            response = requests.get(image_url)
            image_path = f"generated_image_{len(images)}.png"
            with open(image_path, "wb") as f:
                f.write(response.content)

            # Add watermark
            watermarked_image = add_watermark(image_path, "logo.webp", "bottom_right")
            watermarked_image.save(image_path)
            images.append(image_path)

    return images

def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, lora, batch_size, batch_count):
    images = []
    for _ in range(batch_count):
        for _ in range(batch_size):  # Loop for batch size
            result = prodia_client.transform({
                "imageData": image_to_base64(input_image),
                "denoising_strength": denoising,
                "prompt": prompt,
                "negative_prompt": negative_prompt,
                "model": model,
                "steps": steps,
                "sampler": sampler,
                "upscale": True,
                "cfg_scale": cfg_scale,
                "width": width,
                "height": height,
                "seed": seed,
                "lora": lora
            })

            job = prodia_client.wait(result)
            print(job)  # Debugging: print the job dictionary

            if "imageUrl" not in job:
                raise KeyError(f"'imageUrl' not found in job response: {job}")

            image_url = job["imageUrl"]
            print(f"Downloading image from URL: {image_url}")  # Debugging: print the image URL

            # Download the image
            response = requests.get(image_url)
            image_path = f"transformed_image_{len(images)}.png"
            with open(image_path, "wb") as f:
                f.write(response.content)

            # Add watermark
            watermarked_image = add_watermark(image_path, "logo.webp", "bottom_right")
            watermarked_image.save(image_path)
            images.append(image_path)

    return images


css = """
#generate {
    height: 100%;
}
"""

loras = prodia_client.list_loras()

# Set default LoRA
default_lora = "more_details_v10" if "more_details_v10" in loras else loras[0]

with gr.Blocks(css=css, theme=gr.themes.Monochrome()) as demo:  # Apply the Soft theme
    with gr.Row():
        with gr.Column(scale=6):
            model = gr.Dropdown(interactive=True, value="amIReal_V41.safetensors [0a8a2e61]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
            lora = gr.Dropdown(interactive=True, value=default_lora, show_label=True, label="LoRA", choices=loras)  # Set default LoRA
  
    with gr.Tabs() as tabs:
        with gr.Tab("txt2img", id='t2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="(3d, render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4), (worst quality, low quality:1.4), child, deformed, malformed, bad teeth, bad hands, bad fingers, bad eyes, long body, blurry, duplicated, cloned, duplicate body parts, disfigured, extra limbs, fused fingers, extra fingers, twisted, distorted, malformed hands, mutated hands, mutated fingers, conjoined, missing limbs, bad anatomy, bad proportions, logo, watermark, text, copyright, signature, lowres, mutated, mutilated, artifacts, gross, ugly, tattoo, weird lettering, weird drawing, easynegative, FastNegativeV2")
                with gr.Column():
                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")
                    
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Generation"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
                                
                            with gr.Column(scale=1):
                                steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
    
                        with gr.Row():
                            with gr.Column(scale=1):
                                width = gr.Slider(label="Width", maximum=1024, value=640, step=8)
                                height = gr.Slider(label="Height", maximum=1024, value=1024, step=8)
                            
                            with gr.Column(scale=1):
                                batch_size = gr.Slider(label="Batch Size", minimum=1, maximum=4, value=1, step=1)  # Add batch size slider
                                batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=4, value=1, step=1)  # Add batch count slider
    
                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                        seed = gr.Number(label="Seed", value=-1)

                with gr.Column(scale=2):
                    image_output = gr.Gallery(label="Generated Images")  # Use Gallery to display multiple images
    
            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
                                               seed, lora, batch_size, batch_count], outputs=image_output, concurrency_limit=64)  # Add batch size and count inputs
        
        with gr.Tab("img2img", id='i2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
                    i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="(3d, render, cgi, doll, painting, fake, cartoon, 3d modeling:1.4), (worst quality, low quality:1.4), child, deformed, malformed, bad teeth, bad hands, bad fingers, bad eyes, long body, blurry, duplicated, cloned, duplicate body parts, disfigured, extra limbs, fused fingers, extra fingers, twisted, distorted, malformed hands, mutated hands, mutated fingers, conjoined, missing limbs, bad anatomy, bad proportions, logo, watermark, text, copyright, signature, lowres, mutated, mutilated, artifacts, gross, ugly, tattoo, weird lettering, weird drawing, easynegative, FastNegativeV2")
                with gr.Column():
                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
                    
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Generation"):
                        i2i_image_input = gr.Image(type="pil")

                        with gr.Row():
                            with gr.Column(scale=1):
                                i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
                                
                            with gr.Column(scale=1):
                                i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)

                        with gr.Row():
                            with gr.Column(scale=1):
                                i2i_width = gr.Slider(label="Width", maximum=1024, value=640, step=8)
                                i2i_height = gr.Slider(label="Height", maximum=1024, value=1024, step=8)
                            
                            with gr.Column(scale=1):
                                i2i_batch_size = gr.Slider(label="Batch Size", minimum=1, maximum=4, value=1, step=1)  # Add batch size slider
                                i2i_batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=4, value=1, step=1)  # Add batch count slider
    
                        i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
                        i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1)
                        i2i_seed = gr.Number(label="Seed", value=-1)

                with gr.Column(scale=2):
                    i2i_image_output = gr.Gallery(label="Transformed Images")  # Use Gallery to display multiple images
    
            i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
                                                   model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
                                                   i2i_seed, lora, i2i_batch_size, i2i_batch_count], outputs=i2i_image_output, concurrency_limit=64)  # Add batch size and count inputs
        
        with gr.Tab("PNG Info"):
            def plaintext_to_html(text, classname=None):
                content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
    
                return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
    
    
            def get_exif_data(image):
                items = image.info
    
                info = ''
                for key, text in items.items():
                    info += f"""
                    <div>
                    <p><b>{plaintext_to_html(str(key))}</b></p>
                    <p>{plaintext_to_html(str(text))}</p>
                    </div>
                    """.strip()+"\n"
    
                if len(info) == 0:
                    message = "Nothing found in the image."
                    info = f"<div><p>{message}<p></div>"
    
                return info
    
            with gr.Row():
                with gr.Column():
                    image_input = gr.Image(type="pil")
                    
                with gr.Column():
                    exif_output = gr.HTML(label="EXIF Data")
                    send_to_txt2img_btn = gr.Button("Send to txt2img")
    
            image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
            send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input], outputs=[tabs, prompt, negative_prompt,
                                                                                      steps, seed, model, sampler,
                                                                                      width, height, cfg_scale],
                                      concurrency_limit=64)

demo.queue(max_size=80, api_open=True).launch(max_threads=256, show_api=True)