Update app.py

app.py

@@ -1,154 +1,97 @@
 import gradio as gr
-import numpy as np
+import requests
+import io
+from PIL import Image
 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",
-]
-
-css = """
+# التنسيق البصري الفاخر التابع لـ 7.com.ly (True Black & Cyber Neon)
+custom_css = """
+body, .gradio-container {
+    background-color: #000000 !important;
+    color: #ffffff !important;
+    font-family: 'Inter', sans-serif;
+}
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 800px;
+    background: rgba(10, 10, 10, 0.9) !important;
+    border: 1px solid #bf00ff !important;
+    box-shadow: 0 0 20px rgba(191, 0, 255, 0.15) !important;
+    border-radius: 12px !important;
+    padding: 30px !important;
+}
+button.primary {
+    background: linear-gradient(135deg, #00f3ff, #bf00ff) !important;
+    color: #000000 !important;
+    font-weight: bold !important;
+    border: none !important;
+}
+button.primary:hover {
+    box-shadow: 0 0 25px rgba(0, 243, 255, 0.5) !important;
+}
+input, textarea {
+    background-color: #050505 !important;
+    color: #ffffff !important;
+    border: 1px solid #00f3ff !important;
 }
 """
 
-with gr.Blocks(css=css) as demo:
+def generate_byok(prompt, negative_prompt, seed, randomize_seed, width, height, hf_token):
+    if not hf_token:
+        return None, "برجاء إدخال مفتاح الهوية اللحظي (HF Token) لتفعيل المعالجة الفورية."
+    
+    if randomize_seed:
+        seed = random.randint(0, 2147483647)
+        
+    # استخدام محرك FLUX.1-schnell فائق السرعة عبر الـ API لزمن استجابة يقارب الصفر
+    API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+    headers = {"Authorization": f"Bearer {hf_token}"}
+    
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "negative_prompt": negative_prompt,
+            "width": width,
+            "height": height,
+            "seed": seed
+        }
+    }
+    
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload, timeout=30)
+        if response.status_code == 200:
+            return Image.open(io.BytesIO(response.content))
+        else:
+            return None
+    except Exception:
+        return None
+
+with gr.Blocks(css=custom_css, theme=gr.themes.Base()) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown("<h1 style='text-align: center; color: #00f3ff; margin-bottom: 0;'>7.com.ly</h1>")
+        gr.Markdown("<p style='text-align: center; color: #bf00ff; margin-top: 0;'>Sovereign Sovereign Image Engine (BYOK)</p>")
+        
         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)
-
+            prompt = gr.Text(label="الوصف البصري (Prompt)", placeholder="أدخل تفاصيل الصورة المطلوبة هنا...", scale=4)
+            run_button = gr.Button("توليد فوري", scale=1, variant="primary")
+            
+        result = gr.Image(label="النتيجة السيادية", show_label=False)
+        
+        with gr.Accordion("إعدادات المحرك المتقدمة", open=False):
+            hf_token = gr.Text(label="مفتاح العميل اللحظي (Hugging Face Token)", placeholder="hf_...", type="password")
+            negative_prompt = gr.Text(label="العناصر المستبعدة (Negative prompt)", placeholder="أشياء لا تريدها في الصورة")
+            seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=0)
+            randomize_seed = gr.Checkbox(label="عشوائية الـ 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
-                )
+                width = gr.Slider(label="العرض (Width)", minimum=256, maximum=1024, step=32, value=1024)
+                height = gr.Slider(label="الارتفاع (Height)", minimum=256, maximum=1024, step=32, value=1024)
 
-        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],
+    run_button.click(
+        fn=generate_byok,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, hf_token],
+        outputs=[result]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()