import gradio as gr
import requests
import io
import random
import os
import time
from PIL import Image
from deep_translator import GoogleTranslator
import json


API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

article_text = """
<div style="text-align: center;">
    <p>ဒီ Tool မှာ Train ထားသည့် LoRA Model များကို ထည့်သွင်းပြီးထုတ်နိုင်ပါတယ်</p>
    
    <div style="display: flex; justify-content: center;">
        <a href="https://writtech.com/waloneai/walone-lora-library/">
            <img src="https://writtech.com/waloneai/premium/lora.png" 
                 alt="Buy Me a Coffee" 
                 style="height: 40px; width: auto; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); border-radius: 10px;">
        </a>
    </div>
</div>
"""

def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024):
    if prompt == "" or prompt == None:
        return None

    if lora_id.strip() == "" or lora_id == None:
        lora_id = "black-forest-labs/FLUX.1-dev" 

    key = random.randint(0, 999)

    API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
    
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    
    # prompt = GoogleTranslator(source='my', target='en').translate(prompt)
    # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    # print(f'\033[1mGeneration {key}:\033[0m {prompt}')

    # If seed is -1, generate a random seed and use it
    if randomize_seed:
        seed = random.randint(1, 4294967296)
    
    payload = {
        "inputs": prompt,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed,
        "parameters": {
            "width": width,  # Pass the width to the API
            "height": height  # Pass the height to the API
        }
    }

    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
    if response.status_code != 200:
        print(f"Error: Failed to get image. Response status: {response.status_code}")
        print(f"Response content: {response.text}")
        if response.status_code == 503:
            raise gr.Error(f"{response.status_code} : The model is being loaded")
        raise gr.Error(f"{response.status_code}")
    
    try:
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
        return image, seed, seed
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None


examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css = """
#app-container {
    max-width: 600px;
    margin-left: auto;
    margin-right: auto;
}
"""

with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
    gr.HTML("<center><h1>Walone AI Image LoRA</h1></center>")
    with gr.Column(elem_id="app-container"):
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
                with gr.Row():
                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA path (optional)", placeholder="shweaung/vintage-ads-flux")
                with gr.Row():
                    with gr.Accordion("Advanced Settings", open=False):
                        with gr.Row():
                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8)
                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8)
                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
                        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                        with gr.Row():
                            steps = gr.Slider(label="Sampling steps", value=28, minimum=1, maximum=100, step=1)
                            cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
                        # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])

        with gr.Row():
            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
        with gr.Row():
            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
        with gr.Row():
            seed_output = gr.Textbox(label="Seed Used", show_copy_button = True, elem_id="seed-output")

        gr.Markdown(article_text)
        
        gr.Examples(
            examples = examples,
            inputs = [text_prompt],
        )

        
        text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])

app.launch(show_api=False, share=False)