Add multiple class functionality

app.py

@@ -1,50 +1,7 @@
 import gradio as gr
-from xdog import to_sketch
-from model import Generator, ResNeXtBottleneck
-import torch
-from data_utils import *
-import glob
-gen = torch.load('model/model.pth')
 
-def convert_to_lineart(img, sigma, k, gamma, epsilon, phi, area_min):
-    phi = 10 * phi
-    out = to_sketch(img, sigma=sigma, k=k, gamma=gamma, epsilon=epsilon, phi=phi, area_min=area_min)
-    return out
+def greet(name):
+    return "Hello " + name + "!!"
 
-def inference(sk):
-    return predict_img(gen, sk, hnt = None)
-
-title = "To Line Art"
-description = "Line art colorization showcase. "
-article = "Github Repo"
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(type="pil", value='examples/Genshin-Impact-anime.jpg')
-            to_lineart_button = gr.Button("To Lineart")
-
-            gr.Examples(
-                examples=glob.glob('examples/*.jpg'),
-                inputs=image,
-                outputs=image,
-                fn=None,
-                cache_examples=False,
-            )
-
-        with gr.Column():
-            sigma = gr.Slider(0.1, 0.5, value=0.3, step=0.1, label='σ')
-            k = gr.Slider(1.0, 8.0, value=4.5, step=0.5, label='k')
-            gamma = gr.Slider(0.05, 1.0, value=0.95, step=0.05, label='γ')
-            epsilon = gr.Slider(-2, 2, value=-1, step=0.5, label='ε')
-            phi = gr.Slider(10, 20, label = 'φ', value=15)
-            min_area = gr.Slider(1, 5, value=2, step=1, label='Minimal Area')
-        
-        with gr.Column():
-            lineart = gr.Image(type="pil", image_mode='L')
-            inpaint_button = gr.Button("Inpaint")
-
-    to_lineart_button.click(convert_to_lineart, inputs=[image, sigma, k, gamma, epsilon, phi, min_area], outputs=lineart)
-    inpaint_button.click(inference, inputs=lineart, outputs=lineart)
-
-demo.launch()
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

app2.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from xdog import to_sketch
+from model import Generator, ResNeXtBottleneck
+import torch
+from data_utils import *
+import glob
+gen = torch.load('model/model.pth')
+
+def convert_to_lineart(img, sigma, k, gamma, epsilon, phi, area_min):
+    phi = 10 * phi
+    out = to_sketch(img, sigma=sigma, k=k, gamma=gamma, epsilon=epsilon, phi=phi, area_min=area_min)
+    return out
+
+def inference(sk):
+    return predict_img(gen, sk, hnt = None)
+
+title = "To Line Art"
+description = "Line art colorization showcase. "
+article = "Github Repo"
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(type="pil", value='examples/Genshin-Impact-anime.jpg')
+            to_lineart_button = gr.Button("To Lineart")
+
+            gr.Examples(
+                examples=glob.glob('examples/*.jpg'),
+                inputs=image,
+                outputs=image,
+                fn=None,
+                cache_examples=False,
+            )
+
+        with gr.Column():
+            sigma = gr.Slider(0.1, 0.5, value=0.3, step=0.1, label='σ')
+            k = gr.Slider(1.0, 8.0, value=4.5, step=0.5, label='k')
+            gamma = gr.Slider(0.05, 1.0, value=0.95, step=0.05, label='γ')
+            epsilon = gr.Slider(-2, 2, value=-1, step=0.5, label='ε')
+            phi = gr.Slider(10, 20, label = 'φ', value=15)
+            min_area = gr.Slider(1, 5, value=2, step=1, label='Minimal Area')
+        
+        with gr.Column():
+            lineart = gr.Image(type="pil", image_mode='L')
+            inpaint_button = gr.Button("Inpaint")
+
+    to_lineart_button.click(convert_to_lineart, inputs=[image, sigma, k, gamma, epsilon, phi, min_area], outputs=lineart)
+    inpaint_button.click(inference, inputs=lineart, outputs=lineart)
+
+demo.launch()