app.py

import gradio as gr
import spaces
import torch
import os
from compel import Compel, ReturnedEmbeddingsType
from diffusers import DiffusionPipeline
import requests

# Model setup
model_name = os.environ.get('MODEL_NAME', 'UnfilteredAI/NSFW-gen-v2')
pipe = DiffusionPipeline.from_pretrained(
    model_name,
    torch_dtype=torch.float16
)
pipe.to('cuda')

compel = Compel(
    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
    requires_pooled=[False, True]
)

# Translation function
@spaces.GPU
def translate_albanian_to_english(text):
    if not text.strip():
        return ""
    for attempt in range(2):
        try:
            response = requests.post(
                "https://hal1993-mdftranslation1234567890abcdef1234567890-fc073a6.hf.space/v1/translate",
                json={"from_language": "sq", "to_language": "en", "input_text": text},
                headers={"accept": "application/json", "Content-Type": "application/json"},
                timeout=5
            )
            response.raise_for_status()
            translated = response.json().get("translate", "")
            return translated
        except Exception as e:
            if attempt == 1:
                raise gr.Error(f"Përkthimi dështoi: {str(e)}")
    raise gr.Error("Përkthimi dështoi. Ju lutem provoni përsëri.")

# Aspect ratio function
def update_aspect_ratio(ratio):
    if ratio == "1:1":
        return 1024, 1024
    elif ratio == "9:16":
        return 576, 1024  # 1024 * 9/16 = 576
    elif ratio == "16:9":
        return 1024, 576  # 1024 * 9/16 = 576
    return 1024, 1024

@spaces.GPU(duration=120)
def generate(prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, num_samples, progress=gr.Progress(track_tqdm=True)):
    # Translate Albanian prompt to English
    final_prompt = translate_albanian_to_english(prompt.strip()) if prompt.strip() else ""
    
    # Use Compel for prompt embeddings
    embeds, pooled = compel(final_prompt)
    neg_embeds, neg_pooled = compel(negative_prompt)
    
    # Run pipeline
    images = pipe(
        prompt_embeds=embeds,
        pooled_prompt_embeds=pooled,
        negative_prompt_embeds=neg_embeds,
        negative_pooled_prompt_embeds=neg_pooled,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        width=width,
        height=height,
        num_images_per_prompt=num_samples
    ).images
    
    # Return single image
    return images[0]

# Gradio interface
def create_demo():
    with gr.Blocks() as demo:
        # CSS for layout, 320px gap, and download button scaling
        gr.HTML("""
        <style>
        body::before {
            content: "";
            display: block;
            height: 320px;
            background-color: var(--body-background-fill);
        }
        button[aria-label="Fullscreen"], button[aria-label="Fullscreen"]:hover {
            display: none !important;
            visibility: hidden !important;
            opacity: 0 !important;
            pointer-events: none !important;
        }
        button[aria-label="Share"], button[aria-label="Share"]:hover {
            display: none !important;
        }
        button[aria-label="Download"] {
            transform: scale(3);
            transform-origin: top right;
            margin: 0 !important;
            padding: 6px !important;
        }
        </style>
        """)

        gr.Markdown("# Krijo Imazhe")
        gr.Markdown("Gjenero imazhe të reja nga përshkrimin yt me fuqinë e inteligjencës artificiale.")

        with gr.Column():
            prompt = gr.Textbox(
                label="Përshkrimi",
                placeholder="Shkruani përshkrimin këtu",
                lines=3
            )
            aspect_ratio = gr.Radio(
                label="Raporti i fotos",
                choices=["9:16", "1:1", "16:9"],
                value="1:1"
            )
            generate_button = gr.Button(value="Gjenero")
            # Hidden components for processing
            negative_prompt = gr.Textbox(
                value="(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn, (deformed | distorted | disfigured:1.3), bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers:1.4, disconnected limbs, blurry, amputation.",
                visible=False
            )
            num_inference_steps = gr.Slider(
                value=60,
                minimum=1,
                maximum=100,
                step=1,
                visible=False
            )
            guidance_scale = gr.Slider(
                value=7,
                minimum=1,
                maximum=20,
                step=0.1,
                visible=False
            )
            width_slider = gr.Slider(
                value=1024,
                minimum=256,
                maximum=1536,
                step=8,
                visible=False
            )
            height_slider = gr.Slider(
                value=1024,
                minimum=256,
                maximum=1536,
                step=8,
                visible=False
            )
            num_samples = gr.Slider(
                value=1,
                minimum=1,
                maximum=1,
                step=1,
                visible=False
            )

        with gr.Row():
            result_image = gr.Image(
                label="Imazhi i Gjeneruar",
                interactive=False
            )

        # Update hidden sliders based on aspect ratio
        aspect_ratio.change(
            fn=update_aspect_ratio,
            inputs=[aspect_ratio],
            outputs=[width_slider, height_slider],
            queue=False
        )

        # Bind the generate button
        inputs = [
            prompt, negative_prompt, num_inference_steps, guidance_scale,
            width_slider, height_slider, num_samples
        ]
        generate_button.click(
            fn=generate,
            inputs=inputs,
            outputs=[result_image],
            show_progress="full"
        )

    return demo

if __name__ == "__main__":
    print(f"Gradio version: {gr.__version__}")
    app = create_demo()
    app.queue(max_size=12).launch(server_name='0.0.0.0')