trying fix

app.py

@@ -182,7 +182,7 @@ if __name__ == "__main__":
     parser.add_argument(
         '--listen',
         type=str,
-        default='127.0.0.1',
+        default='0.0.0.0',
         help='IP to listen on for connections to Gradio',
     )
     parser.add_argument(

app_batched.py

@@ -96,68 +96,127 @@ def predict(texts, melodies):
     return res
 
 
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # MusicGen
-
-        This is the 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).
-        <br/>
-        <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-        <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-        for longer sequences, more control and no queue.</p>
-        """
-    )
-    with gr.Row():
-        with gr.Column():
-            with gr.Row():
-                text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
-            with gr.Row():
-                submit = gr.Button("Generate")
-        with gr.Column():
-            output = gr.Video(label="Generated Music")
-    submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=8)
-    gr.Examples(
-        fn=predict,
-        examples=[
-            [
-                "An 80s driving pop song with heavy drums and synth pads in the background",
-                "./assets/bach.mp3",
-            ],
-            [
-                "A cheerful country song with acoustic guitars",
-                "./assets/bolero_ravel.mp3",
-            ],
-            [
-                "90s rock song with electric guitar and heavy drums",
-                None,
-            ],
-            [
-                "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
-                "./assets/bach.mp3",
+def ui(**kwargs):
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # MusicGen
+
+            This is the 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).
+            <br/>
+            <a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
+            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+            for longer sequences, more control and no queue.</p>
+            """
+        )
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
+                    melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
+                with gr.Row():
+                    submit = gr.Button("Generate")
+            with gr.Column():
+                output = gr.Video(label="Generated Music")
+        submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=8)
+        gr.Examples(
+            fn=predict,
+            examples=[
+                [
+                    "An 80s driving pop song with heavy drums and synth pads in the background",
+                    "./assets/bach.mp3",
+                ],
+                [
+                    "A cheerful country song with acoustic guitars",
+                    "./assets/bolero_ravel.mp3",
+                ],
+                [
+                    "90s rock song with electric guitar and heavy drums",
+                    None,
+                ],
+                [
+                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
+                    "./assets/bach.mp3",
+                ],
+                [
+                    "lofi slow bpm electro chill with organic samples",
+                    None,
+                ],
             ],
-            [
-                "lofi slow bpm electro chill with organic samples",
-                None,
-            ],
-        ],
-        inputs=[text, melody],
-        outputs=[output]
+            inputs=[text, melody],
+            outputs=[output]
+        )
+        gr.Markdown("""
+        ### More details
+
+        The model will generate 12 seconds of audio based on the description you provided.
+        You 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.
+        All samples are generated with the `melody` model.
+
+        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.
+        """)
+
+        # Show the interface
+        launch_kwargs = {}
+        username = kwargs.get('username')
+        password = kwargs.get('password')
+        server_port = kwargs.get('server_port', 0)
+        inbrowser = kwargs.get('inbrowser', False)
+        share = kwargs.get('share', False)
+        server_name = kwargs.get('listen')
+
+        launch_kwargs['server_name'] = server_name
+
+        if username and password:
+            launch_kwargs['auth'] = (username, password)
+        if server_port > 0:
+            launch_kwargs['server_port'] = server_port
+        if inbrowser:
+            launch_kwargs['inbrowser'] = inbrowser
+        if share:
+            launch_kwargs['share'] = share
+        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--listen',
+        type=str,
+        default='0.0.0.0',
+        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'
     )
-    gr.Markdown("""
-    ### More details
-
-    The model will generate 12 seconds of audio based on the description you provided.
-    You 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.
-    All samples are generated with the `melody` model.
-
-    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.
-    """)
+    args = parser.parse_args()
 
-    demo.queue(max_size=8 * 4).launch()
+    ui(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+        share=args.share,
+        listen=args.listen
+    )