Create app.py

app.py

@@ -0,0 +1,163 @@
+import gradio as gr
+import os
+import random
+import requests
+from PIL import Image
+from io import BytesIO
+from gradio_client import Client, handle_file
+
+# Constants
+MAX_SEED = 2**32 - 1
+MAX_IMAGE_SIZE = 1024
+
+def get_random_api_key():
+import gradio as gr
+import os
+import random
+from gradio_client import Client, handle_file
+from PIL import Image
+import tempfile
+
+# Constants
+MAX_SEED = 2**32 - 1
+MAX_IMAGE_SIZE = 1024
+
+def get_random_api_key():
+    keys = os.getenv("KEYS", "").split(",")
+    return random.choice(keys).strip()
+
+def resize_img(image, max_size=1024):
+    width, height = image.size
+    scaling_factor = min(max_size / width, max_size / height)
+    new_width = int(width * scaling_factor)
+    new_height = int(height * scaling_factor)
+    return image.resize((new_width, new_height), Image.LANCZOS)
+
+def process_image(
+    image,
+    prompt,
+    scale,
+    seed,
+    randomize_seed,
+    width,
+    height,
+):
+    api_key = get_random_api_key()
+    client = Client("InstantX/SD35-IP-Adapter", hf_token=api_key)
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    if image is None:
+        return None, seed
+
+    # Convert to PIL Image if needed
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+
+    # Resize image
+    resized_image = resize_img(image)
+
+    # Save the resized image to a temporary file
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
+        resized_image.save(temp_file.name)
+        image_path = temp_file.name
+
+    try:
+        result = client.predict(
+            image=handle_file(image_path),
+            prompt=prompt,
+            scale=scale,
+            seed=seed,
+            randomize_seed=randomize_seed,
+            width=width,
+            height=height,
+            api_name="/process_image"
+        )
+        generated_image_url = result[0]['url']
+    finally:
+        # Удаляем временный файл после использования
+        os.remove(image_path)
+
+    return generated_image_url, result[1]
+
+# UI CSS
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# InstantX's SD3.5 IP Adapter")
+        
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(
+                    label="Input Image",
+                    type="pil"
+                )
+                scale = gr.Slider(
+                    label="Image Scale",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.7,
+                )
+                prompt = gr.Text(
+                    label="Prompt",
+                    max_lines=1,
+                    placeholder="Введите ваш запрос",
+                )
+                run_button = gr.Button("Сгенерировать", variant="primary")
+            
+            with gr.Column():
+                result = gr.Image(label="Результат")
+        
+        with gr.Accordion("Дополнительные настройки", open=False):
+            seed = gr.Slider(
+                label="Сид",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Случайный сид", value=True)
+            
+            with gr.Row():
+                width = gr.Slider(
+                    label="Ширина",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="Высота",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+    
+    run_button.click(
+        fn=process_image,
+        inputs=[
+            input_image,
+            prompt,
+            scale,
+            seed,
+            randomize_seed,
+            width,
+            height,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()