Update app.py

app.py

@@ -206,106 +206,101 @@ def toggle_audio_src(choice):
 
 
 def ui_full(launch_kwargs):
-    with gr.Blocks() as interface:
-        gr.Markdown(
-            """
-            # MusicGen
-            This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Input Text", interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Submit")
-                with gr.Row():
-                    model = gr.Radio(["melody", "medium", "small", "large"],
-                                     label="Model", value="melody", interactive=True)
-                with gr.Row():
-                    duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
-                with gr.Row():
-                    topk = gr.Number(label="Top-k", value=250, interactive=True)
-                    topp = gr.Number(label="Top-p", value=0, interactive=True)
-                    temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
-                    cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
-            with gr.Column():
-                output = [
-                    gr.Audio(label=f"Generated Music {i+1}")
-                    for i in range(len(files))
-                ]
-                output_area = gr.Output(output)
-
-        submit.click(predict_full,
-                     inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef],
-                     outputs=[output_area.value])  # Use value attribute of output_area
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_full,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                    "melody"
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                    "melody"
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                    "medium"
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
-                    "./assets/bach.mp3",
-                    "melody"
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                    "medium",
-                ],
+    interface = gr.Interface(
+        fn=predict_full,
+        inputs=[
+            gr.Radio(["melody", "medium", "small", "large"], label="Model", default="melody"),
+            gr.Text(label="Input Text"),
+            gr.Audio(source="upload", type="numpy", label="File", interactive=True, elem_id="melody-input"),
+            gr.Slider(minimum=1, maximum=120, default=10, label="Duration", step=1),
+            gr.Number(label="Top-k", default=250),
+            gr.Number(label="Top-p", default=0),
+            gr.Number(label="Temperature", default=1.0),
+            gr.Number(label="Classifier Free Guidance", default=3.0),
+        ],
+        outputs=[
+            gr.Audio(label=f"Generated Music {i+1}") for i in range(len(files))
+        ],
+        title="MusicGen",
+        description="This is your private demo for MusicGen, a simple and controllable model for music generation.",
+        examples=[
+            [
+                "An 80s driving pop song with heavy drums and synth pads in the background",
+                "./assets/bach.mp3",
+                "melody"
             ],
-            inputs=[text, melody, model],
-            outputs=[output_area.value],  # Use value attribute of output_area
-        )
-        gr.Markdown(
-            """
-            ### More details
-            The model will generate a short music extract based on the description you provided.
-            The model can generate up to 30 seconds of audio in one pass. It is now possible
-            to extend the generation by feeding back the end of the previous chunk of audio.
-            This can take a long time, and the model might lose consistency. The model might also
-            decide at arbitrary positions that the song ends.
-            **WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
-            An overlap of 12 seconds is kept with the previously generated chunk, and 18 "new" seconds
-            are generated each time.
-            We present 4 model variations:
-            1. Melody -- a music generation model capable of generating music condition
-                on text and melody inputs. **Note**, you can also use text only.
-            2. Small -- a 300M transformer decoder conditioned on text only.
-            3. Medium -- a 1.5B transformer decoder conditioned on text only.
-            4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
-            When using `melody`, ou can optionaly provide a reference audio from
-            which a broad melody will be extracted. The model will then try to follow both
-            the description and melody provided.
-            You can also use your own GPU or a Google Colab by following the instructions on our repo.
-            See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-            for more details.
-            """
-        )
+            [
+                "A cheerful country song with acoustic guitars",
+                "./assets/bolero_ravel.mp3",
+                "melody"
+            ],
+            [
+                "90s rock song with electric guitar and heavy drums",
+                None,
+                "medium"
+            ],
+            [
+                "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
+                "./assets/bach.mp3",
+                "melody"
+            ],
+            [
+                "lofi slow bpm electro chill with organic samples",
+                None,
+                "medium",
+            ],
+        ],
+        allow_flagging=False,
+        layout="vertical",
+        **launch_kwargs
+    )
 
-        interface.queue().launch(**launch_kwargs)
+    interface.launch()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--listen',
+        type=str,
+        default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
+        help='IP to listen on for connections to Gradio',
+    )
+    parser.add_argument(
+        '--username', type=str, default='', help='Username for authentication'
+    )
+    parser.add_argument(
+        '--password', type=str, default='', help='Password for authentication'
+    )
+    parser.add_argument(
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
+    )
+    parser.add_argument(
+        '--share', action='store_true', help='Share the gradio UI'
+    )
+
+    args = parser.parse_args()
+
+    launch_kwargs = {}
+    launch_kwargs['server_name'] = args.listen
+
+    if args.username and args.password:
+        launch_kwargs['auth'] = (args.username, args.password)
+    if args.server_port:
+        launch_kwargs['server_port'] = args.server_port
+    if args.inbrowser:
+        launch_kwargs['inbrowser'] = args.inbrowser
+    if args.share:
+        launch_kwargs['share'] = args.share
+
+    # Show the interface
+    ui_full(launch_kwargs)
 
 
 def ui_batched(launch_kwargs):