initial commit

app.py

@@ -1,7 +1,162 @@
+"""
+File: app.py
+Author: Elena Ryumina and Dmitry Ryumin
+Description: Description: Main application file for Facial_Expression_Recognition.
+             The file defines the Gradio interface, sets up the main blocks,
+             and includes event handlers for various components.
+License: MIT License
+"""
+
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 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 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"),
+    )
+
+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),
+    )
+
+with gr.Blocks(css="app.css") as demo:
+    # Header with Observatory logo
+    with gr.Row(elem_classes="header-container"):
+        with gr.Column():
+            gr.Image("images/LMLOBS.png", show_label=False, container=False, elem_classes="header-logo")
+    
+    gr.Markdown("# Facial Emotion Expression Demo")
+    gr.Markdown("### LittleMonkeyLab | Goldsmiths Observatory")
+    
+    with gr.Tab("Dynamic App"):
+        with gr.Row():
+            with gr.Column(scale=2):
+                input_video = gr.Video(elem_classes="video1")
+                gr.Markdown("""
+                ### How to use:
+                1. Upload a video or use one of the examples below
+                2. Click 'Submit' to analyze facial expressions
+                3. Watch the processed video and emotion statistics
+                """)
+                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", visible=False)
+                    output_heatmaps = gr.Video(label="Heatmaps", scale=1, elem_classes="video4", visible=False)
+                output_statistics = gr.Plot(label="Statistics of emotions", elem_classes="stat")
+                emotion_averages = gr.Dataframe(
+                    headers=["Emotion", "Average Intensity", "Peak Value"],
+                    label="Emotion Analysis Summary",
+                    elem_classes="emotion-summary"
+                )
+                gr.Markdown("""
+                ### Understanding the Results:
+                - The graph shows emotion intensities over time
+                - Higher peaks indicate stronger emotional expressions
+                - Multiple emotions can be present simultaneously
+                - Common patterns:
+                  * Happiness: Upturned mouth, raised cheeks
+                  * Sadness: Downturned mouth, raised inner eyebrows
+                  * Anger: Lowered eyebrows, tightened lips
+                  * Surprise: Raised eyebrows, widened eyes
+                  * Fear: Widened eyes, raised outer eyebrows
+                  * Disgust: Wrinkled nose, raised upper lip
+                  * Neutral: Relaxed facial features
+                """)
+        gr.Examples(
+            ["videos/video1.mp4",
+            "videos/video2.mp4",
+            ],
+            [input_video],
+        )
+
+    with gr.Tab("Static App"):
+        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],
+        )
+
+    # Footer with LittleMonkeyLab logo
+    with gr.Row(elem_classes="center-content"):
+        with gr.Column():
+            gr.Image("images/LMLLOGO.png", show_label=False, container=False, elem_classes="footer-logo")
+            gr.Markdown("© LittleMonkeyLab | Goldsmiths Observatory", elem_classes="footer-text")
+
+    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, emotion_averages],
+        queue=True,
+    )
+    clear_btn_dynamic.click(
+        fn=clear_dynamic_info,
+        inputs=[],
+        outputs=[
+            input_video,
+            output_video,
+            output_face,
+            output_heatmaps,
+            output_statistics,
+            emotion_averages
+        ],
+        queue=True,
+    )
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+if __name__ == "__main__":
+    demo.queue(api_open=False).launch(share=False)