justArtist

app.py

@@ -30,110 +30,10 @@ drawdemo = gr.Interface(
     ],
     outputs="image",
         examples=[
-        ['van gogh dogs playing poker', "stable-diffusion-v1-5", False],
-        ['picasso the scream', "stable-diffusion-v1-5", False],
-        ['dali american gothic', "stable-diffusion-v1-5", False],
-        ['matisse mona lisa', "stable-diffusion-v1-5", False],
-        ['maxfield parrish angel in lake ', "stable-diffusion-v1-5", False],
-        ['peter max dogs playing poker', "stable-diffusion-v1-5", False],
-        ['hindu mandala copper and patina green', "stable-diffusion-v1-5", False],
-        ['hindu mandala fruit salad', "stable-diffusion-v1-5", False],
-        ['hindu mandala neon green black and purple', "stable-diffusion-v1-5", False],
-        ['astronaut riding a horse on mars', "stable-diffusion-v1-5", False]
+        ['hindu mandala fruit salad', "stable-diffusion-v1-5", False]
     ],
 )
 
-AudioDemo = gr.Interface(
-    fn=artist_lib.generate_tone,
-    inputs=[
-        gr.Dropdown(artist_lib.notes, type="index"),
-        gr.Slider(4, 6, step=1),
-        gr.Textbox(value=1, label="Duration in seconds")
-    ],
-    outputs="audio"
-)
-
-imageClassifierDemo = gr.Interface(
-    fn=artist_lib.imageClassifier,
-    inputs="image",
-    outputs="text"
-)
-
-audioGeneratorDemo = gr.Interface(
-    fn=artist_lib.audioGenerator,
-    inputs="text",
-    outputs="audio",
-    examples=[
-        ['balsamic beats'],
-        ['dance the night away']
-    ]
-)
-
-nameMyPetDemo = gr.Interface(
-    fn=artist_lib.nameMyPet,
-    inputs=[
-        gr.Text(label="What type of animal is your pet?", value="green cat")
-    ],
-    outputs="text",
-        examples=[
-        ['dog'],
-        ['pink dolphin'],
-        ['elevated elephant'],
-        ['green monkey'],
-        ['bionic beaver'],
-        ['felonous fish'],
-        ['delinquent dog'],
-        ['dragging donkey'],
-        ['stinky skunk'],
-        ['pink unicorn'],
-        ['naughty narwahl'],
-        ['blue cat']
-    ],
-)
-
-blog_writer_demo = gr.Interface(
-    fn=artist_lib.write_blog,
-    inputs=[
-        gr.Text(label="Blog description text", value="machine learning can be used to track chickens"),
-        gr.Dropdown(label='Model', choices=["gpt-neo-1.3B", "gpt-neo-2.7B"], value="gpt-neo-1.3B"),
-        gr.Number(label='Minimum word count', value=50, precision=0),
-        gr.Number(label='Maximum word count', value=50, precision=0),
-        gr.Checkbox(label="Force-New"),
-    ],
-    outputs="text",
-        examples=[
-        ['machine learning can be used to track chickens', "gpt-neo-1.3B", 50, 50, False],
-        ['music and machine learning', "gpt-neo-2.7B", 50, 50, False]
-    ],
-)
-
-generateAudioDemo = gr.Interface(
-    fn=artist_lib.generate_spectrogram_audio_and_loop,
-    title="Audio Diffusion",
-    description="Generate audio using Huggingface diffusers.\
-        The models without 'latent' or 'ddim' give better results but take about \
-            20 minutes without a GPU. For GPU, you can use \
-                [colab](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/gradio_app.ipynb) \
-                    to run this app.",
-    inputs=[
-        gr.Dropdown(label="Model",
-                    choices=[
-                        "teticio/audio-diffusion-256",
-                        "teticio/audio-diffusion-breaks-256",
-                        "teticio/audio-diffusion-instrumental-hiphop-256",
-                        "teticio/audio-diffusion-ddim-256",
-                        "teticio/latent-audio-diffusion-256",
-                        "teticio/latent-audio-diffusion-ddim-256"
-                    ],
-                    value="teticio/latent-audio-diffusion-ddim-256")
-    ],
-    outputs=[
-        gr.Image(label="Mel spectrogram", image_mode="L"),
-        gr.Audio(label="Audio"),
-        gr.Audio(label="Loop"),
-    ],
-    allow_flagging="never")
-
 with gr.Blocks() as gallerydemo:
     with gr.Column(variant="panel"):
         with gr.Row(variant="compact"):
@@ -151,9 +51,7 @@ with gr.Blocks() as gallerydemo:
 
     btn.click(artist_lib.fake_gan, None, gallery)
 
-#artist = gr.TabbedInterface( [drawdemo, blog_writer_demo, gallerydemo], ["Draw", "Bloggr", "Gallery"])
-#artist = gr.TabbedInterface( [drawdemo, blog_writer_demo, imageClassifierDemo, generateAudioDemo, audioGeneratorDemo, AudioDemo, nameMyPetDemo], ["Draw", "Bloggr", "imageClassifier", "generateAudio", "audioGenerator", "AudioDemo", "nameMyPet"])
-artist = gr.TabbedInterface( [drawdemo, imageClassifierDemo, generateAudioDemo, nameMyPetDemo, blog_writer_demo], ["Draw", "imageClassifier", "generateAudio", "nameMyPet", "Bloggr"])
+artist = gr.TabbedInterface( [drawdemo], ["Draw"])
 
 artist.queue(
     max_size = 4