Commit 2: Add 50 file(s)

demos/image_mod_default_image/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_mod_default_image"]}, {"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", "from gradio.media import get_image\n", "\n", "def image_mod(image):\n", "    return image.rotate(45)\n", "\n", "# get_image() returns file paths to sample media included with Gradio\n", "cheetah = get_image(\"cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", "    flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", "        get_image(\"lion.jpg\"),\n", "        get_image(\"logo.png\")\n", "        ])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(max_file_size=\"70kb\")\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_mod_default_image"]}, {"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", "from gradio.media import get_image\n", "\n", "def image_mod(image):\n", "    return image.rotate(45)\n", "\n", "# get_image() returns file paths to sample media included with Gradio\n", "cheetah = get_image(\"cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", "                    api_name=\"predict\",\n", "    flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", "        get_image(\"lion.jpg\"),\n", "        get_image(\"logo.png\")\n", "        ])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(max_file_size=\"70kb\")\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/image_mod_default_image/run.py

@@ -8,6 +8,7 @@ def image_mod(image):
 cheetah = get_image("cheetah1.jpg")
 
 demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image",
+                    api_name="predict",
     flagging_options=["blurry", "incorrect", "other"], examples=[
         get_image("lion.jpg"),
         get_image("logo.png")

demos/interface_random_slider/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_random_slider"]}, {"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", "\n", "def func(slider_1, slider_2, *args):\n", "    return slider_1 + slider_2 * 5\n", "\n", "demo = gr.Interface(\n", "    func,\n", "    [\n", "        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n", "        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n", "        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n", "        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n", "        gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n", "        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n", "    ],\n", "    \"number\",\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: interface_random_slider"]}, {"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", "\n", "def func(slider_1, slider_2, *args):\n", "    return slider_1 + slider_2 * 5\n", "\n", "demo = gr.Interface(\n", "    func,\n", "    [\n", "        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n", "        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n", "        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n", "        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n", "        gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n", "        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n", "    ],\n", "    \"number\",\n", "    api_name=\"predict\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/interface_random_slider/run.py

@@ -14,6 +14,7 @@ demo = gr.Interface(
         gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),
     ],
     "number",
+    api_name="predict"
 )
 
 if __name__ == "__main__":

demos/kitchen_sink/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: kitchen_sink"]}, {"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": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "from gradio.media import get_image, get_video, get_audio, get_file\n", "\n", "CHOICES = [\"foo\", \"bar\", \"baz\"]\n", "JSONOBJ = \"\"\"{\"items\":{\"item\":[{\"id\": \"0001\",\"type\": null,\"is_good\": false,\"ppu\": 0.55,\"batters\":{\"batter\":[{ \"id\": \"1001\", \"type\": \"Regular\" },{ \"id\": \"1002\", \"type\": \"Chocolate\" },{ \"id\": \"1003\", \"type\": \"Blueberry\" },{ \"id\": \"1004\", \"type\": \"Devil's Food\" }]},\"topping\":[{ \"id\": \"5001\", \"type\": \"None\" },{ \"id\": \"5002\", \"type\": \"Glazed\" },{ \"id\": \"5005\", \"type\": \"Sugar\" },{ \"id\": \"5007\", \"type\": \"Powdered Sugar\" },{ \"id\": \"5006\", \"type\": \"Chocolate with Sprinkles\" },{ \"id\": \"5003\", \"type\": \"Chocolate\" },{ \"id\": \"5004\", \"type\": \"Maple\" }]}]}}\"\"\"\n", "\n", "def fn(\n", "    text1,\n", "    text2,\n", "    num,\n", "    slider1,\n", "    slider2,\n", "    single_checkbox,\n", "    checkboxes,\n", "    radio,\n", "    dropdown,\n", "    multi_dropdown,\n", "    im1,\n", "    # im2,\n", "    # im3,\n", "    im4,\n", "    video,\n", "    audio1,\n", "    audio2,\n", "    file,\n", "    df1,\n", "    time,\n", "):\n", "    return (\n", "        (text1 if single_checkbox else text2)\n", "        + \", selected:\"\n", "        + \", \".join(checkboxes),  # Text\n", "        {\n", "            \"positive\": num / (num + slider1 + slider2),\n", "            \"negative\": slider1 / (num + slider1 + slider2),\n", "            \"neutral\": slider2 / (num + slider1 + slider2),\n", "        },  # Label\n", "        (audio1[0], np.flipud(audio1[1]))\n", "        if audio1 is not None\n", "        else get_audio(\"cantina.wav\"),  # Audio\n", "        np.flipud(im1)\n", "        if im1 is not None\n", "        else get_image(\"cheetah1.jpg\"),  # Image\n", "        video\n", "        if video is not None\n", "        else get_video(\"world.mp4\"),  # Video\n", "        [\n", "            (\"The\", \"art\"),\n", "            (\"quick brown\", \"adj\"),\n", "            (\"fox\", \"nn\"),\n", "            (\"jumped\", \"vrb\"),\n", "            (\"testing testing testing\", None),\n", "            (\"over\", \"prp\"),\n", "            (\"the\", \"art\"),\n", "            (\"testing\", None),\n", "            (\"lazy\", \"adj\"),\n", "            (\"dogs\", \"nn\"),\n", "            (\".\", \"punc\"),\n", "        ]\n", "        + [(f\"test {x}\", f\"test {x}\") for x in range(10)],  # HighlightedText\n", "        # [(\"The testing testing testing\", None), (\"quick brown\", 0.2), (\"fox\", 1), (\"jumped\", -1), (\"testing testing testing\", 0), (\"over\", 0), (\"the\", 0), (\"testing\", 0), (\"lazy\", 1), (\"dogs\", 0), (\".\", 1)] + [(f\"test {x}\",  x/10) for x in range(-10, 10)],  # HighlightedText\n", "        [\n", "            (\"The testing testing testing\", None),\n", "            (\"over\", 0.6),\n", "            (\"the\", 0.2),\n", "            (\"testing\", None),\n", "            (\"lazy\", -0.1),\n", "            (\"dogs\", 0.4),\n", "            (\".\", 0),\n", "        ]\n", "        + [(\"test\", x / 10) for x in range(-10, 10)],  # HighlightedText\n", "        json.loads(JSONOBJ),  # JSON\n", "        \"<button style='background-color: red'>Click Me: \"\n", "        + radio\n", "        + \"</button>\",  # HTML\n", "        get_file(\"titanic.csv\"),  # File\n", "        df1,  # Dataframe\n", "        np.random.randint(0, 10, (4, 4)),  # Dataframe\n", "        time, # DateTime\n", "    )\n", "\n", "demo = gr.Interface(\n", "    fn,\n", "    inputs=[\n", "        gr.Textbox(value=\"Lorem ipsum\", label=\"Textbox\"),\n", "        gr.Textbox(lines=3, placeholder=\"Type here..\", label=\"Textbox 2\"),\n", "        gr.Number(label=\"Number\", value=42),\n", "        gr.Slider(10, 20, value=15, label=\"Slider: 10 - 20\"),\n", "        gr.Slider(maximum=20, step=0.04, label=\"Slider: step @ 0.04\"),\n", "        gr.Checkbox(label=\"Checkbox\"),\n", "        gr.CheckboxGroup(label=\"CheckboxGroup\", choices=CHOICES, value=CHOICES[0:2]),\n", "        gr.Radio(label=\"Radio\", choices=CHOICES, value=CHOICES[2]),\n", "        gr.Dropdown(label=\"Dropdown\", choices=CHOICES),\n", "        gr.Dropdown(\n", "            label=\"Multiselect Dropdown (Max choice: 2)\",\n", "            choices=CHOICES,\n", "            multiselect=True,\n", "            max_choices=2,\n", "        ),\n", "        gr.Image(label=\"Image\"),\n", "        # gr.Image(label=\"Image w/ Cropper\", tool=\"select\"),\n", "        # gr.Image(label=\"Sketchpad\", source=\"canvas\"),\n", "        gr.Image(label=\"Webcam\", sources=[\"webcam\"]),\n", "        gr.Video(label=\"Video\"),\n", "        gr.Audio(label=\"Audio\"),\n", "        gr.Audio(label=\"Microphone\", sources=[\"microphone\"]),\n", "        gr.File(label=\"File\"),\n", "        gr.Dataframe(label=\"Dataframe\", headers=[\"Name\", \"Age\", \"Gender\"]),\n", "        gr.DateTime(label=\"DateTime\"),\n", "    ],\n", "    outputs=[\n", "        gr.Textbox(label=\"Textbox\"),\n", "        gr.Label(label=\"Label\"),\n", "        gr.Audio(label=\"Audio\"),\n", "        gr.Image(label=\"Image\", elem_id=\"output-img\"),\n", "        gr.Video(label=\"Video\"),\n", "        gr.HighlightedText(\n", "            label=\"HighlightedText\", color_map={\"punc\": \"pink\", \"test 0\": \"blue\"}\n", "        ),\n", "        gr.HighlightedText(label=\"HighlightedText\", show_legend=True),\n", "        gr.JSON(label=\"JSON\", show_indices=True),\n", "        gr.HTML(label=\"HTML\"),\n", "        gr.File(label=\"File\"),\n", "        gr.Dataframe(label=\"Dataframe\"),\n", "        gr.Dataframe(label=\"Numpy\"),\n", "        gr.DateTime(label=\"DateTime\"),\n", "    ],\n", "    examples=[\n", "        [\n", "            \"the quick brown fox\",\n", "            \"jumps over the lazy dog\",\n", "            10,\n", "            12,\n", "            4,\n", "            True,\n", "            [\"foo\", \"baz\"],\n", "            \"baz\",\n", "            \"bar\",\n", "            [\"foo\", \"bar\"],\n", "            get_image(\"cheetah1.jpg\"),\n", "            # get_image(\"cheetah1.jpg\"),\n", "            # get_image(\"cheetah1.jpg\"),\n", "            get_image(\"cheetah1.jpg\"),\n", "            get_video(\"world.mp4\"),\n", "            get_audio(\"cantina.wav\"),\n", "            get_audio(\"cantina.wav\"),\n", "            get_file(\"titanic.csv\"),\n", "            [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", "            \"2025-06-10 12:00:00\",\n", "        ]\n", "    ]\n", "    * 3,\n", "    title=\"Kitchen Sink\",\n", "    description=\"Try out all the components!\",\n", "    article=\"Learn more about [Gradio](http://gradio.app)\",\n", "    cache_examples=True,\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: kitchen_sink"]}, {"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": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "from gradio.media import get_image, get_video, get_audio, get_file\n", "\n", "CHOICES = [\"foo\", \"bar\", \"baz\"]\n", "JSONOBJ = \"\"\"{\"items\":{\"item\":[{\"id\": \"0001\",\"type\": null,\"is_good\": false,\"ppu\": 0.55,\"batters\":{\"batter\":[{ \"id\": \"1001\", \"type\": \"Regular\" },{ \"id\": \"1002\", \"type\": \"Chocolate\" },{ \"id\": \"1003\", \"type\": \"Blueberry\" },{ \"id\": \"1004\", \"type\": \"Devil's Food\" }]},\"topping\":[{ \"id\": \"5001\", \"type\": \"None\" },{ \"id\": \"5002\", \"type\": \"Glazed\" },{ \"id\": \"5005\", \"type\": \"Sugar\" },{ \"id\": \"5007\", \"type\": \"Powdered Sugar\" },{ \"id\": \"5006\", \"type\": \"Chocolate with Sprinkles\" },{ \"id\": \"5003\", \"type\": \"Chocolate\" },{ \"id\": \"5004\", \"type\": \"Maple\" }]}]}}\"\"\"\n", "\n", "def fn(\n", "    text1,\n", "    text2,\n", "    num,\n", "    slider1,\n", "    slider2,\n", "    single_checkbox,\n", "    checkboxes,\n", "    radio,\n", "    dropdown,\n", "    multi_dropdown,\n", "    im1,\n", "    # im2,\n", "    # im3,\n", "    im4,\n", "    video,\n", "    audio1,\n", "    audio2,\n", "    file,\n", "    df1,\n", "    time,\n", "):\n", "    return (\n", "        (text1 if single_checkbox else text2)\n", "        + \", selected:\"\n", "        + \", \".join(checkboxes),  # Text\n", "        {\n", "            \"positive\": num / (num + slider1 + slider2),\n", "            \"negative\": slider1 / (num + slider1 + slider2),\n", "            \"neutral\": slider2 / (num + slider1 + slider2),\n", "        },  # Label\n", "        (audio1[0], np.flipud(audio1[1]))\n", "        if audio1 is not None\n", "        else get_audio(\"cantina.wav\"),  # Audio\n", "        np.flipud(im1)\n", "        if im1 is not None\n", "        else get_image(\"cheetah1.jpg\"),  # Image\n", "        video\n", "        if video is not None\n", "        else get_video(\"world.mp4\"),  # Video\n", "        [\n", "            (\"The\", \"art\"),\n", "            (\"quick brown\", \"adj\"),\n", "            (\"fox\", \"nn\"),\n", "            (\"jumped\", \"vrb\"),\n", "            (\"testing testing testing\", None),\n", "            (\"over\", \"prp\"),\n", "            (\"the\", \"art\"),\n", "            (\"testing\", None),\n", "            (\"lazy\", \"adj\"),\n", "            (\"dogs\", \"nn\"),\n", "            (\".\", \"punc\"),\n", "        ]\n", "        + [(f\"test {x}\", f\"test {x}\") for x in range(10)],  # HighlightedText\n", "        # [(\"The testing testing testing\", None), (\"quick brown\", 0.2), (\"fox\", 1), (\"jumped\", -1), (\"testing testing testing\", 0), (\"over\", 0), (\"the\", 0), (\"testing\", 0), (\"lazy\", 1), (\"dogs\", 0), (\".\", 1)] + [(f\"test {x}\",  x/10) for x in range(-10, 10)],  # HighlightedText\n", "        [\n", "            (\"The testing testing testing\", None),\n", "            (\"over\", 0.6),\n", "            (\"the\", 0.2),\n", "            (\"testing\", None),\n", "            (\"lazy\", -0.1),\n", "            (\"dogs\", 0.4),\n", "            (\".\", 0),\n", "        ]\n", "        + [(\"test\", x / 10) for x in range(-10, 10)],  # HighlightedText\n", "        json.loads(JSONOBJ),  # JSON\n", "        \"<button style='background-color: red'>Click Me: \"\n", "        + radio\n", "        + \"</button>\",  # HTML\n", "        get_file(\"titanic.csv\"),  # File\n", "        df1,  # Dataframe\n", "        np.random.randint(0, 10, (4, 4)),  # Dataframe\n", "        time, # DateTime\n", "    )\n", "\n", "demo = gr.Interface(\n", "    fn,\n", "    inputs=[\n", "        gr.Textbox(value=\"Lorem ipsum\", label=\"Textbox\"),\n", "        gr.Textbox(lines=3, placeholder=\"Type here..\", label=\"Textbox 2\"),\n", "        gr.Number(label=\"Number\", value=42),\n", "        gr.Slider(10, 20, value=15, label=\"Slider: 10 - 20\"),\n", "        gr.Slider(maximum=20, step=0.04, label=\"Slider: step @ 0.04\"),\n", "        gr.Checkbox(label=\"Checkbox\"),\n", "        gr.CheckboxGroup(label=\"CheckboxGroup\", choices=CHOICES, value=CHOICES[0:2]),\n", "        gr.Radio(label=\"Radio\", choices=CHOICES, value=CHOICES[2]),\n", "        gr.Dropdown(label=\"Dropdown\", choices=CHOICES),\n", "        gr.Dropdown(\n", "            label=\"Multiselect Dropdown (Max choice: 2)\",\n", "            choices=CHOICES,\n", "            multiselect=True,\n", "            max_choices=2,\n", "        ),\n", "        gr.Image(label=\"Image\"),\n", "        # gr.Image(label=\"Image w/ Cropper\", tool=\"select\"),\n", "        # gr.Image(label=\"Sketchpad\", source=\"canvas\"),\n", "        gr.Image(label=\"Webcam\", sources=[\"webcam\"]),\n", "        gr.Video(label=\"Video\"),\n", "        gr.Audio(label=\"Audio\"),\n", "        gr.Audio(label=\"Microphone\", sources=[\"microphone\"]),\n", "        gr.File(label=\"File\"),\n", "        gr.Dataframe(label=\"Dataframe\", headers=[\"Name\", \"Age\", \"Gender\"]),\n", "        gr.DateTime(label=\"DateTime\"),\n", "    ],\n", "    outputs=[\n", "        gr.Textbox(label=\"Textbox\"),\n", "        gr.Label(label=\"Label\"),\n", "        gr.Audio(label=\"Audio\"),\n", "        gr.Image(label=\"Image\", elem_id=\"output-img\"),\n", "        gr.Video(label=\"Video\"),\n", "        gr.HighlightedText(\n", "            label=\"HighlightedText\", color_map={\"punc\": \"pink\", \"test 0\": \"blue\"}\n", "        ),\n", "        gr.HighlightedText(label=\"HighlightedText\", show_legend=True),\n", "        gr.JSON(label=\"JSON\", show_indices=True),\n", "        gr.HTML(label=\"HTML\"),\n", "        gr.File(label=\"File\"),\n", "        gr.Dataframe(label=\"Dataframe\"),\n", "        gr.Dataframe(label=\"Numpy\"),\n", "        gr.DateTime(label=\"DateTime\"),\n", "    ],\n", "    examples=[\n", "        [\n", "            \"the quick brown fox\",\n", "            \"jumps over the lazy dog\",\n", "            10,\n", "            12,\n", "            4,\n", "            True,\n", "            [\"foo\", \"baz\"],\n", "            \"baz\",\n", "            \"bar\",\n", "            [\"foo\", \"bar\"],\n", "            get_image(\"cheetah1.jpg\"),\n", "            # get_image(\"cheetah1.jpg\"),\n", "            # get_image(\"cheetah1.jpg\"),\n", "            get_image(\"cheetah1.jpg\"),\n", "            get_video(\"world.mp4\"),\n", "            get_audio(\"cantina.wav\"),\n", "            get_audio(\"cantina.wav\"),\n", "            get_file(\"titanic.csv\"),\n", "            [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", "            \"2025-06-10 12:00:00\",\n", "        ]\n", "    ]\n", "    * 3,\n", "    title=\"Kitchen Sink\",\n", "    description=\"Try out all the components!\",\n", "    article=\"Learn more about [Gradio](http://gradio.app)\",\n", "    cache_examples=True,\n", "    api_name=\"predict\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/kitchen_sink/run.py

@@ -159,6 +159,7 @@ demo = gr.Interface(
     description="Try out all the components!",
     article="Learn more about [Gradio](http://gradio.app)",
     cache_examples=True,
+    api_name="predict"
 )
 
 if __name__ == "__main__":

demos/kitchen_sink_random/run.py

@@ -86,6 +86,7 @@ demo = gr.Interface(
     outputs=[
         gr.State(value=lambda: random.choice(string.ascii_lowercase))
     ],
+    api_name="predict",
 )
 
 if __name__ == "__main__":

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, 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}
+{"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", "    api_name=\"predict\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/matrix_transpose/run.py

@@ -16,7 +16,8 @@ demo = gr.Interface(
         [np.random.randint(0, 10, (10, 3)).tolist()],
         [np.random.randint(0, 10, (10, 10)).tolist()],
     ],
-    cache_examples=False
+    cache_examples=False,
+    api_name="predict"
 )
 
 if __name__ == "__main__":

demos/model3D/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: model3D"]}, {"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", "# get_model3d() returns the file path to sample 3D models included with Gradio\n", "from gradio.media import get_model3d, MEDIA_ROOT\n", "\n", "\n", "def load_mesh(mesh_file_name):\n", "    return mesh_file_name\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=load_mesh,\n", "    inputs=gr.Model3D(label=\"Other name\", display_mode=\"wireframe\"),\n", "    outputs=gr.Model3D(\n", "        clear_color=(0.0, 0.0, 0.0, 0.0), label=\"3D Model\", display_mode=\"wireframe\"\n", "    ),\n", "    examples=[\n", "        [get_model3d(\"Bunny.obj\")],\n", "        [get_model3d(\"Duck.glb\")],\n", "        [get_model3d(\"Fox.gltf\")],\n", "        [get_model3d(\"face.obj\")],\n", "        [get_model3d(\"sofia.stl\")],\n", "        [\n", "            \"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat\"\n", "        ],\n", "        [\n", "            \"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply\"\n", "        ],\n", "    ],\n", "    cache_examples=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(allowed_paths=[str(MEDIA_ROOT)])\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: model3D"]}, {"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", "# get_model3d() returns the file path to sample 3D models included with Gradio\n", "from gradio.media import get_model3d, MEDIA_ROOT\n", "\n", "\n", "def load_mesh(mesh_file_name):\n", "    return mesh_file_name\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=load_mesh,\n", "    inputs=gr.Model3D(label=\"Other name\", display_mode=\"wireframe\"),\n", "    outputs=gr.Model3D(\n", "        clear_color=(0.0, 0.0, 0.0, 0.0), label=\"3D Model\", display_mode=\"wireframe\"\n", "    ),\n", "    examples=[\n", "        [get_model3d(\"Bunny.obj\")],\n", "        [get_model3d(\"Duck.glb\")],\n", "        [get_model3d(\"Fox.gltf\")],\n", "        [get_model3d(\"face.obj\")],\n", "        [get_model3d(\"sofia.stl\")],\n", "        [\n", "            \"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat\"\n", "        ],\n", "        [\n", "            \"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply\"\n", "        ],\n", "    ],\n", "    cache_examples=True,\n", "    api_name=\"predict\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(allowed_paths=[str(MEDIA_ROOT)])\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/model3D/run.py

@@ -27,6 +27,7 @@ demo = gr.Interface(
         ],
     ],
     cache_examples=True,
+    api_name="predict",
 )
 
 if __name__ == "__main__":

demos/native_plots/line_plot_demo.py

@@ -54,14 +54,18 @@ with gr.Blocks() as line_plots:
     with gr.Row():
         price_by_rating = gr.LinePlot(
             food_rating_data,
+            title="Price by Rating (for Ratings >2)",
             x="rating",
             y="price",
+            x_lim=[2, None],
             buttons=["export"],
         )
         price_by_rating_color = gr.LinePlot(
             food_rating_data,
+            title="Price by Rating (for Ratings <4)",
             x="rating",
             y="price",
+            x_lim=[None, 4],
             color="cuisine",
             color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
             buttons=["export"],

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_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}
+{"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", "    api_name=\"predict\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/reverse_audio/run.py

@@ -19,7 +19,8 @@ input_audio = gr.Audio(
 demo = gr.Interface(
     fn=reverse_audio,
     inputs=input_audio,
-    outputs="audio"
+    outputs="audio",
+    api_name="predict",
 )
 
 if __name__ == "__main__":

demos/stream_frames/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stream_frames"]}, {"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": ["import gradio as gr\n", "import numpy as np\n", "\n", "def flip(im):\n", "    return np.flipud(im)\n", "\n", "demo = gr.Interface(\n", "    flip,\n", "    gr.Image(sources=[\"webcam\"], streaming=True),\n", "    \"image\",\n", "    live=True\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: stream_frames"]}, {"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": ["import gradio as gr\n", "import numpy as np\n", "\n", "def flip(im):\n", "    return np.flipud(im)\n", "\n", "demo = gr.Interface(\n", "    flip,\n", "    gr.Image(sources=[\"webcam\"], streaming=True),\n", "    \"image\",\n", "    live=True,\n", "    api_name=\"predict\",\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/stream_frames/run.py

@@ -8,7 +8,8 @@ demo = gr.Interface(
     flip,
     gr.Image(sources=["webcam"], streaming=True),
     "image",
-    live=True
+    live=True,
+    api_name="predict",
 )
 if __name__ == "__main__":
     demo.launch()

demos/video_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: video_component"]}, {"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", "# get_video() returns the file path to sample videos included with Gradio\n", "from gradio.media import get_video\n", "\n", "demo = gr.Interface(\n", "    fn=lambda x: x,\n", "    inputs=gr.Video(),\n", "    outputs=gr.Video(),\n", "    examples=[\n", "        [get_video(\"world.mp4\")],\n", "        [get_video(\"a.mp4\")],\n", "        [get_video(\"b.mp4\")],\n", "    ],\n", "    cache_examples=True\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: video_component"]}, {"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", "# get_video() returns the file path to sample videos included with Gradio\n", "from gradio.media import get_video\n", "\n", "demo = gr.Interface(\n", "    fn=lambda x: x,\n", "    inputs=gr.Video(),\n", "    outputs=gr.Video(),\n", "    examples=[\n", "        [get_video(\"world.mp4\")],\n", "        [get_video(\"a.mp4\")],\n", "        [get_video(\"b.mp4\")],\n", "    ],\n", "    cache_examples=True,\n", "    api_name=\"predict\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/video_component/run.py

@@ -11,7 +11,8 @@ demo = gr.Interface(
         [get_video("a.mp4")],
         [get_video("b.mp4")],
     ],
-    cache_examples=True
+    cache_examples=True,
+    api_name="predict"
 )
 
 if __name__ == "__main__":

demos/zip_files/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: zip_files"]}, {"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": ["from zipfile import ZipFile\n", "\n", "import gradio as gr\n", "\n", "def zip_files(files):\n", "    with ZipFile(\"tmp.zip\", \"w\") as zip_obj:\n", "        for file in files:\n", "            zip_obj.write(file.name, file.name.split(\"/\")[-1])\n", "    return \"tmp.zip\"\n", "\n", "demo = gr.Interface(\n", "    zip_files,\n", "    gr.File(file_count=\"multiple\", file_types=[\"text\", \".json\", \".csv\"]),\n", "    \"file\",\n", "    examples=[[[gr.get_file(\"titanic.csv\"),\n", "    gr.get_file(\"titanic.csv\"),\n", "    gr.get_file(\"titanic.csv\")]]],\n", "    cache_examples=True\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: zip_files"]}, {"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": ["from zipfile import ZipFile\n", "\n", "import gradio as gr\n", "\n", "def zip_files(files):\n", "    with ZipFile(\"tmp.zip\", \"w\") as zip_obj:\n", "        for file in files:\n", "            zip_obj.write(file.name, file.name.split(\"/\")[-1])\n", "    return \"tmp.zip\"\n", "\n", "demo = gr.Interface(\n", "    zip_files,\n", "    gr.File(file_count=\"multiple\", file_types=[\"text\", \".json\", \".csv\"]),\n", "    \"file\",\n", "    examples=[[[gr.get_file(\"titanic.csv\"),\n", "    gr.get_file(\"titanic.csv\"),\n", "    gr.get_file(\"titanic.csv\")]]],\n", "    cache_examples=True,\n", "    api_name=\"predict\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}