updated demos

demo/gpt_j/run.py

@@ -8,9 +8,12 @@ examples = [
     ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
 ]
 
-gr.Interface.load(
+demo = gr.Interface.load(
     "huggingface/EleutherAI/gpt-j-6B",
-    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    inputs=gr.Textbox(lines=5, label="Input Text"),
     title=title,
     examples=examples,
-).launch()
+)
+
+if __name__ == "__main__":
+    demo.launch()

demo/hello_login/run.py

@@ -7,6 +7,6 @@ def greet(name):
     return "Hello " + name + "!!"
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 if __name__ == "__main__":
-    iface.launch(auth=lambda u, p: user_db.get(u) == p)
+    demo.launch(auth=lambda u, p: user_db.get(u) == p)

demo/hello_world/run.py

@@ -5,6 +5,7 @@ def greet(name):
     return "Hello " + name + "!!"
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/hello_world_2/run.py

@@ -5,10 +5,11 @@ def greet(name):
     return "Hello " + name + "!"
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn=greet,
-    inputs=gr.inputs.Textbox(lines=2, placeholder="Name Here..."),
+    inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
     outputs="text",
 )
+
 if __name__ == "__main__":
-    app, local_url, share_url = iface.launch()
+    app, local_url, share_url = demo.launch()

demo/hello_world_3/run.py

@@ -8,10 +8,10 @@ def greet(name, is_morning, temperature):
     return greeting, round(celsius, 2)
 
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn=greet,
-    inputs=["text", "checkbox", gr.inputs.Slider(0, 100)],
+    inputs=["text", "checkbox", gr.Slider(0, 100)],
     outputs=["text", "number"],
 )
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()

demo/image_classifier/run.py

@@ -17,12 +17,15 @@ def classify_image(inp):
     return {labels[i]: float(prediction[i]) for i in range(1000)}
 
 
-image = gr.inputs.Image(shape=(224, 224))
-label = gr.outputs.Label(num_top_classes=3)
+image = gr.Image(shape=(224, 224))
+label = gr.Label(num_top_classes=3)
 
-gr.Interface(
+demo = gr.Interface(
     fn=classify_image,
     inputs=image,
     outputs=label,
     examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
-).launch()
+)
+
+if __name__ == "__main__":
+    demo.launch()

demo/image_classifier_2/run.py

@@ -20,6 +20,10 @@ def predict(inp):
     return {labels[i]: float(prediction[i]) for i in range(1000)}
 
 
-inputs = gr.inputs.Image()
-outputs = gr.outputs.Label(num_top_classes=3)
-gr.Interface(fn=predict, inputs=inputs, outputs=outputs).launch()
+inputs = gr.Image()
+outputs = gr.Label(num_top_classes=3)
+
+demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)
+
+if __name__ == "__main__":
+    demo.launch()

demo/image_classifier_interpretation/run.py

@@ -17,9 +17,11 @@ def classify_image(inp):
     return {labels[i]: float(prediction[i]) for i in range(1000)}
 
 
-image = gr.inputs.Image(shape=(224, 224))
-label = gr.outputs.Label(num_top_classes=3)
+image = gr.Image(shape=(224, 224))
+label = gr.Label(num_top_classes=3)
 
-gr.Interface(
-    fn=classify_image, inputs=image, outputs=label, interpretation="default"
-).launch()
+demo = gr.Interface(fn=classify_image, inputs=image, outputs=label, 
+                    interpretation="default")
+
+if __name__ == "__main__":
+    demo.launch()

demo/image_mod/run.py

@@ -5,6 +5,7 @@ def image_mod(image):
     return image.rotate(45)
 
 
-iface = gr.Interface(image_mod, gr.inputs.Image(type="pil"), "image")
+demo = gr.Interface(image_mod, gr.inputs.Image(type="pil"), "image")
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()