Update

app_caption.py

@@ -9,37 +9,39 @@ import gradio as gr
 from prismer_model import Model
 
 
-def create_demo():
+def create_demo() -> gr.Blocks:
     model = Model()
     model.mode = 'caption'
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label='Input', type='filepath')
-            model_name = gr.Dropdown(label='Model', choices=['Prismer-Base', 'Prismer-Large'], value='Prismer-Base')
-            run_button = gr.Button('Run')
-        with gr.Column(scale=1.5):
-            caption = gr.Text(label='Model Prediction')
-            with gr.Row():
-                depth = gr.Image(label='Depth')
-                edge = gr.Image(label='Edge')
-                normals = gr.Image(label='Normals')
-            with gr.Row():
-                segmentation = gr.Image(label='Segmentation')
-                object_detection = gr.Image(label='Object Detection')
-                ocr = gr.Image(label='OCR Detection')
-
-    inputs = [image, model_name]
-    outputs = [caption, depth, edge, normals, segmentation, object_detection, ocr]
-
-    paths = sorted(pathlib.Path('prismer/images').glob('*'))
-    examples = [[path.as_posix(), 'Prismer-Base'] for path in paths]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=outputs,
-                fn=model.run_caption,
-                cache_examples=os.getenv('SYSTEM') == 'spaces')
-
-    run_button.click(fn=model.run_caption, inputs=inputs, outputs=outputs)
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(label='Input', type='filepath')
+                model_name = gr.Dropdown(label='Model', choices=['Prismer-Base', 'Prismer-Large'], value='Prismer-Base')
+                run_button = gr.Button('Run')
+            with gr.Column(scale=1.5):
+                caption = gr.Text(label='Model Prediction')
+                with gr.Row():
+                    depth = gr.Image(label='Depth')
+                    edge = gr.Image(label='Edge')
+                    normals = gr.Image(label='Normals')
+                with gr.Row():
+                    segmentation = gr.Image(label='Segmentation')
+                    object_detection = gr.Image(label='Object Detection')
+                    ocr = gr.Image(label='OCR Detection')
+
+        inputs = [image, model_name]
+        outputs = [caption, depth, edge, normals, segmentation, object_detection, ocr]
+
+        paths = sorted(pathlib.Path('prismer/images').glob('*'))
+        examples = [[path.as_posix(), 'Prismer-Base'] for path in paths]
+        gr.Examples(examples=examples,
+                    inputs=inputs,
+                    outputs=outputs,
+                    fn=model.run_caption,
+                    cache_examples=os.getenv('SYSTEM') == 'spaces')
+
+        run_button.click(fn=model.run_caption, inputs=inputs, outputs=outputs)
+    return demo
 
 
 if __name__ == '__main__':

app_vqa.py

@@ -9,42 +9,44 @@ import gradio as gr
 from prismer_model import Model
 
 
-def create_demo():
+def create_demo() -> gr.Blocks:
     model = Model()
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(label='Input', type='filepath')
-            model_name = gr.Dropdown(label='Model', choices=['Prismer-Base', 'Prismer-Large'], value='Prismer-Base')
-            question = gr.Text(label='Question')
-            run_button = gr.Button('Run')
-        with gr.Column(scale=1.5):
-            answer = gr.Text(label='Model Prediction')
-            with gr.Row():
-                depth = gr.Image(label='Depth')
-                edge = gr.Image(label='Edge')
-                normals = gr.Image(label='Normals')
-            with gr.Row():
-                segmentation = gr.Image(label='Segmentation')
-                object_detection = gr.Image(label='Object Detection')
-                ocr = gr.Image(label='OCR Detection')
-
-    inputs = [image, model_name, question]
-    outputs = [answer, depth, edge, normals, segmentation, object_detection, ocr]
-
-    paths = sorted(pathlib.Path('prismer/images').glob('*'))
-    ex_questions = ['What is the man on the left doing?',
-                    'What is this person doing?',
-                    'How many cows in this image?',
-                    'What is the type of animal in this image?',
-                    'What toy is it?']
-    examples = [[path.as_posix(), 'Prismer-Base', ex_questions[i]] for i, path in enumerate(paths)]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=outputs,
-                fn=model.run_vqa,
-                cache_examples=os.getenv('SYSTEM') == 'spaces')
-
-    run_button.click(fn=model.run_vqa, inputs=inputs, outputs=outputs)
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(label='Input', type='filepath')
+                model_name = gr.Dropdown(label='Model', choices=['Prismer-Base', 'Prismer-Large'], value='Prismer-Base')
+                question = gr.Text(label='Question')
+                run_button = gr.Button('Run')
+            with gr.Column(scale=1.5):
+                answer = gr.Text(label='Model Prediction')
+                with gr.Row():
+                    depth = gr.Image(label='Depth')
+                    edge = gr.Image(label='Edge')
+                    normals = gr.Image(label='Normals')
+                with gr.Row():
+                    segmentation = gr.Image(label='Segmentation')
+                    object_detection = gr.Image(label='Object Detection')
+                    ocr = gr.Image(label='OCR Detection')
+
+        inputs = [image, model_name, question]
+        outputs = [answer, depth, edge, normals, segmentation, object_detection, ocr]
+
+        paths = sorted(pathlib.Path('prismer/images').glob('*'))
+        ex_questions = ['What is the man on the left doing?',
+                        'What is this person doing?',
+                        'How many cows in this image?',
+                        'What is the type of animal in this image?',
+                        'What toy is it?']
+        examples = [[path.as_posix(), 'Prismer-Base', ex_questions[i]] for i, path in enumerate(paths)]
+        gr.Examples(examples=examples,
+                    inputs=inputs,
+                    outputs=outputs,
+                    fn=model.run_vqa,
+                    cache_examples=os.getenv('SYSTEM') == 'spaces')
+
+        run_button.click(fn=model.run_vqa, inputs=inputs, outputs=outputs)
+    return demo
 
 
 if __name__ == '__main__':