remove needless things in ui

app.py

@@ -199,23 +199,12 @@ def toggle_diffusion(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")
                     # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
@@ -244,105 +233,12 @@ def ui_full(launch_kwargs):
                                                                      temperature, cfg_coef],
                                                outputs=[output, audio_output, diffusion_output, audio_diffusion])
         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",
-                    "facebook/musicgen-melody",
-                    "Default"
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                    "facebook/musicgen-melody",
-                    "Default"
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                    "facebook/musicgen-medium",
-                    "Default"
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
-                    "./assets/bach.mp3",
-                    "facebook/musicgen-melody",
-                    "Default"
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                    "facebook/musicgen-medium",
-                    "Default"
-                ],
-                [
-                    "Punk rock with loud drum and power guitar",
-                    None,
-                    "facebook/musicgen-medium",
-                    "MultiBand_Diffusion"
-                ],
-            ],
-            inputs=[text, melody, model, decoder],
-            outputs=[output]
-        )
-        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. facebook/musicgen-melody -- a music generation model capable of generating music condition
-                on text and melody inputs. **Note**, you can also use text only.
-            2. facebook/musicgen-small -- a 300M transformer decoder conditioned on text only.
-            3. facebook/musicgen-medium -- a 1.5B transformer decoder conditioned on text only.
-            4. facebook/musicgen-large -- a 3.3B transformer decoder conditioned on text only.
-
-            We also present two way of decoding the audio tokens
-            1. Use the default GAN based compression model
-            2. Use MultiBand Diffusion from (paper linknano )
-
-            When using `facebook/musicgen-melody`, you can optionally 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.
-            """
-        )
-
         interface.queue().launch(**launch_kwargs)
 
 
 def ui_batched(launch_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/facebook/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():
@@ -360,47 +256,6 @@ def ui_batched(launch_kwargs):
         submit.click(predict_batched, inputs=[text, melody],
                      outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
         radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_batched,
-            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,
-                ],
-            ],
-            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 optionally 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.
-        """)
-
         demo.queue(max_size=8 * 4).launch(**launch_kwargs)