indentation

gradio_app.py

@@ -281,93 +281,93 @@ def create_gradio_interface():
     try:
         # Create a unified interface that can handle both images and videos
         with gr.Blocks(title="👁️ PupilSense 👁️🕵️‍♂️") as demo:
-        gr.Markdown("# 👁️ PupilSense - Pupil Diameter Analysis")
-        gr.Markdown("Upload an image or video to estimate pupil diameter using deep learning models.")
-
-        with gr.Tab("Image Processing"):
-            with gr.Row():
-                with gr.Column():
-                    image_input = gr.Image(type="pil", label="Upload Image")
-                    image_pupil_selection = gr.Dropdown(
-                        ["left_pupil", "right_pupil", "both"],
-                        value="both",
-                        label="Pupil Selection"
-                    )
-                    image_model = gr.Dropdown(
-                        ["ResNet18", "ResNet50"],
-                        value="ResNet18",
-                        label="Model"
-                    )
-                    image_blink_detection = gr.Checkbox(value=True, label="Detect Blinks")
-                    image_submit = gr.Button("Process Image", variant="primary")
-
-                with gr.Column():
-                    image_output = gr.Image(label="Results")
-                    image_text_output = gr.Textbox(label="Pupil Diameter Results", lines=5)
-
-            image_submit.click(
-                fn=process_image_simple,
-                inputs=[image_input, image_pupil_selection, image_model, image_blink_detection],
-                outputs=[image_output, image_text_output]
-            )
-
-        with gr.Tab("Video Processing"):
-            with gr.Row():
-                with gr.Column():
-                    video_input = gr.Video(label="Upload Video")
-                    video_pupil_selection = gr.Dropdown(
-                        ["left_pupil", "right_pupil", "both"],
-                        value="both",
-                        label="Pupil Selection"
-                    )
-                    video_model = gr.Dropdown(
-                        ["ResNet18", "ResNet50"],
-                        value="ResNet18",
-                        label="Model"
-                    )
-                    video_blink_detection = gr.Checkbox(value=True, label="Detect Blinks")
-                    video_submit = gr.Button("Process Video", variant="primary")
-
-                with gr.Column():
-                    video_output = gr.Image(label="Diameter Analysis")
-                    video_text_output = gr.Textbox(label="Summary Statistics", lines=10)
-
-            video_submit.click(
-                fn=process_video_simple,
-                inputs=[video_input, video_pupil_selection, video_model, video_blink_detection],
-                outputs=[video_output, video_text_output]
-            )
-
-        # Add a unified API endpoint that can handle both images and videos
-        with gr.Tab("API Testing"):
-            gr.Markdown("### API Endpoint for External Access")
-            gr.Markdown("This endpoint can process both images and videos programmatically.")
-
-            with gr.Row():
-                with gr.Column():
-                    api_media_input = gr.File(label="Upload Image or Video File")
-                    api_pupil_selection = gr.Dropdown(
-                        ["left_pupil", "right_pupil", "both"],
-                        value="both",
-                        label="Pupil Selection"
-                    )
-                    api_model = gr.Dropdown(
-                        ["ResNet18", "ResNet50"],
-                        value="ResNet18",
-                        label="Model"
-                    )
-                    api_blink_detection = gr.Checkbox(value=True, label="Detect Blinks")
-                    api_submit = gr.Button("Process Media", variant="primary")
-
-                with gr.Column():
-                    api_output = gr.Image(label="Results")
-                    api_text_output = gr.Textbox(label="Analysis Results", lines=10)
-
-            api_submit.click(
-                fn=process_media_unified,
-                inputs=[api_media_input, api_pupil_selection, api_model, api_blink_detection],
-                outputs=[api_output, api_text_output]
-            )
+            gr.Markdown("# 👁️ PupilSense - Pupil Diameter Analysis")
+            gr.Markdown("Upload an image or video to estimate pupil diameter using deep learning models.")
+
+            with gr.Tab("Image Processing"):
+                with gr.Row():
+                    with gr.Column():
+                        image_input = gr.Image(type="pil", label="Upload Image")
+                        image_pupil_selection = gr.Dropdown(
+                            ["left_pupil", "right_pupil", "both"],
+                            value="both",
+                            label="Pupil Selection"
+                        )
+                        image_model = gr.Dropdown(
+                            ["ResNet18", "ResNet50"],
+                            value="ResNet18",
+                            label="Model"
+                        )
+                        image_blink_detection = gr.Checkbox(value=True, label="Detect Blinks")
+                        image_submit = gr.Button("Process Image", variant="primary")
+
+                    with gr.Column():
+                        image_output = gr.Image(label="Results")
+                        image_text_output = gr.Textbox(label="Pupil Diameter Results", lines=5)
+
+                image_submit.click(
+                    fn=process_image_simple,
+                    inputs=[image_input, image_pupil_selection, image_model, image_blink_detection],
+                    outputs=[image_output, image_text_output]
+                )
+
+            with gr.Tab("Video Processing"):
+                with gr.Row():
+                    with gr.Column():
+                        video_input = gr.Video(label="Upload Video")
+                        video_pupil_selection = gr.Dropdown(
+                            ["left_pupil", "right_pupil", "both"],
+                            value="both",
+                            label="Pupil Selection"
+                        )
+                        video_model = gr.Dropdown(
+                            ["ResNet18", "ResNet50"],
+                            value="ResNet18",
+                            label="Model"
+                        )
+                        video_blink_detection = gr.Checkbox(value=True, label="Detect Blinks")
+                        video_submit = gr.Button("Process Video", variant="primary")
+
+                    with gr.Column():
+                        video_output = gr.Image(label="Diameter Analysis")
+                        video_text_output = gr.Textbox(label="Summary Statistics", lines=10)
+
+                video_submit.click(
+                    fn=process_video_simple,
+                    inputs=[video_input, video_pupil_selection, video_model, video_blink_detection],
+                    outputs=[video_output, video_text_output]
+                )
+
+            # Add a unified API endpoint that can handle both images and videos
+            with gr.Tab("API Testing"):
+                gr.Markdown("### API Endpoint for External Access")
+                gr.Markdown("This endpoint can process both images and videos programmatically.")
+
+                with gr.Row():
+                    with gr.Column():
+                        api_media_input = gr.File(label="Upload Image or Video File")
+                        api_pupil_selection = gr.Dropdown(
+                            ["left_pupil", "right_pupil", "both"],
+                            value="both",
+                            label="Pupil Selection"
+                        )
+                        api_model = gr.Dropdown(
+                            ["ResNet18", "ResNet50"],
+                            value="ResNet18",
+                            label="Model"
+                        )
+                        api_blink_detection = gr.Checkbox(value=True, label="Detect Blinks")
+                        api_submit = gr.Button("Process Media", variant="primary")
+
+                    with gr.Column():
+                        api_output = gr.Image(label="Results")
+                        api_text_output = gr.Textbox(label="Analysis Results", lines=10)
+
+                api_submit.click(
+                    fn=process_media_unified,
+                    inputs=[api_media_input, api_pupil_selection, api_model, api_blink_detection],
+                    outputs=[api_output, api_text_output]
+                )
 
         print("✅ Gradio interface created successfully")
         return demo