Commit 2: Add 50 file(s)

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 demos/calculator/screenshot.gif filter=lfs diff=lfs merge=lfs -text
+demos/kitchen_sink/files/world.mp4 filter=lfs diff=lfs merge=lfs -text

demos/file_explorer_component_events/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: file_explorer_component_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": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('dir1')\n", "!wget -q -O dir1/bar.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/bar.txt\n", "!wget -q -O dir1/foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/foo.txt\n", "os.mkdir('dir2')\n", "!wget -q -O dir2/baz.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/baz.png\n", "!wget -q -O dir2/foo.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/foo.png\n", "os.mkdir('dir3')\n", "!wget -q -O dir3/dir3_bar.log https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_bar.log\n", "!wget -q -O dir3/dir3_foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_foo.txt\n", "!wget -q -O dir3/dir4 https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from pathlib import Path\n", "\n", "base_root = Path(__file__).parent.resolve()\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        dd = gr.Dropdown(label=\"Select File Explorer Root\",\n", "                        value=str(base_root / \"dir1\"),\n", "                        choices=[str(base_root / \"dir1\"), str(base_root / \"dir2\"),\n", "                                 str(base_root / \"dir3\")])\n", "        with gr.Group():\n", "            txt_only_glob = gr.Checkbox(label=\"Show only text files\", value=False)\n", "            ignore_txt_in_glob = gr.Checkbox(label=\"Ignore text files in glob\", value=False)\n", "\n", "    fe = gr.FileExplorer(root_dir=str(base_root / \"dir1\"),\n", "                         glob=\"**/*\", interactive=True)\n", "    textbox = gr.Textbox(label=\"Selected Directory\")\n", "    run = gr.Button(\"Run\")\n", "    total_changes = gr.Number(0, elem_id=\"total-changes\")\n", "\n", "    txt_only_glob.select(lambda s: gr.FileExplorer(glob=\"*.txt\" if s else \"*\") ,\n", "                         inputs=[txt_only_glob], outputs=[fe])\n", "    ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob=\"*.txt\" if s else None),\n", "                            inputs=[ignore_txt_in_glob], outputs=[fe])\n", "\n", "    dd.select(lambda s: gr.FileExplorer(root_dir=s), inputs=[dd], outputs=[fe])\n", "    run.click(lambda s: \",\".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])\n", "    fe.change(lambda num: num + 1, inputs=total_changes, outputs=total_changes)\n", "\n", "    with gr.Row():\n", "        a = gr.Textbox(elem_id=\"input-box\")\n", "        a.change(lambda x: x, inputs=[a])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/file_explorer_component_events/run.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from pathlib import Path
+
+base_root = Path(__file__).parent.resolve()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        dd = gr.Dropdown(label="Select File Explorer Root",
+                        value=str(base_root / "dir1"),
+                        choices=[str(base_root / "dir1"), str(base_root / "dir2"),
+                                 str(base_root / "dir3")])
+        with gr.Group():
+            txt_only_glob = gr.Checkbox(label="Show only text files", value=False)
+            ignore_txt_in_glob = gr.Checkbox(label="Ignore text files in glob", value=False)
+
+    fe = gr.FileExplorer(root_dir=str(base_root / "dir1"),
+                         glob="**/*", interactive=True)
+    textbox = gr.Textbox(label="Selected Directory")
+    run = gr.Button("Run")
+    total_changes = gr.Number(0, elem_id="total-changes")
+
+    txt_only_glob.select(lambda s: gr.FileExplorer(glob="*.txt" if s else "*") ,
+                         inputs=[txt_only_glob], outputs=[fe])
+    ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob="*.txt" if s else None),
+                            inputs=[ignore_txt_in_glob], outputs=[fe])
+
+    dd.select(lambda s: gr.FileExplorer(root_dir=s), inputs=[dd], outputs=[fe])
+    run.click(lambda s: ",".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])
+    fe.change(lambda num: num + 1, inputs=total_changes, outputs=total_changes)
+
+    with gr.Row():
+        a = gr.Textbox(elem_id="input-box")
+        a.change(lambda x: x, inputs=[a])
+
+if __name__ == "__main__":
+    demo.launch()

demos/image_editor_events/run.ipynb

@@ -0,0 +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", "\n", "def predict(im):\n", "    return im[\"composite\"]\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", "    clear_btn = gr.Button(\"Clear\", 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", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/image_editor_events/run.py

@@ -0,0 +1,60 @@
+import gradio as gr
+
+def predict(im):
+    return im["composite"]
+
+with gr.Blocks() as demo:
+    with gr.Group():
+        with gr.Row():
+            im = gr.ImageEditor(
+                type="numpy",
+                crop_size="1:1",
+                elem_id="image_editor",
+            )
+            im_preview = gr.Image()
+    with gr.Group():
+        with gr.Row():
+
+            n_upload = gr.Label(
+                0,
+                label="upload",
+                elem_id="upload",
+            )
+            n_change = gr.Label(
+                0,
+                label="change",
+                elem_id="change",
+            )
+            n_input = gr.Label(
+                0,
+                label="input",
+                elem_id="input",
+            )
+            n_apply = gr.Label(
+                0,
+                label="apply",
+                elem_id="apply",
+            )
+    clear_btn = gr.Button("Clear", elem_id="clear")
+
+    im.upload(
+        lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress="hidden"
+    )
+    im.change(
+        lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress="hidden"
+    )
+    im.input(
+        lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress="hidden"
+    )
+    im.apply(
+        lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress="hidden"
+    )
+    im.change(predict, outputs=im_preview, inputs=im, show_progress="hidden")
+    clear_btn.click(
+        lambda: None,
+        None,
+        im,
+    )
+
+if __name__ == "__main__":
+    demo.launch()

demos/image_mod_default_image/run.ipynb

@@ -0,0 +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": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/lion.jpg\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/logo.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "def image_mod(image):\n", "    return image.rotate(45)\n", "\n", "cheetah = os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", "    flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", "        os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", "        os.path.join(os.path.abspath(''), \"images/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

@@ -0,0 +1,16 @@
+import gradio as gr
+import os
+
+def image_mod(image):
+    return image.rotate(45)
+
+cheetah = os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg")
+
+demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image",
+    flagging_options=["blurry", "incorrect", "other"], examples=[
+        os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
+        os.path.join(os.path.dirname(__file__), "images/logo.png")
+        ])
+
+if __name__ == "__main__":
+    demo.launch(max_file_size="70kb")

demos/image_segmentation/DESCRIPTION.md

@@ -0,0 +1 @@
+Simple image segmentation using gradio's AnnotatedImage component.

demos/image_segmentation/requirements.txt

@@ -0,0 +1 @@
+numpy

demos/image_segmentation/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_segmentation\n", "### Simple image segmentation using gradio's AnnotatedImage component.\n", "        "]}, {"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", "import random\n", "\n", "with gr.Blocks() as demo:\n", "    section_labels = [\n", "        \"apple\",\n", "        \"banana\",\n", "        \"carrot\",\n", "        \"donut\",\n", "        \"eggplant\",\n", "        \"fish\",\n", "        \"grapes\",\n", "        \"hamburger\",\n", "        \"ice cream\",\n", "        \"juice\",\n", "    ]\n", "\n", "    with gr.Row():\n", "        num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n", "        num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n", "\n", "    with gr.Row():\n", "        img_input = gr.Image()\n", "        img_output = gr.AnnotatedImage(\n", "            color_map={\"banana\": \"#a89a00\", \"carrot\": \"#ffae00\"}\n", "        )\n", "\n", "    section_btn = gr.Button(\"Identify Sections\")\n", "    selected_section = gr.Textbox(label=\"Selected Section\")\n", "\n", "    def section(img, num_boxes, num_segments):\n", "        sections = []\n", "        for a in range(num_boxes):\n", "            x = random.randint(0, img.shape[1])\n", "            y = random.randint(0, img.shape[0])\n", "            w = random.randint(0, img.shape[1] - x)\n", "            h = random.randint(0, img.shape[0] - y)\n", "            sections.append(((x, y, x + w, y + h), section_labels[a]))\n", "        for b in range(num_segments):\n", "            x = random.randint(0, img.shape[1])\n", "            y = random.randint(0, img.shape[0])\n", "            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n", "            mask = np.zeros(img.shape[:2])\n", "            for i in range(img.shape[0]):\n", "                for j in range(img.shape[1]):\n", "                    dist_square = (i - y) ** 2 + (j - x) ** 2\n", "                    if dist_square < r**2:\n", "                        mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n", "            sections.append((mask, section_labels[b + num_boxes]))\n", "        return (img, sections)\n", "\n", "    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n", "\n", "    def select_section(evt: gr.SelectData):\n", "        return section_labels[evt.index]\n", "\n", "    img_output.select(select_section, None, selected_section)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/image_segmentation/run.py

@@ -0,0 +1,61 @@
+import gradio as gr
+import numpy as np
+import random
+
+with gr.Blocks() as demo:
+    section_labels = [
+        "apple",
+        "banana",
+        "carrot",
+        "donut",
+        "eggplant",
+        "fish",
+        "grapes",
+        "hamburger",
+        "ice cream",
+        "juice",
+    ]
+
+    with gr.Row():
+        num_boxes = gr.Slider(0, 5, 2, step=1, label="Number of boxes")
+        num_segments = gr.Slider(0, 5, 1, step=1, label="Number of segments")
+
+    with gr.Row():
+        img_input = gr.Image()
+        img_output = gr.AnnotatedImage(
+            color_map={"banana": "#a89a00", "carrot": "#ffae00"}
+        )
+
+    section_btn = gr.Button("Identify Sections")
+    selected_section = gr.Textbox(label="Selected Section")
+
+    def section(img, num_boxes, num_segments):
+        sections = []
+        for a in range(num_boxes):
+            x = random.randint(0, img.shape[1])
+            y = random.randint(0, img.shape[0])
+            w = random.randint(0, img.shape[1] - x)
+            h = random.randint(0, img.shape[0] - y)
+            sections.append(((x, y, x + w, y + h), section_labels[a]))
+        for b in range(num_segments):
+            x = random.randint(0, img.shape[1])
+            y = random.randint(0, img.shape[0])
+            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
+            mask = np.zeros(img.shape[:2])
+            for i in range(img.shape[0]):
+                for j in range(img.shape[1]):
+                    dist_square = (i - y) ** 2 + (j - x) ** 2
+                    if dist_square < r**2:
+                        mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
+            sections.append((mask, section_labels[b + num_boxes]))
+        return (img, sections)
+
+    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)
+
+    def select_section(evt: gr.SelectData):
+        return section_labels[evt.index]
+
+    img_output.select(select_section, None, selected_section)
+
+if __name__ == "__main__":
+    demo.launch()

demos/interface_random_slider/run.ipynb

@@ -0,0 +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}

demos/interface_random_slider/run.py

@@ -0,0 +1,20 @@
+import gradio as gr
+
+def func(slider_1, slider_2, *args):
+    return slider_1 + slider_2 * 5
+
+demo = gr.Interface(
+    func,
+    [
+        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"),
+        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"),
+        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"),
+        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"),
+        gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"),
+        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),
+    ],
+    "number",
+)
+
+if __name__ == "__main__":
+    demo.launch()

demos/kitchen_sink/files/time.csv

@@ -0,0 +1,8 @@
+time,value,price
+1,1,4
+2,3,8
+3,6,12
+4,10,16
+5,15,20
+6,21,24
+7,28,28

demos/kitchen_sink/files/titanic.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
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demos/kitchen_sink/requirements.txt

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demos/kitchen_sink/run.ipynb

@@ -0,0 +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": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cantina.wav\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cheetah1.jpg\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/lion.jpg\n", "!wget -q -O files/logo.png https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/logo.png\n", "!wget -q -O files/time.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/time.csv\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/titanic.csv\n", "!wget -q -O files/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/tower.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\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", "):\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 os.path.join(os.path.abspath(''), \"files/cantina.wav\"),  # Audio\n", "        np.flipud(im1)\n", "        if im1 is not None\n", "        else os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),  # Image\n", "        video\n", "        if video is not None\n", "        else os.path.join(os.path.abspath(''), \"files/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", "        os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", "        df1,  # Dataframe\n", "        np.random.randint(0, 10, (4, 4)),  # Dataframe\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", "    ],\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\"),\n", "        gr.HTML(label=\"HTML\"),\n", "        gr.File(label=\"File\"),\n", "        gr.Dataframe(label=\"Dataframe\"),\n", "        gr.Dataframe(label=\"Numpy\"),\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", "            os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            # os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            # os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            os.path.join(os.path.abspath(''), \"files/world.mp4\"),\n", "            os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", "            os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", "            os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", "            [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\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}

demos/kitchen_sink/run.py

@@ -0,0 +1,159 @@
+import os
+import json
+
+import numpy as np
+
+import gradio as gr
+
+CHOICES = ["foo", "bar", "baz"]
+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" }]}]}}"""
+
+def fn(
+    text1,
+    text2,
+    num,
+    slider1,
+    slider2,
+    single_checkbox,
+    checkboxes,
+    radio,
+    dropdown,
+    multi_dropdown,
+    im1,
+    # im2,
+    # im3,
+    im4,
+    video,
+    audio1,
+    audio2,
+    file,
+    df1,
+):
+    return (
+        (text1 if single_checkbox else text2)
+        + ", selected:"
+        + ", ".join(checkboxes),  # Text
+        {
+            "positive": num / (num + slider1 + slider2),
+            "negative": slider1 / (num + slider1 + slider2),
+            "neutral": slider2 / (num + slider1 + slider2),
+        },  # Label
+        (audio1[0], np.flipud(audio1[1]))
+        if audio1 is not None
+        else os.path.join(os.path.dirname(__file__), "files/cantina.wav"),  # Audio
+        np.flipud(im1)
+        if im1 is not None
+        else os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),  # Image
+        video
+        if video is not None
+        else os.path.join(os.path.dirname(__file__), "files/world.mp4"),  # Video
+        [
+            ("The", "art"),
+            ("quick brown", "adj"),
+            ("fox", "nn"),
+            ("jumped", "vrb"),
+            ("testing testing testing", None),
+            ("over", "prp"),
+            ("the", "art"),
+            ("testing", None),
+            ("lazy", "adj"),
+            ("dogs", "nn"),
+            (".", "punc"),
+        ]
+        + [(f"test {x}", f"test {x}") for x in range(10)],  # HighlightedText
+        # [("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
+        [
+            ("The testing testing testing", None),
+            ("over", 0.6),
+            ("the", 0.2),
+            ("testing", None),
+            ("lazy", -0.1),
+            ("dogs", 0.4),
+            (".", 0),
+        ]
+        + [("test", x / 10) for x in range(-10, 10)],  # HighlightedText
+        json.loads(JSONOBJ),  # JSON
+        "<button style='background-color: red'>Click Me: "
+        + radio
+        + "</button>",  # HTML
+        os.path.join(os.path.dirname(__file__), "files/titanic.csv"),
+        df1,  # Dataframe
+        np.random.randint(0, 10, (4, 4)),  # Dataframe
+    )
+
+demo = gr.Interface(
+    fn,
+    inputs=[
+        gr.Textbox(value="Lorem ipsum", label="Textbox"),
+        gr.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"),
+        gr.Number(label="Number", value=42),
+        gr.Slider(10, 20, value=15, label="Slider: 10 - 20"),
+        gr.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"),
+        gr.Checkbox(label="Checkbox"),
+        gr.CheckboxGroup(label="CheckboxGroup", choices=CHOICES, value=CHOICES[0:2]),
+        gr.Radio(label="Radio", choices=CHOICES, value=CHOICES[2]),
+        gr.Dropdown(label="Dropdown", choices=CHOICES),
+        gr.Dropdown(
+            label="Multiselect Dropdown (Max choice: 2)",
+            choices=CHOICES,
+            multiselect=True,
+            max_choices=2,
+        ),
+        gr.Image(label="Image"),
+        # gr.Image(label="Image w/ Cropper", tool="select"),
+        # gr.Image(label="Sketchpad", source="canvas"),
+        gr.Image(label="Webcam", sources=["webcam"]),
+        gr.Video(label="Video"),
+        gr.Audio(label="Audio"),
+        gr.Audio(label="Microphone", sources=["microphone"]),
+        gr.File(label="File"),
+        gr.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]),
+    ],
+    outputs=[
+        gr.Textbox(label="Textbox"),
+        gr.Label(label="Label"),
+        gr.Audio(label="Audio"),
+        gr.Image(label="Image", elem_id="output-img"),
+        gr.Video(label="Video"),
+        gr.HighlightedText(
+            label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}
+        ),
+        gr.HighlightedText(label="HighlightedText", show_legend=True),
+        gr.JSON(label="JSON"),
+        gr.HTML(label="HTML"),
+        gr.File(label="File"),
+        gr.Dataframe(label="Dataframe"),
+        gr.Dataframe(label="Numpy"),
+    ],
+    examples=[
+        [
+            "the quick brown fox",
+            "jumps over the lazy dog",
+            10,
+            12,
+            4,
+            True,
+            ["foo", "baz"],
+            "baz",
+            "bar",
+            ["foo", "bar"],
+            os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            os.path.join(os.path.dirname(__file__), "files/world.mp4"),
+            os.path.join(os.path.dirname(__file__), "files/cantina.wav"),
+            os.path.join(os.path.dirname(__file__), "files/cantina.wav"),
+            os.path.join(os.path.dirname(__file__), "files/titanic.csv"),
+            [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],
+        ]
+    ]
+    * 3,
+    title="Kitchen Sink",
+    description="Try out all the components!",
+    article="Learn more about [Gradio](http://gradio.app)",
+    cache_examples=True,
+)
+
+if __name__ == "__main__":
+    demo.launch()

demos/kitchen_sink_random/constants.py

@@ -0,0 +1,65 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import random
+import os
+
+def random_plot():
+    start_year = 2020
+    x = np.arange(start_year, start_year + random.randint(0, 10))
+    year_count = x.shape[0]
+    plt_format = "-"
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    series = np.arange(0, year_count, dtype=float)
+    series = series**2
+    series += np.random.rand(year_count)
+    ax.plot(x, series, plt_format)
+    return fig
+
+img_dir = os.path.join(os.path.dirname(__file__), "files")
+file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files")
+model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files")
+highlighted_text_output_1 = [
+    {
+        "entity": "I-LOC",
+        "score": 0.9988978,
+        "index": 2,
+        "word": "Chicago",
+        "start": 5,
+        "end": 12,
+    },
+    {
+        "entity": "I-MISC",
+        "score": 0.9958592,
+        "index": 5,
+        "word": "Pakistani",
+        "start": 22,
+        "end": 31,
+    },
+]
+highlighted_text_output_2 = [
+    {
+        "entity": "I-LOC",
+        "score": 0.9988978,
+        "index": 2,
+        "word": "Chicago",
+        "start": 5,
+        "end": 12,
+    },
+    {
+        "entity": "I-LOC",
+        "score": 0.9958592,
+        "index": 5,
+        "word": "Pakistan",
+        "start": 22,
+        "end": 30,
+    },
+]
+
+highlighted_text = "Does Chicago have any Pakistani restaurants"
+
+def random_model3d():
+    model_3d = random.choice(
+        [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"]
+    )
+    return model_3d

demos/kitchen_sink_random/requirements.txt

@@ -0,0 +1,2 @@
+matplotlib
+pandas

demos/kitchen_sink_random/run.py

@@ -0,0 +1,97 @@
+import gradio as gr
+from datetime import datetime
+import random
+import string
+import os
+import pandas as pd
+
+from constants import (
+    file_dir,
+    img_dir,
+    highlighted_text,
+    highlighted_text_output_2,
+    highlighted_text_output_1,
+    random_plot,
+    random_model3d,
+)
+
+demo = gr.Interface(
+    lambda *args: args[0],
+    inputs=[
+        gr.Textbox(value=lambda: datetime.now(), label="Current Time"),
+        gr.Number(value=lambda: random.random(), label="Ranom Percentage"),
+        gr.Slider(minimum=-1, maximum=1, randomize=True, label="Slider with randomize"),
+        gr.Slider(
+            minimum=0,
+            maximum=1,
+            value=lambda: random.random(),
+            label="Slider with value func",
+        ),
+        gr.Checkbox(value=lambda: random.random() > 0.5, label="Random Checkbox"),
+        gr.CheckboxGroup(
+            choices=["a", "b", "c", "d"],
+            value=lambda: random.choice(["a", "b", "c", "d"]),
+            label="Random CheckboxGroup",
+        ),
+        gr.Radio(
+            choices=list(string.ascii_lowercase),
+            value=lambda: random.choice(string.ascii_lowercase),
+        ),
+        gr.Dropdown(
+            choices=["a", "b", "c", "d", "e"],
+            value=lambda: random.choice(["a", "b", "c"]),
+        ),
+        gr.Image(
+            value=lambda: random.choice(
+                [os.path.join(img_dir, img) for img in os.listdir(img_dir)]
+            )
+        ),
+        gr.Video(value=lambda: os.path.join(file_dir, "world.mp4")),
+        gr.Audio(value=lambda: os.path.join(file_dir, "cantina.wav")),
+        gr.File(
+            value=lambda: random.choice(
+                [os.path.join(file_dir, img) for img in os.listdir(file_dir)]
+            )
+        ),
+        gr.Dataframe(
+            value=lambda: pd.DataFrame(
+                {"random_number_rows": range(random.randint(0, 10))}
+            )
+        ),
+        gr.State(value=lambda: random.choice(string.ascii_lowercase)),
+        gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])),
+        gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])),
+        gr.HighlightedText(
+            value=lambda: random.choice(
+                [
+                    {"text": highlighted_text, "entities": highlighted_text_output_1},
+                    {"text": highlighted_text, "entities": highlighted_text_output_2},
+                ]
+            ),
+        ),
+        gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])),
+        gr.HTML(
+            value=lambda: random.choice(
+                [
+                    '<p style="color:red;">I am red</p>',
+                    '<p style="color:blue;">I am blue</p>',
+                ]
+            )
+        ),
+        gr.Gallery(
+            value=lambda: [os.path.join(img_dir, img) for img in os.listdir(img_dir)]
+        ),
+        gr.Chatbot(
+            value=lambda: random.choice([[("hello", "hi!")], [("bye", "goodbye!")]])
+        ),
+        gr.Model3D(value=random_model3d),
+        gr.Plot(value=random_plot),
+        gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"),
+    ],
+    outputs=[
+        gr.State(value=lambda: random.choice(string.ascii_lowercase))
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()

demos/matrix_transpose/run.ipynb

@@ -0,0 +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),\n", "    \"numpy\",\n", "    examples=[\n", "        [np.zeros((3, 3)).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

@@ -0,0 +1,23 @@
+import numpy as np
+
+import gradio as gr
+
+def transpose(matrix):
+    return matrix.T
+
+demo = gr.Interface(
+    transpose,
+    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
+    "numpy",
+    examples=[
+        [np.zeros((3, 3)).tolist()],
+        [np.ones((2, 2)).tolist()],
+        [np.random.randint(0, 10, (3, 10)).tolist()],
+        [np.random.randint(0, 10, (10, 3)).tolist()],
+        [np.random.randint(0, 10, (10, 10)).tolist()],
+    ],
+    cache_examples=False
+)
+
+if __name__ == "__main__":
+    demo.launch()

demos/mini_leaderboard/assets/custom_css.css

@@ -0,0 +1,87 @@
+/* Hides the final AutoEvalColumn */
+#llm-benchmark-tab-table table td:last-child,
+#llm-benchmark-tab-table table th:last-child {
+    display: none;
+}
+
+/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
+table td:first-child,
+table th:first-child {
+    max-width: 400px;
+    overflow: auto;
+    white-space: nowrap;
+}
+
+/* Full width space */
+.gradio-container {
+  max-width: 95%!important;
+}
+
+/* Text style and margins */
+.markdown-text {
+    font-size: 16px !important;
+}
+
+#models-to-add-text {
+    font-size: 18px !important;
+}
+
+#citation-button span {
+    font-size: 16px !important;
+}
+
+#citation-button textarea {
+    font-size: 16px !important;
+}
+
+#citation-button > label > button {
+    margin: 6px;
+    transform: scale(1.3);
+}
+
+#search-bar-table-box > div:first-child {
+    background: none;
+    border: none;
+}
+ 
+#search-bar {
+    padding: 0px;
+}
+
+.tab-buttons button {
+    font-size: 20px;
+}
+
+/* Filters style */
+#filter_type{
+    border: 0;
+    padding-left: 0;
+    padding-top: 0;
+}
+#filter_type label {
+    display: flex;
+}
+#filter_type label > span{
+    margin-top: var(--spacing-lg);
+    margin-right: 0.5em;
+}
+#filter_type label > .wrap{
+    width: 103px;
+}
+#filter_type label > .wrap .wrap-inner{  
+    padding: 2px;
+}
+#filter_type label > .wrap .wrap-inner input{
+    width: 1px
+}
+#filter-columns-type{
+    border:0;
+    padding:0.5;
+}
+#filter-columns-size{
+    border:0;
+    padding:0.5;
+}
+#box-filter > .form{
+    border: 0
+}

demos/mini_leaderboard/requirements.txt

@@ -0,0 +1 @@
+pandas

demos/mini_leaderboard/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: mini_leaderboard"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pandas "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('assets')\n", "!wget -q -O assets/__init__.py https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/__init__.py\n", "!wget -q -O assets/custom_css.css https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/custom_css.css\n", "!wget -q -O assets/leaderboard_data.json https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/leaderboard_data.json"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "from pathlib import Path\n", "\n", "abs_path = Path(__file__).parent.absolute()\n", "\n", "df = pd.read_json(str(abs_path / \"assets/leaderboard_data.json\"))\n", "invisible_df = df.copy()\n", "\n", "COLS = [\n", "    \"T\",\n", "    \"Model\",\n", "    \"Average \u2b06\ufe0f\",\n", "    \"ARC\",\n", "    \"HellaSwag\",\n", "    \"MMLU\",\n", "    \"TruthfulQA\",\n", "    \"Winogrande\",\n", "    \"GSM8K\",\n", "    \"Type\",\n", "    \"Architecture\",\n", "    \"Precision\",\n", "    \"Merged\",\n", "    \"Hub License\",\n", "    \"#Params (B)\",\n", "    \"Hub \u2764\ufe0f\",\n", "    \"Model sha\",\n", "    \"model_name_for_query\",\n", "]\n", "ON_LOAD_COLS = [\n", "    \"T\",\n", "    \"Model\",\n", "    \"Average \u2b06\ufe0f\",\n", "    \"ARC\",\n", "    \"HellaSwag\",\n", "    \"MMLU\",\n", "    \"TruthfulQA\",\n", "    \"Winogrande\",\n", "    \"GSM8K\",\n", "    \"model_name_for_query\",\n", "]\n", "TYPES = [\n", "    \"str\",\n", "    \"markdown\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"str\",\n", "    \"str\",\n", "    \"str\",\n", "    \"str\",\n", "    \"bool\",\n", "    \"str\",\n", "    \"number\",\n", "    \"number\",\n", "    \"bool\",\n", "    \"str\",\n", "    \"bool\",\n", "    \"bool\",\n", "    \"str\",\n", "]\n", "NUMERIC_INTERVALS = {\n", "    \"?\": pd.Interval(-1, 0, closed=\"right\"),\n", "    \"~1.5\": pd.Interval(0, 2, closed=\"right\"),\n", "    \"~3\": pd.Interval(2, 4, closed=\"right\"),\n", "    \"~7\": pd.Interval(4, 9, closed=\"right\"),\n", "    \"~13\": pd.Interval(9, 20, closed=\"right\"),\n", "    \"~35\": pd.Interval(20, 45, closed=\"right\"),\n", "    \"~60\": pd.Interval(45, 70, closed=\"right\"),\n", "    \"70+\": pd.Interval(70, 10000, closed=\"right\"),\n", "}\n", "MODEL_TYPE = [str(s) for s in df[\"T\"].unique()]\n", "Precision = [str(s) for s in df[\"Precision\"].unique()]\n", "\n", "# Searching and filtering\n", "def update_table(\n", "    hidden_df: pd.DataFrame,\n", "    columns: list,\n", "    type_query: list,\n", "    precision_query: str,\n", "    size_query: list,\n", "    query: str,\n", "):\n", "    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)  # type: ignore\n", "    filtered_df = filter_queries(query, filtered_df)\n", "    df = select_columns(filtered_df, columns)\n", "    return df\n", "\n", "def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:\n", "    return df[(df[\"model_name_for_query\"].str.contains(query, case=False))]  # type: ignore\n", "\n", "def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:\n", "    # We use COLS to maintain sorting\n", "    filtered_df = df[[c for c in COLS if c in df.columns and c in columns]]\n", "    return filtered_df  # type: ignore\n", "\n", "def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:\n", "    final_df = []\n", "    if query != \"\":\n", "        queries = [q.strip() for q in query.split(\";\")]\n", "        for _q in queries:\n", "            _q = _q.strip()\n", "            if _q != \"\":\n", "                temp_filtered_df = search_table(filtered_df, _q)\n", "                if len(temp_filtered_df) > 0:\n", "                    final_df.append(temp_filtered_df)\n", "        if len(final_df) > 0:\n", "            filtered_df = pd.concat(final_df)\n", "            filtered_df = filtered_df.drop_duplicates(  # type: ignore\n", "                subset=[\"Model\", \"Precision\", \"Model sha\"]\n", "            )\n", "\n", "    return filtered_df\n", "\n", "def filter_models(\n", "    df: pd.DataFrame,\n", "    type_query: list,\n", "    size_query: list,\n", "    precision_query: list,\n", ") -> pd.DataFrame:\n", "    # Show all models\n", "    filtered_df = df\n", "\n", "    type_emoji = [t[0] for t in type_query]\n", "    filtered_df = filtered_df.loc[df[\"T\"].isin(type_emoji)]\n", "    filtered_df = filtered_df.loc[df[\"Precision\"].isin(precision_query + [\"None\"])]\n", "\n", "    numeric_interval = pd.IntervalIndex(\n", "        sorted([NUMERIC_INTERVALS[s] for s in size_query])  # type: ignore\n", "    )\n", "    params_column = pd.to_numeric(df[\"#Params (B)\"], errors=\"coerce\")\n", "    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))  # type: ignore\n", "    filtered_df = filtered_df.loc[mask]\n", "\n", "    return filtered_df\n", "\n", "demo = gr.Blocks(css=str(abs_path / \"assets/leaderboard_data.json\"))\n", "with demo:\n", "    gr.Markdown(\"\"\"Test Space of the LLM Leaderboard\"\"\", elem_classes=\"markdown-text\")\n", "\n", "    with gr.Tabs(elem_classes=\"tab-buttons\") as tabs:\n", "        with gr.TabItem(\"\ud83c\udfc5 LLM Benchmark\", elem_id=\"llm-benchmark-tab-table\", id=0):\n", "            with gr.Row():\n", "                with gr.Column():\n", "                    with gr.Row():\n", "                        search_bar = gr.Textbox(\n", "                            placeholder=\" \ud83d\udd0d Search for your model (separate multiple queries with `;`) and press ENTER...\",\n", "                            show_label=False,\n", "                            elem_id=\"search-bar\",\n", "                        )\n", "                    with gr.Row():\n", "                        shown_columns = gr.CheckboxGroup(\n", "                            choices=COLS,\n", "                            value=ON_LOAD_COLS,\n", "                            label=\"Select columns to show\",\n", "                            elem_id=\"column-select\",\n", "                            interactive=True,\n", "                        )\n", "                with gr.Column(min_width=320):\n", "                    filter_columns_type = gr.CheckboxGroup(\n", "                        label=\"Model types\",\n", "                        choices=MODEL_TYPE,\n", "                        value=MODEL_TYPE,\n", "                        interactive=True,\n", "                        elem_id=\"filter-columns-type\",\n", "                    )\n", "                    filter_columns_precision = gr.CheckboxGroup(\n", "                        label=\"Precision\",\n", "                        choices=Precision,\n", "                        value=Precision,\n", "                        interactive=True,\n", "                        elem_id=\"filter-columns-precision\",\n", "                    )\n", "                    filter_columns_size = gr.CheckboxGroup(\n", "                        label=\"Model sizes (in billions of parameters)\",\n", "                        choices=list(NUMERIC_INTERVALS.keys()),\n", "                        value=list(NUMERIC_INTERVALS.keys()),\n", "                        interactive=True,\n", "                        elem_id=\"filter-columns-size\",\n", "                    )\n", "\n", "            leaderboard_table = gr.components.Dataframe(\n", "                value=df[ON_LOAD_COLS],  # type: ignore\n", "                headers=ON_LOAD_COLS,\n", "                datatype=TYPES,\n", "                elem_id=\"leaderboard-table\",\n", "                interactive=False,\n", "                visible=True,\n", "                column_widths=[\"2%\", \"33%\"],\n", "            )\n", "\n", "            # Dummy leaderboard for handling the case when the user uses backspace key\n", "            hidden_leaderboard_table_for_search = gr.components.Dataframe(\n", "                value=invisible_df[COLS],  # type: ignore\n", "                headers=COLS,\n", "                datatype=TYPES,\n", "                visible=False,\n", "            )\n", "            search_bar.submit(\n", "                update_table,\n", "                [\n", "                    hidden_leaderboard_table_for_search,\n", "                    shown_columns,\n", "                    filter_columns_type,\n", "                    filter_columns_precision,\n", "                    filter_columns_size,\n", "                    search_bar,\n", "                ],\n", "                leaderboard_table,\n", "            )\n", "            for selector in [\n", "                shown_columns,\n", "                filter_columns_type,\n", "                filter_columns_precision,\n", "                filter_columns_size,\n", "            ]:\n", "                selector.change(\n", "                    update_table,\n", "                    [\n", "                        hidden_leaderboard_table_for_search,\n", "                        shown_columns,\n", "                        filter_columns_type,\n", "                        filter_columns_precision,\n", "                        filter_columns_size,\n", "                        search_bar,\n", "                    ],\n", "                    leaderboard_table,\n", "                    queue=True,\n", "                )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.queue(default_concurrency_limit=40).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/mini_leaderboard/run.py

@@ -0,0 +1,236 @@
+import gradio as gr
+import pandas as pd
+from pathlib import Path
+
+abs_path = Path(__file__).parent.absolute()
+
+df = pd.read_json(str(abs_path / "assets/leaderboard_data.json"))
+invisible_df = df.copy()
+
+COLS = [
+    "T",
+    "Model",
+    "Average ⬆️",
+    "ARC",
+    "HellaSwag",
+    "MMLU",
+    "TruthfulQA",
+    "Winogrande",
+    "GSM8K",
+    "Type",
+    "Architecture",
+    "Precision",
+    "Merged",
+    "Hub License",
+    "#Params (B)",
+    "Hub ❤️",
+    "Model sha",
+    "model_name_for_query",
+]
+ON_LOAD_COLS = [
+    "T",
+    "Model",
+    "Average ⬆️",
+    "ARC",
+    "HellaSwag",
+    "MMLU",
+    "TruthfulQA",
+    "Winogrande",
+    "GSM8K",
+    "model_name_for_query",
+]
+TYPES = [
+    "str",
+    "markdown",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "str",
+    "str",
+    "str",
+    "str",
+    "bool",
+    "str",
+    "number",
+    "number",
+    "bool",
+    "str",
+    "bool",
+    "bool",
+    "str",
+]
+NUMERIC_INTERVALS = {
+    "?": pd.Interval(-1, 0, closed="right"),
+    "~1.5": pd.Interval(0, 2, closed="right"),
+    "~3": pd.Interval(2, 4, closed="right"),
+    "~7": pd.Interval(4, 9, closed="right"),
+    "~13": pd.Interval(9, 20, closed="right"),
+    "~35": pd.Interval(20, 45, closed="right"),
+    "~60": pd.Interval(45, 70, closed="right"),
+    "70+": pd.Interval(70, 10000, closed="right"),
+}
+MODEL_TYPE = [str(s) for s in df["T"].unique()]
+Precision = [str(s) for s in df["Precision"].unique()]
+
+# Searching and filtering
+def update_table(
+    hidden_df: pd.DataFrame,
+    columns: list,
+    type_query: list,
+    precision_query: str,
+    size_query: list,
+    query: str,
+):
+    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)  # type: ignore
+    filtered_df = filter_queries(query, filtered_df)
+    df = select_columns(filtered_df, columns)
+    return df
+
+def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    return df[(df["model_name_for_query"].str.contains(query, case=False))]  # type: ignore
+
+def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
+    # We use COLS to maintain sorting
+    filtered_df = df[[c for c in COLS if c in df.columns and c in columns]]
+    return filtered_df  # type: ignore
+
+def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
+    final_df = []
+    if query != "":
+        queries = [q.strip() for q in query.split(";")]
+        for _q in queries:
+            _q = _q.strip()
+            if _q != "":
+                temp_filtered_df = search_table(filtered_df, _q)
+                if len(temp_filtered_df) > 0:
+                    final_df.append(temp_filtered_df)
+        if len(final_df) > 0:
+            filtered_df = pd.concat(final_df)
+            filtered_df = filtered_df.drop_duplicates(  # type: ignore
+                subset=["Model", "Precision", "Model sha"]
+            )
+
+    return filtered_df
+
+def filter_models(
+    df: pd.DataFrame,
+    type_query: list,
+    size_query: list,
+    precision_query: list,
+) -> pd.DataFrame:
+    # Show all models
+    filtered_df = df
+
+    type_emoji = [t[0] for t in type_query]
+    filtered_df = filtered_df.loc[df["T"].isin(type_emoji)]
+    filtered_df = filtered_df.loc[df["Precision"].isin(precision_query + ["None"])]
+
+    numeric_interval = pd.IntervalIndex(
+        sorted([NUMERIC_INTERVALS[s] for s in size_query])  # type: ignore
+    )
+    params_column = pd.to_numeric(df["#Params (B)"], errors="coerce")
+    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))  # type: ignore
+    filtered_df = filtered_df.loc[mask]
+
+    return filtered_df
+
+demo = gr.Blocks(css=str(abs_path / "assets/leaderboard_data.json"))
+with demo:
+    gr.Markdown("""Test Space of the LLM Leaderboard""", elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        search_bar = gr.Textbox(
+                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
+                            show_label=False,
+                            elem_id="search-bar",
+                        )
+                    with gr.Row():
+                        shown_columns = gr.CheckboxGroup(
+                            choices=COLS,
+                            value=ON_LOAD_COLS,
+                            label="Select columns to show",
+                            elem_id="column-select",
+                            interactive=True,
+                        )
+                with gr.Column(min_width=320):
+                    filter_columns_type = gr.CheckboxGroup(
+                        label="Model types",
+                        choices=MODEL_TYPE,
+                        value=MODEL_TYPE,
+                        interactive=True,
+                        elem_id="filter-columns-type",
+                    )
+                    filter_columns_precision = gr.CheckboxGroup(
+                        label="Precision",
+                        choices=Precision,
+                        value=Precision,
+                        interactive=True,
+                        elem_id="filter-columns-precision",
+                    )
+                    filter_columns_size = gr.CheckboxGroup(
+                        label="Model sizes (in billions of parameters)",
+                        choices=list(NUMERIC_INTERVALS.keys()),
+                        value=list(NUMERIC_INTERVALS.keys()),
+                        interactive=True,
+                        elem_id="filter-columns-size",
+                    )
+
+            leaderboard_table = gr.components.Dataframe(
+                value=df[ON_LOAD_COLS],  # type: ignore
+                headers=ON_LOAD_COLS,
+                datatype=TYPES,
+                elem_id="leaderboard-table",
+                interactive=False,
+                visible=True,
+                column_widths=["2%", "33%"],
+            )
+
+            # Dummy leaderboard for handling the case when the user uses backspace key
+            hidden_leaderboard_table_for_search = gr.components.Dataframe(
+                value=invisible_df[COLS],  # type: ignore
+                headers=COLS,
+                datatype=TYPES,
+                visible=False,
+            )
+            search_bar.submit(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    search_bar,
+                ],
+                leaderboard_table,
+            )
+            for selector in [
+                shown_columns,
+                filter_columns_type,
+                filter_columns_precision,
+                filter_columns_size,
+            ]:
+                selector.change(
+                    update_table,
+                    [
+                        hidden_leaderboard_table_for_search,
+                        shown_columns,
+                        filter_columns_type,
+                        filter_columns_precision,
+                        filter_columns_size,
+                        search_bar,
+                    ],
+                    leaderboard_table,
+                    queue=True,
+                )
+
+if __name__ == "__main__":
+    demo.queue(default_concurrency_limit=40).launch()

demos/model3D/files/face.obj

@@ -0,0 +1,2471 @@
+####
+#
+# OBJ File Generated by Meshlab
+#
+####
+# Object face.obj
+#
+# Vertices: 845
+# Faces: 1610
+#
+####
+v 54.126293 -49.502399 71.230705 0.384314 0.254902 0.200000
+v 51.424591 -54.578999 75.431709 0.337255 0.227451 0.172549
+v 44.556496 -61.237099 65.307907 0.384314 0.254902 0.192157
+v 18.416197 -75.205498 35.343601 0.529412 0.376471 0.298039
+v 13.523697 -77.731102 34.633598 0.513725 0.364706 0.290196
+v 11.454597 -74.201599 29.775103 0.639216 0.454902 0.372549
+v 5.855427 -77.386299 30.315100 0.603922 0.435294 0.356863
+v -0.378855 -77.287903 29.743700 0.611765 0.443137 0.360784
+v 6.812867 -79.041397 33.545300 0.505882 0.368627 0.298039
+v 21.881296 -70.810898 34.982002 0.588235 0.415686 0.329412
+v 17.653797 -69.034798 30.263802 0.674510 0.478431 0.388235
+v 39.436996 -66.435501 64.968597 0.164706 0.109804 0.082353
+v 33.808598 -70.338699 57.717205 0.364706 0.247059 0.184314
+v 31.909595 -68.409302 47.974705 0.439216 0.301961 0.227451
+v 38.451797 -64.744904 55.703503 0.427451 0.294118 0.219608
+v 28.802397 -72.939499 50.408905 0.372549 0.254902 0.192157
+v 22.955397 -75.486603 43.228905 0.400000 0.274510 0.215686
+v 26.511995 -70.622101 41.049904 0.494118 0.341176 0.262745
+v 24.465696 -76.217796 49.972805 0.345098 0.239216 0.184314
+v 15.571197 -78.986099 40.602001 0.396078 0.278431 0.219608
+v 17.437696 -79.466103 47.124001 0.352941 0.247059 0.196078
+v 28.846695 -74.018898 56.477203 0.164706 0.113725 0.086275
+v 9.321696 -81.938202 48.001999 0.172549 0.129412 0.105882
+v -0.898453 -82.647102 47.089699 0.172549 0.129412 0.105882
+v -0.725759 -82.110100 43.572498 0.400000 0.301961 0.254902
+v 18.763496 -79.316498 51.133400 0.172549 0.117647 0.094118
+v 8.731546 -81.557503 44.441002 0.392157 0.286275 0.235294
+v 27.614496 -65.079002 38.432205 0.596078 0.419608 0.321569
+v 33.274998 -61.490501 43.398605 0.568627 0.400000 0.301961
+v 38.784996 -60.440899 50.047802 0.513725 0.356863 0.266667
+v 42.862595 -58.651798 55.019802 0.490196 0.337255 0.250980
+v 7.726847 -80.575996 38.638500 0.415686 0.301961 0.247059
+v -0.490965 -80.930901 37.912300 0.427451 0.317647 0.262745
+v -0.419731 -79.354401 33.152100 0.513725 0.376471 0.305882
+v -0.332592 -73.782600 27.050400 0.705882 0.505882 0.415686
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+# 1610 faces, 0 coords texture
+
+# End of File

demos/model3D/files/source.txt

@@ -0,0 +1,13 @@
+Stanford Bunny:
+https://graphics.stanford.edu/data/3Dscanrep/
+https://graphics.stanford.edu/~mdfisher/Data/Meshes/bunny.obj
+
+Duck & Fox:
+https://github.com/KhronosGroup/glTF-Sample-Models
+
+Face:
+https://github.com/mikedh/trimesh/tree/main/models
+
+NASA SOFIA:
+https://nasa3d.arc.nasa.gov/detail/sofia
+https://github.com/nasa/NASA-3D-Resources/blob/master/3D%20Models/SOFIA/Fuselage_top.stl