Update app.py

app.py

@@ -1,154 +1,76 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+from huggingface_hub import InferenceClient
+from PIL import Image
+import io
+import os
+
+API_KEY = os.getenv("HF_API_TOKEN")
+MODEL_NAME = "xey/sldr_flux_nsfw_v2-studio"
+
+client = InferenceClient(api_key=API_KEY)
+
+def generate_image(prompt):
+    """Generate an image from a text prompt using Hugging Face API."""
+    try:
+        image = client.text_to_image(prompt, model=MODEL_NAME)
+        img_byte_array = io.BytesIO()
+        image.save(img_byte_array, format="PNG")
+        img_byte_array.seek(0)
+        return image
+    except Exception as e:
+        return f"Error generating image: {e}"
 
 css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+body {
+    background: linear-gradient(135deg, #121212, #1e1e2e);
+    color: white;
+    font-family: 'Poppins', sans-serif;
+    text-align: center;
+}
+.gradio-container {
+    background: rgba(30, 30, 46, 0.9);
+    padding: 25px;
+    border-radius: 12px;
+    box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.3);
+    max-width: 600px;
+    margin: auto;
+}
+button {
+    background: linear-gradient(135deg, #ff416c, #ff4b4b);
+    color: white;
+    border: none;
+    border-radius: 10px;
+    font-size: 18px;
+    padding: 12px;
+    transition: 0.3s;
+}
+button:hover {
+    background: linear-gradient(135deg, #ff4b4b, #ff416c);
+    transform: scale(1.05);
+}
+h1, h2, h3, p, label {
+    color: white !important;
+}
+input, textarea {
+    background: #252537;
+    color: white;
+    border: 1px solid #444;
+    padding: 10px;
+    border-radius: 8px;
 }
 """
 
 with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+    title="🚀 Agents Valley AI Image Generator",
+    gr.Markdown(
+        "<h2 style='color: white; font-size: 35px; font-weight: 600; margin-bottom: 100px;'>AI image Generator powered by Agents Valley 🤖</h2>"
+    )
+    gr.Markdown(
+        "<h2 style='color: white; font-size: 15px; font-weight: 600; margin-bottom: 100px;'>Sldr_Flux_Nsfw_V2-Studio</h2>"
     )
+    prompt_input = gr.Textbox(label="Enter your prompt", value="A robot on a stallion.")
+    output_image = gr.Image(label="Generated Image by Agents Valley")
+    generate_button = gr.Button("Generate Image")
+    generate_button.click(fn=generate_image, inputs=prompt_input, outputs=output_image)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()