import os
import gradio as gr
import base64
from random import randint
from all_models import models
from io import BytesIO
from PIL import Image
from fastapi import FastAPI, Request
from deep_translator import GoogleTranslator

css_code = os.getenv("DazDinGo_CSS")

# Initialize translator
translator = GoogleTranslator(source='auto', target='en')

# Load models
models_load = {}
for model in models:
    try:
        models_load[model] = gr.load(f'models/{model}')
    except Exception as error:
        models_load[model] = gr.Interface(lambda txt: None, ['text'], ['image'])

app = FastAPI()

def gen_image(model_str, prompt):
    if model_str == 'NA':
        return None
    # Translate prompt to English {noise} {klir}
    translated_prompt = translator.translate(prompt)
    noise = str(randint(0, 4294967296))
    klir = '| ultra detail, ultra elaboration, ultra quality, perfect'
    return models_load[model_str](f'{translated_prompt} {noise} {klir}')

def image_to_base64(image):
    buffered = BytesIO()
    if isinstance(image, str):  # if it's a file path
        img = Image.open(image)
        img.save(buffered, format="JPEG")
    else:  # if it's a PIL Image
        image.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue()).decode()

# API endpoint
@app.post("/generate")
async def api_generate(request: Request):
    data = await request.json()
    model = data.get('model', models[0])
    prompt = data.get('prompt', '')
    
    if model not in models:
        return {"error": "Model not found"}
    
    # Translate prompt to English for API endpoint too
    translated_prompt = translator.translate(prompt)
    image = gen_image(model, translated_prompt)
    if image is None:
        return {"error": "Image generation failed"}
    
    base64_str = image_to_base64(image)
    
    return {
        "status": "success",
        "model": model,
        "original_prompt": prompt,
        "translated_prompt": translated_prompt,
        "image_base64": base64_str,
        "image_format": "jpeg"
    }

# Gradio Interface
def make_me():
    with gr.Row():
        with gr.Column(scale=4):
            txt_input = gr.Textbox(
                label='Your prompt:', 
                lines=4, 
                container=False, 
                elem_id="custom_textbox", 
                placeholder="Prompt"
            )
        
        with gr.Column(scale=1):
            gen_button = gr.Button('Generate image', elem_id="custom_gen_button")
            stop_button = gr.Button('Stop', variant='secondary', interactive=False, 
                                elem_id="custom_stop_button")
            
            def on_generate_click():
                return gr.Button('Generate image', elem_id="custom_gen_button"), gr.Button('Stop', variant='secondary', interactive=True, elem_id="custom_stop_button")
            
            def on_stop_click():
                return gr.Button('Generate image', elem_id="custom_gen_button"), gr.Button('Stop', variant='secondary', interactive=False, elem_id="custom_stop_button")
            
            gen_button.click(on_generate_click, inputs=None, outputs=[gen_button, stop_button])
            stop_button.click(on_stop_click, inputs=None, outputs=[gen_button, stop_button])
    
    with gr.Row():
        with gr.Column():
            model_dropdown = gr.Dropdown(models, label="Select Model", 
                                      value=models[0] if models else None)
            output_image = gr.Image(
                label="Generated Image", 
                width=512,
                height=768,
                elem_id="custom_image", 
                show_label=True, 
                interactive=False
            )
            
            # JSON output
            json_output = gr.JSON(label="API Response")
            
            def generate_wrapper(model_str, prompt):
                # Translate prompt to English
                translated_prompt = translator.translate(prompt)
                image = gen_image(model_str, translated_prompt)
                if image is None:
                    return None, {"error": "Generation failed"}
                
                base64_str = image_to_base64(image)
                response = {
                    "status": "success",
                    "model": model_str,
                    "original_prompt": prompt,
                    "translated_prompt": translated_prompt,
                    "image_base64": base64_str,
                    "image_format": "jpeg"
                }
                return image, response
            
            gen_event = gen_button.click(generate_wrapper, [model_dropdown, txt_input], 
                                     [output_image, json_output])
            stop_button.click(on_stop_click, inputs=None, 
                            outputs=[gen_button, stop_button], cancels=[gen_event])

# Create Gradio app
with gr.Blocks(css=css_code) as demo: 
    make_me()

# Enable queue before mounting
demo.queue()

# Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, demo, path="/")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)