Commit 2: Add 50 file(s)

demos/image_editor_events/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "\n", "def predict(im):\n", "    return im[\"composite\"]\n", "\n", "\n", "def verify_clear(im):\n", "    print(im)\n", "    return int(not np.any(im[\"composite\"])), im[\"composite\"]\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Group():\n", "        with gr.Row():\n", "            im = gr.ImageEditor(\n", "                type=\"numpy\",\n", "                crop_size=\"1:1\",\n", "                elem_id=\"image_editor\",\n", "            )\n", "            im_preview = gr.Image()\n", "    with gr.Group():\n", "        with gr.Row():\n", "\n", "            n_upload = gr.Label(\n", "                0,\n", "                label=\"upload\",\n", "                elem_id=\"upload\",\n", "            )\n", "            n_change = gr.Label(\n", "                0,\n", "                label=\"change\",\n", "                elem_id=\"change\",\n", "            )\n", "            n_input = gr.Label(\n", "                0,\n", "                label=\"input\",\n", "                elem_id=\"input\",\n", "            )\n", "            n_apply = gr.Label(\n", "                0,\n", "                label=\"apply\",\n", "                elem_id=\"apply\",\n", "            )\n", "            cleared_properly = gr.Number(label=\"cleared properly\")\n", "    clear_btn = gr.Button(\"Clear Button\", elem_id=\"clear\")\n", "\n", "    im.upload(\n", "        lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress=\"hidden\"\n", "    )\n", "    im.change(\n", "        lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress=\"hidden\"\n", "    )\n", "    im.input(\n", "        lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress=\"hidden\"\n", "    )\n", "    im.apply(\n", "        lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress=\"hidden\"\n", "    )\n", "    im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", "    clear_btn.click(\n", "        lambda: None,\n", "        None,\n", "        im,\n", "    ).then(verify_clear, inputs=im, outputs=[cleared_properly, im])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "\n", "def predict(im):\n", "    return im[\"composite\"]\n", "\n", "\n", "def verify_clear(im):\n", "    print(im)\n", "    return int(not np.any(im[\"composite\"])), im[\"composite\"]\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Group():\n", "        with gr.Row():\n", "            im = gr.ImageEditor(\n", "                type=\"numpy\",\n", "                elem_id=\"image_editor\",\n", "            )\n", "            im_preview = gr.Image()\n", "    with gr.Group():\n", "        with gr.Row():\n", "\n", "            n_upload = gr.Label(\n", "                0,\n", "                label=\"upload\",\n", "                elem_id=\"upload\",\n", "            )\n", "            n_change = gr.Label(\n", "                0,\n", "                label=\"change\",\n", "                elem_id=\"change\",\n", "            )\n", "            n_input = gr.Label(\n", "                0,\n", "                label=\"input\",\n", "                elem_id=\"input\",\n", "            )\n", "            n_apply = gr.Label(\n", "                0,\n", "                label=\"apply\",\n", "                elem_id=\"apply\",\n", "            )\n", "            cleared_properly = gr.Number(label=\"cleared properly\")\n", "    clear_btn = gr.Button(\"Clear Button\", elem_id=\"clear\")\n", "\n", "    im.upload(\n", "        lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress=\"hidden\"\n", "    )\n", "    im.change(\n", "        lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress=\"hidden\"\n", "    )\n", "    im.input(\n", "        lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress=\"hidden\"\n", "    )\n", "    im.apply(\n", "        lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress=\"hidden\"\n", "    )\n", "    im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", "    clear_btn.click(\n", "        lambda: None,\n", "        None,\n", "        im,\n", "    ).then(verify_clear, inputs=im, outputs=[cleared_properly, im])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/image_editor_events/run.py

@@ -16,7 +16,6 @@ with gr.Blocks() as demo:
         with gr.Row():
             im = gr.ImageEditor(
                 type="numpy",
-                crop_size="1:1",
                 elem_id="image_editor",
             )
             im_preview = gr.Image()

demos/matrix_transpose/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: matrix_transpose"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def transpose(matrix):\n", "    return matrix.T\n", "\n", "demo = gr.Interface(\n", "    transpose,\n", "    gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n", "    \"numpy\",\n", "    examples=[\n", "        [np.zeros((30, 30)).tolist()],\n", "        [np.ones((2, 2)).tolist()],\n", "        [np.random.randint(0, 10, (3, 10)).tolist()],\n", "        [np.random.randint(0, 10, (10, 3)).tolist()],\n", "        [np.random.randint(0, 10, (10, 10)).tolist()],\n", "    ],\n", "    cache_examples=False\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: matrix_transpose"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def transpose(matrix):\n", "    return matrix.T\n", "\n", "demo = gr.Interface(\n", "    transpose,\n", "    gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3, buttons=[\"fullscreen\"]),\n", "    \"numpy\",\n", "    examples=[\n", "        [np.zeros((30, 30)).tolist()],\n", "        [np.ones((2, 2)).tolist()],\n", "        [np.random.randint(0, 10, (3, 10)).tolist()],\n", "        [np.random.randint(0, 10, (10, 3)).tolist()],\n", "        [np.random.randint(0, 10, (10, 10)).tolist()],\n", "    ],\n", "    cache_examples=False\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/matrix_transpose/run.py

@@ -7,7 +7,7 @@ def transpose(matrix):
 
 demo = gr.Interface(
     transpose,
-    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3, show_fullscreen_button=True),
+    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3, buttons=["fullscreen"]),
     "numpy",
     examples=[
         [np.zeros((30, 30)).tolist()],

demos/native_plots/bar_plot_demo.py

@@ -15,14 +15,14 @@ with gr.Blocks() as bar_plots:
             temp_sensor_data,
             x="time",
             y="temperature",
-            show_export_button=True,
+            buttons=["export"],
         )
         temp_by_time_location = gr.BarPlot(
             temp_sensor_data,
             x="time",
             y="temperature",
             color="location",
-            show_export_button=True,
+            buttons=["export"],
         )
 
     time_graphs = [temp_by_time, temp_by_time_location]
@@ -51,7 +51,7 @@ with gr.Blocks() as bar_plots:
             food_rating_data,
             x="cuisine",
             y="price",
-            show_export_button=True,
+            buttons=["export"],
         )
         with gr.Column(scale=0):
             gr.Button("Sort $ > $$$").click(lambda: gr.BarPlot(sort="y"), None, price_by_cuisine)
@@ -64,7 +64,7 @@ with gr.Blocks() as bar_plots:
             x="rating",
             y="price",
             x_bin=1,
-            show_export_button=True,
+            buttons=["export"],
         )
         price_by_rating_color = gr.BarPlot(
             food_rating_data,
@@ -73,7 +73,7 @@ with gr.Blocks() as bar_plots:
             color="cuisine",
             x_bin=1,
             color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
-            show_export_button=True,
+            buttons=["export"],
         )
 
 if __name__ == "__main__":

demos/native_plots/line_plot_demo.py

@@ -14,14 +14,14 @@ with gr.Blocks() as line_plots:
         temp_sensor_data,
         x="time",
         y="temperature",
-        show_export_button=True,
+        buttons=["export"],
     )
     temp_by_time_location = gr.LinePlot(
         temp_sensor_data,
         x="time",
         y="temperature",
         color="location",
-        show_export_button=True,
+        buttons=["export"],
     )
 
     time_graphs = [temp_by_time, temp_by_time_location]
@@ -49,14 +49,14 @@ with gr.Blocks() as line_plots:
         food_rating_data,
         x="cuisine",
         y="price",
-        show_export_button=True,
+        buttons=["export"],
     )
     with gr.Row():
         price_by_rating = gr.LinePlot(
             food_rating_data,
             x="rating",
             y="price",
-            show_export_button=True,
+            buttons=["export"],
         )
         price_by_rating_color = gr.LinePlot(
             food_rating_data,
@@ -64,7 +64,7 @@ with gr.Blocks() as line_plots:
             y="price",
             color="cuisine",
             color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
-            show_export_button=True,
+            buttons=["export"],
         )
 
 if __name__ == "__main__":

demos/native_plots/scatter_plot_demo.py

@@ -14,14 +14,14 @@ with gr.Blocks() as scatter_plots:
         temp_sensor_data,
         x="time",
         y="temperature",
-        show_export_button=True,
+        buttons=["export"],
     )
     temp_by_time_location = gr.ScatterPlot(
         temp_sensor_data,
         x="time",
         y="temperature",
         color="location",
-        show_export_button=True,
+        buttons=["export"],
     )
 
     time_graphs = [temp_by_time, temp_by_time_location]
@@ -49,7 +49,7 @@ with gr.Blocks() as scatter_plots:
         food_rating_data,
         x="cuisine",
         y="price",
-        show_export_button=True,
+        buttons=["export"],
     )
     with gr.Row():
         price_by_rating = gr.ScatterPlot(
@@ -57,15 +57,14 @@ with gr.Blocks() as scatter_plots:
             x="rating",
             y="price",
             color="wait",
-            show_actions_button=True,
-            show_export_button=True,
+            buttons=["actions", "export"],
         )
         price_by_rating_color = gr.ScatterPlot(
             food_rating_data,
             x="rating",
             y="price",
             color="cuisine",
-            show_export_button=True,
+            buttons=["export"],
         )
 
 if __name__ == "__main__":

demos/reverse_audio/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: reverse_audio"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def reverse_audio(audio):\n", "    sr, data = audio\n", "    return (sr, np.flipud(data))\n", "\n", "input_audio = gr.Audio(\n", "    sources=[\"microphone\"],\n", "    waveform_options=gr.WaveformOptions(\n", "        waveform_color=\"#01C6FF\",\n", "        waveform_progress_color=\"#0066B4\",\n", "        skip_length=2,\n", "        show_controls=False,\n", "    ),\n", ")\n", "demo = gr.Interface(\n", "    fn=reverse_audio,\n", "    inputs=input_audio,\n", "    outputs=\"audio\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: reverse_audio"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def reverse_audio(audio):\n", "    sr, data = audio\n", "    return (sr, np.flipud(data))\n", "\n", "input_audio = gr.Audio(\n", "    sources=[\"microphone\"],\n", "    waveform_options=gr.WaveformOptions(\n", "        waveform_color=\"#01C6FF\",\n", "        waveform_progress_color=\"#0066B4\",\n", "        skip_length=2,\n", "        show_recording_waveform=False,\n", "    ),\n", ")\n", "demo = gr.Interface(\n", "    fn=reverse_audio,\n", "    inputs=input_audio,\n", "    outputs=\"audio\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/reverse_audio/run.py

@@ -13,7 +13,7 @@ input_audio = gr.Audio(
         waveform_color="#01C6FF",
         waveform_progress_color="#0066B4",
         skip_length=2,
-        show_controls=False,
+        show_recording_waveform=False,
     ),
 )
 demo = gr.Interface(