Update app.py

app.py

@@ -1,319 +1,30 @@
-import gradio as gr
-from demo import automask_image_app, automask_video_app, sahi_autoseg_app
-
-
-
-def image_app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                seg_automask_image_file = gr.Image(type="filepath").style(height=260)
-                with gr.Row():
-                    with gr.Column():
-                        seg_automask_image_model_type = gr.Dropdown(
-                            choices=[
-                                "vit_h",
-                                "vit_l",
-                                "vit_b",
-                            ],
-                            value="vit_l",
-                            label="Model Type",
-                        )
-
-                        seg_automask_image_min_area = gr.Number(
-                            value=0,
-                            label="Min Area",
-                        )
-                    with gr.Row():
-                        with gr.Column():
-                            seg_automask_image_points_per_side = gr.Slider(
-                                minimum=0,
-                                maximum=32,
-                                step=2,
-                                value=16,
-                                label="Points per Side",
-                            )
-
-                            seg_automask_image_points_per_batch = gr.Slider(
-                                minimum=0,
-                                maximum=64,
-                                step=2,
-                                value=32,
-                                label="Points per Batch",
-                            )
-
-                seg_automask_image_predict = gr.Button(value="Generator")
-
-            with gr.Column():
-                output_image = gr.Image()
-
-        seg_automask_image_predict.click(
-            fn=automask_image_app,
-            inputs=[
-                seg_automask_image_file,
-                seg_automask_image_model_type,
-                seg_automask_image_points_per_side,
-                seg_automask_image_points_per_batch,
-                seg_automask_image_min_area,
-            ],
-            outputs=[output_image],
-        )
-        
-        gr.Examples(
-            examples=[
-                [
-                    "testv3.jpeg",
-                    "vit_l",
-                    16,
-                    32,
-                    0,
-                ],
-            
-            ],
-            fn=automask_image_app,
-            inputs=[
-                seg_automask_image_file,
-                seg_automask_image_model_type,
-                seg_automask_image_points_per_side,
-                seg_automask_image_points_per_batch,
-                seg_automask_image_min_area,
-            ],  
-            outputs=[output_image],
-            cache_examples=True,
-        )
-
-
-def video_app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                seg_automask_video_file = gr.Video().style(height=260)
-                with gr.Row():
-                    with gr.Column():
-                        seg_automask_video_model_type = gr.Dropdown(
-                            choices=[
-                                "vit_h",
-                                "vit_l",
-                                "vit_b",
-                            ],
-                            value="vit_l",
-                            label="Model Type",
-                        )
-                        seg_automask_video_min_area = gr.Number(
-                            value=1000,
-                            label="Min Area",
-                        )
-
-                    with gr.Row():
-                        with gr.Column():
-                            seg_automask_video_points_per_side = gr.Slider(
-                                minimum=0,
-                                maximum=32,
-                                step=2,
-                                value=16,
-                                label="Points per Side",
-                            )
-
-                            seg_automask_video_points_per_batch = gr.Slider(
-                                minimum=0,
-                                maximum=64,
-                                step=2,
-                                value=32,
-                                label="Points per Batch",
-                            )
-
-                seg_automask_video_predict = gr.Button(value="Generator")
-            with gr.Column():
-                output_video = gr.Video()
+from whisperplus.app import youtube_url_to_text_app, speaker_diarization_app
 
-        seg_automask_video_predict.click(
-            fn=automask_video_app,
-            inputs=[
-                seg_automask_video_file,
-                seg_automask_video_model_type,
-                seg_automask_video_points_per_side,
-                seg_automask_video_points_per_batch,
-                seg_automask_video_min_area,
-            ],
-            outputs=[output_video],
-        )
-
-        gr.Examples(
-            examples=[
-                [
-                    "testv2.mp4",
-                    "vit_l",
-                    16,
-                    32,
-                    0,
-                ],  
-            ],
-            fn=automask_video_app,
-            inputs=[
-                seg_automask_video_file,
-                seg_automask_video_model_type,
-                seg_automask_video_points_per_side,
-                seg_automask_video_points_per_batch,
-                seg_automask_video_min_area,
-            ],
-            outputs=[output_video],
-            cache_examples=True,
-        )
-
-
-def sahi_app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                sahi_image_file = gr.Image(type="filepath").style(height=260)
-                sahi_autoseg_model_type = gr.Dropdown(
-                    choices=[
-                        "vit_h",
-                        "vit_l",
-                        "vit_b",
-                    ],
-                    value="vit_l",
-                    label="Sam Model Type",
-                )
-
-                with gr.Row():
-                    with gr.Column():
-                        sahi_model_type = gr.Dropdown(
-                            choices=[
-                                "yolov5",
-                                "yolov8",
-                            ],
-                            value="yolov5",
-                            label="Detector Model Type",
-                        )
-                        sahi_image_size = gr.Slider(
-                            minimum=0,
-                            maximum=1280,
-                            step=32,
-                            value=640,
-                            label="Image Size",
-                        )
-
-                        sahi_overlap_width = gr.Slider(
-                            minimum=0,
-                            maximum=1,
-                            step=0.1,
-                            value=0.2,
-                            label="Overlap Width",
-                        )
-
-                        sahi_slice_width = gr.Slider(
-                            minimum=0,
-                            maximum=640,
-                            step=32,
-                            value=256,
-                            label="Slice Width",
-                        )
-
-                    with gr.Row():
-                        with gr.Column():
-                            sahi_model_path = gr.Dropdown(
-                                choices=[
-                                    "yolov5l.pt",
-                                    "yolov5l6.pt",
-                                    "yolov8l.pt",
-                                    "yolov8x.pt",
-                                ],
-                                value="yolov5l6.pt",
-                                label="Detector Model Path",
-                            )
-
-                            sahi_conf_th = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                step=0.1,
-                                value=0.2,
-                                label="Confidence Threshold",
-                            )
-                            sahi_overlap_height = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                step=0.1,
-                                value=0.2,
-                                label="Overlap Height",
-                            )
-                            sahi_slice_height = gr.Slider(
-                                minimum=0,
-                                maximum=640,
-                                step=32,
-                                value=256,
-                                label="Slice Height",
-                            )
-                sahi_image_predict = gr.Button(value="Generator")
-
-            with gr.Column():
-                output_image = gr.Image()
-
-        sahi_image_predict.click(
-            fn=sahi_autoseg_app,
-            inputs=[
-                sahi_image_file,
-                sahi_autoseg_model_type,
-                sahi_model_type,
-                sahi_model_path,
-                sahi_conf_th,
-                sahi_image_size,
-                sahi_slice_height,
-                sahi_slice_width,
-                sahi_overlap_height,
-                sahi_overlap_width,
-            ],
-            outputs=[output_image],
-        )
-        
-        gr.Examples(
-            examples=[
-                [
-                    "testv1.jpg",
-                    "vit_l",
-                    "yolov5",
-                    "yolov5l6.pt",
-                    0.2,
-                    1280,
-                    256,
-                    256,
-                    0.2,
-                    0.2,
-                ],
-            ],
-            fn=sahi_autoseg_app,
-            inputs=[
-                sahi_image_file,
-                sahi_autoseg_model_type,
-                sahi_model_type,
-                sahi_model_path,
-                sahi_conf_th,
-                sahi_image_size,
-                sahi_slice_height,
-                sahi_slice_width,
-                sahi_overlap_height,
-                sahi_overlap_width,
-            ],
-            outputs=[output_image],
-            cache_examples=True,
-        )
-
-
-def metaseg_app():
-    app = gr.Blocks()
-    with app:
-        with gr.Row():
-            with gr.Column():
-                with gr.Tab("Image"):
-                    image_app()
-                with gr.Tab("Video"):
-                    video_app()
-                with gr.Tab("SAHI"):
-                    sahi_app()
+import gradio as gr
 
-    app.queue(concurrency_count=1)
-    app.launch(debug=True, enable_queue=True)
 
 
-if __name__ == "__main__":
-    metaseg_app()
+gradio_app = gr.Blocks()
+with gradio_app:
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    WhisperPlus: Advancing Speech-to-Text Processing 🚀
+    </h1>
+    """)
+    gr.HTML(
+        """
+        <h3 style='text-align: center'>
+        Follow me for more!
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
+        </h3>
+        """)
+    with gr.Row():
+        with gr.Column():
+            with gr.Tab(label="Youtube URL to Text"):
+                youtube_url_to_text_app()
+            with gr.Tab(label="Speaker Diarization"):
+                speaker_diarization_app()
+
+gradio_app.queue()
+gradio_app.launch(debug=True)