Update app.py

app.py

@@ -1,24 +1,122 @@
 import gradio as gr
-from transformers import pipeline
-import time
 
-pipe = pipeline("automatic-speech-recognition")
+# Importing necessary components for the Gradio app
+from app.description import DESCRIPTION_STATIC, DESCRIPTION_DYNAMIC
+from app.authors import AUTHORS
+from app.app_utils import preprocess_image_and_predict, preprocess_video_and_predict
 
-def transcribe(audio, state=""):
-    print(audio)
-    time.sleep(2)
-    text = pipe(audio)["text"]
-    state += text + " "
-    return state, state
 
+def clear_static_info():
+    return (
+        gr.Image(value=None, type="pil"),
+        gr.Image(value=None, scale=1, elem_classes="dl5"),
+        gr.Image(value=None, scale=1, elem_classes="dl2"),
+        gr.Label(value=None, num_top_classes=3, scale=1, elem_classes="dl3"),
+    )
 
-with gr.Blocks() as demo:
-  state = gr.State(value="")
-  with gr.Row():
-      with gr.Column():
-        audio = gr.Audio(source="microphone", type="filepath") 
-      with gr.Column():
-        textbox = gr.Textbox()
-  audio.stream(fn=transcribe, inputs=[audio, state], outputs=[textbox, state])
+def clear_dynamic_info():
+    return (
+        gr.Video(value=None),
+        gr.Video(value=None),
+        gr.Video(value=None),
+        gr.Video(value=None),
+        gr.Plot(value=None),
+    )
 
-demo.launch(debug=True)
+with gr.Blocks(css="app.css") as demo:
+    with gr.Tab("Dynamic App"):
+        gr.Markdown(value=DESCRIPTION_DYNAMIC)
+        with gr.Row():
+            with gr.Column(scale=2):
+                input_video = gr.Video(elem_classes="video1")
+                with gr.Row():
+                    clear_btn_dynamic = gr.Button(
+                        value="Clear", interactive=True, scale=1
+                    )
+                    submit_dynamic = gr.Button(
+                        value="Submit", interactive=True, scale=1, elem_classes="submit"
+                    )
+            with gr.Column(scale=2, elem_classes="dl4"):
+                with gr.Row():
+                    output_video = gr.Video(label="Original video", scale=1, elem_classes="video2")
+                    output_face = gr.Video(label="Pre-processed video", scale=1, elem_classes="video3")
+                    output_heatmaps = gr.Video(label="Heatmaps", scale=1, elem_classes="video4")
+                output_statistics = gr.Plot(label="Statistics of emotions", elem_classes="stat")
+        gr.Examples(
+            ["videos/video1.mp4",
+            "videos/video2.mp4",
+            ],
+            [input_video],
+        )
+
+    with gr.Tab("Static App"):
+        gr.Markdown(value=DESCRIPTION_STATIC)
+        with gr.Row():
+            with gr.Column(scale=2, elem_classes="dl1"):
+                input_image = gr.Image(label="Original image", type="pil")
+                with gr.Row():
+                    clear_btn = gr.Button(
+                        value="Clear", interactive=True, scale=1, elem_classes="clear"
+                    )
+                    submit = gr.Button(
+                        value="Submit", interactive=True, scale=1, elem_classes="submit"
+                    )
+            with gr.Column(scale=1, elem_classes="dl4"):
+                with gr.Row():
+                    output_image = gr.Image(label="Face", scale=1, elem_classes="dl5")
+                    output_heatmap = gr.Image(label="Heatmap", scale=1, elem_classes="dl2")
+                output_label = gr.Label(num_top_classes=3, scale=1, elem_classes="dl3")
+        gr.Examples(
+            [
+                "images/fig7.jpg",
+                "images/fig1.jpg",
+                "images/fig2.jpg",
+                "images/fig3.jpg",
+                "images/fig4.jpg",
+                "images/fig5.jpg",
+                "images/fig6.jpg",
+            ],
+            [input_image],
+        )
+    with gr.Tab("Authors"):
+        gr.Markdown(value=AUTHORS)
+
+    submit.click(
+        fn=preprocess_image_and_predict,
+        inputs=[input_image],
+        outputs=[output_image, output_heatmap, output_label],
+        queue=True,
+    )
+    clear_btn.click(
+        fn=clear_static_info,
+        inputs=[],
+        outputs=[input_image, output_image, output_heatmap, output_label],
+        queue=True,
+    )
+
+    submit_dynamic.click(
+        fn=preprocess_video_and_predict,
+        inputs=input_video,
+        outputs=[
+            output_video,
+            output_face,
+            output_heatmaps, 
+            output_statistics
+        ],
+        queue=True,
+    )
+    clear_btn_dynamic.click(
+        fn=clear_dynamic_info,
+        inputs=[],
+        outputs=[
+            input_video,
+            output_video,
+            output_face,
+            output_heatmaps, 
+            output_statistics
+        ],
+        queue=True,
+    )
+
+if __name__ == "__main__":
+    demo.queue(api_open=False).launch(share=False)