Update app.py

app.py

@@ -1,7 +1,91 @@
+import os
 import gradio as gr
+import torch
+import yaml
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+from munch import munchify
+import torchvision.transforms as transforms
+from torchvision.transforms import functional as F
+from diffusers import (
+    AutoPipelineForInpainting,
+)
+from generate_dataset import outpainting_generator_rectangle
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+
+transform = transforms.Compose([
+        transforms.ToPILImage(),
+        transforms.Resize((512, 512), interpolation=F.InterpolationMode.LANCZOS),
+    ])
+
+def pref_inpainting(image,
+                    box_width_ratio,
+                    mask_random_start,
+                    steps,
+                    ):
+    with open("/data0/kendong/Diffusions/zero123-live/configs/imagereward_train_configs.yaml") as file:
+        config_dict= yaml.safe_load(file)
+        config = munchify(config_dict)
+
+    pipe = AutoPipelineForInpainting.from_pretrained(
+                '/data1/kendong/joint-rl-diffusion/alignment_log/exp_reward_group_regression_all_1w_1.6boundary/iteration_2560', num_inference_steps=steps)
+    
+    pipe = pipe.to("cuda")
+
+   
+    color, mask = outpainting_generator_rectangle(image, box_width_ratio/100, mask_random_start)
+    mask = mask.convert('L')
+   
+    color, mask = np.array(color).transpose(2, 0, 1), np.array(mask)
+    mask = mask[None, ...]
+    mask_ = np.zeros_like(mask)
+    mask_[mask < 125] = 0
+    mask_[mask >= 125] = 1
+    
+    color = torch.from_numpy(color).to("cuda")
+    mask = torch.from_numpy(mask).to("cuda")
+
+
+    color, mask = transform(color), transform(mask)
+    res = pipe(prompt='', image=color, mask_image=mask, eta=config.eta).images[0]
+    
+    
+    res.save(os.path.join('./',  'test.png'))
+    
+    return color, res
+
+
+inputs = [
+    gr.Image(type="pil", image_mode="RGBA", label='Input Image'), # shape=[512, 512]
+    gr.Slider(30, 45, value=35, step=1, label="box_width_ratio"),
+    gr.Slider(0, 256, value=125, step=1, label="mask_random_start"),
+    gr.Slider(30, 100, value=50, step=5, label="steps"),
+]
+
+outputs = [
+    gr.Image(type="pil", image_mode="RGBA", label='Mask RGB Image', container=True, width="65%"),
+    gr.Image(type="pil", image_mode="RGBA", label='Results', container=True, width="65%"), 
+]
+
+examples = [
+    ["/data0/kendong/Diffusions/zero123-live/test_demo/assets/ILSVRC2012_test_00000003.JPEG", 35, 125, 50],
+    ["/data0/kendong/Diffusions/zero123-live/test_demo/assets/ILSVRC2012_test_00000181.JPEG", 35, 125, 50],
+    ["/data0/kendong/Diffusions/zero123-live/test_demo/assets/ILSVRC2012_test_00002334.JPEG", 35, 125, 50],
+    ["/data0/kendong/Diffusions/zero123-live/test_demo/assets/ILSVRC2012_test_00002613.JPEG", 35, 125, 50],
+]
+
+
+        
+iface = gr.Interface(
+    fn=pref_inpainting,
+    inputs=inputs,
+    outputs=outputs,
+    title="Inpainting with Human Preference (Utilizing Free CPU Resources)",
+    description="Upload an image and start your inpainting (currently only supporting outpainting masks; other mask types coming soon).",
+    theme="default",
+    examples= examples,
+    allow_flagging="never"
+)
+
+
+iface.launch(share=True)