Refactor to use imgkit

.gitignore

@@ -0,0 +1,2 @@
+/venv
+.env

app.py

@@ -1,154 +1,48 @@
 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",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-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],
-    )
+import imgkit
+from PIL import Image
+from io import BytesIO
+
+def html_to_image(html_code, width, height):
+    options = {
+        'format': 'png',
+        'width': str(width),
+        'height': str(height),
+        'encoding': "UTF-8"
+    }
+    image = Image.open(BytesIO(imgkit.from_string(html_code, False, options=options)))
+    return image
+
+interface = gr.Interface(
+    fn=html_to_image,
+    inputs=[
+        gr.Code(
+            label="HTML Code",
+            language="html",
+            lines=30,
+        ),
+        gr.Number(
+            label="Width",
+            value=1280,
+            step=10,
+            info="Width in pixels (100-2000)"
+        ),
+        gr.Number(
+            label="Height",
+            value=720,
+            step=10,
+            info="Height in pixels (100-2000)"
+        )
+    ],
+    outputs=gr.Image(type="pil", label="Generated Image"),
+    title="HTML to Image Converter",
+    description="Enter HTML code and set dimensions to generate an image",
+    examples=[
+        ["<div style='background: linear-gradient(45deg, #ff6b6b, #4ecdc4); padding: 20px; border-radius: 10px;'><h1 style='color: white; font-family: Arial;'>Hello, World!</h1></div>", 800, 400],
+        ["<div style='background: #f0f0f0; padding: 20px;'><ul style='color: #333;'><li>Item 1</li><li>Item 2</li><li>Item 3</li></ul></div>", 600, 300]
+    ],
+    theme=gr.themes.Soft()
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()

packages.txt

@@ -0,0 +1 @@
+wkhtmltopdf

requirements.txt

@@ -1,6 +1,2 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+imgkit
+gradio