Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from diffusers import DiffusionPipeline
+from PIL import Image
+import numpy as np
+
+pipeline = DiffusionPipeline.from_pretrained("Qilex/neoGen2")
+
+def generate_pets(num_to_generate):
+    images = pipeline(num_to_generate)["sample"]
+    return images
+
+def concatenate_imgs(imgs):
+    length = len(imgs)
+    if length == 1:
+        return imgs[0]
+    top = Image.fromarray(np.concatenate([np.array(x) for x in imgs[:2]],axis=1))
+    if length == 2:
+        return top
+    if len(imgs)==3:
+        fake = np.zeros([128,128,3],dtype=np.uint8)
+        fake[:] = 255
+        bottom = Image.fromarray(np.concatenate([imgs[2], fake],axis=1))
+    elif len(imgs)==4:
+        bottom = Image.fromarray(np.concatenate([imgs[2], imgs[3]],axis=1))
+    return Image.fromarray(np.concatenate([top,bottom],axis=0))
+
+def go(num):
+    imgs = generate_pets(num)
+    grid = concatenate_imgs(imgs)
+    print(type(grid))
+    return grid
+
+title = "VirtualPet Dream"
+description = """
+This AI will 'dream' you up a virtual pet.
+\nThis is a denoising diffusion model trained in 48 hours for a hackathon, so the images can be pretty wonky.
+\nImages are 128x128px.
+"""
+article = '''Here's a gallery of some of the better pets:
+<div style="display: flex; justify-content:space-evenly">
+<img src="https://alexlyman.org/external_images/sample_10.png">
+<img src="https://alexlyman.org/external_images/sample_5.png" >
+<img src="https://alexlyman.org/external_images/sample_4.png" >
+<img src="https://alexlyman.org/external_images/sample_8.png" >
+</div>'''
+gr.Interface(
+    fn=go,
+    inputs= gr.Slider(1, 4, value = 2, step = 1, label="Number of images to generate (more takes longer)"),
+    outputs=gr.Image(),
+    title=title,
+    description=description,
+    article = article,
+).launch()