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  1. .gitattributes +1 -0
  2. demos/file_explorer_component_events/dir3/dir4/dir_4_foo.txt +0 -0
  3. demos/file_explorer_component_events/run.ipynb +1 -0
  4. demos/file_explorer_component_events/run.py +36 -0
  5. demos/image_editor_events/run.ipynb +1 -0
  6. demos/image_editor_events/run.py +60 -0
  7. demos/image_mod_default_image/images/cheetah1.jpg +0 -0
  8. demos/image_mod_default_image/images/lion.jpg +0 -0
  9. demos/image_mod_default_image/images/logo.png +0 -0
  10. demos/image_mod_default_image/run.ipynb +1 -0
  11. demos/image_mod_default_image/run.py +16 -0
  12. demos/image_segmentation/DESCRIPTION.md +1 -0
  13. demos/image_segmentation/requirements.txt +1 -0
  14. demos/image_segmentation/run.ipynb +1 -0
  15. demos/image_segmentation/run.py +61 -0
  16. demos/interface_random_slider/run.ipynb +1 -0
  17. demos/interface_random_slider/run.py +20 -0
  18. demos/kitchen_sink/files/cantina.wav +0 -0
  19. demos/kitchen_sink/files/cheetah1.jpg +0 -0
  20. demos/kitchen_sink/files/lion.jpg +0 -0
  21. demos/kitchen_sink/files/logo.png +0 -0
  22. demos/kitchen_sink/files/time.csv +8 -0
  23. demos/kitchen_sink/files/titanic.csv +892 -0
  24. demos/kitchen_sink/files/tower.jpg +0 -0
  25. demos/kitchen_sink/files/world.mp4 +3 -0
  26. demos/kitchen_sink/requirements.txt +1 -0
  27. demos/kitchen_sink/run.ipynb +1 -0
  28. demos/kitchen_sink/run.py +159 -0
  29. demos/kitchen_sink_random/__init__.py +0 -0
  30. demos/kitchen_sink_random/constants.py +65 -0
  31. demos/kitchen_sink_random/files/cheetah1.jpeg +0 -0
  32. demos/kitchen_sink_random/files/cheetah1.jpg +0 -0
  33. demos/kitchen_sink_random/files/lion.jpg +0 -0
  34. demos/kitchen_sink_random/requirements.txt +2 -0
  35. demos/kitchen_sink_random/run.py +97 -0
  36. demos/matrix_transpose/run.ipynb +1 -0
  37. demos/matrix_transpose/run.py +23 -0
  38. demos/matrix_transpose/screenshot.png +0 -0
  39. demos/mini_leaderboard/assets/__init__.py +0 -0
  40. demos/mini_leaderboard/assets/custom_css.css +87 -0
  41. demos/mini_leaderboard/assets/leaderboard_data.json +0 -0
  42. demos/mini_leaderboard/requirements.txt +1 -0
  43. demos/mini_leaderboard/run.ipynb +1 -0
  44. demos/mini_leaderboard/run.py +236 -0
  45. demos/model3D/files/Bunny.obj +0 -0
  46. demos/model3D/files/Duck.glb +0 -0
  47. demos/model3D/files/Fox.gltf +0 -0
  48. demos/model3D/files/face.obj +2471 -0
  49. demos/model3D/files/sofia.stl +0 -0
  50. demos/model3D/files/source.txt +13 -0
.gitattributes CHANGED
@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  demos/calculator/screenshot.gif filter=lfs diff=lfs merge=lfs -text
 
 
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  demos/calculator/screenshot.gif filter=lfs diff=lfs merge=lfs -text
37
+ demos/kitchen_sink/files/world.mp4 filter=lfs diff=lfs merge=lfs -text
demos/file_explorer_component_events/dir3/dir4/dir_4_foo.txt ADDED
File without changes
demos/file_explorer_component_events/run.ipynb ADDED
@@ -0,0 +1 @@
 
 
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 ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from pathlib import Path
3
+
4
+ base_root = Path(__file__).parent.resolve()
5
+
6
+ with gr.Blocks() as demo:
7
+ with gr.Row():
8
+ dd = gr.Dropdown(label="Select File Explorer Root",
9
+ value=str(base_root / "dir1"),
10
+ choices=[str(base_root / "dir1"), str(base_root / "dir2"),
11
+ str(base_root / "dir3")])
12
+ with gr.Group():
13
+ txt_only_glob = gr.Checkbox(label="Show only text files", value=False)
14
+ ignore_txt_in_glob = gr.Checkbox(label="Ignore text files in glob", value=False)
15
+
16
+ fe = gr.FileExplorer(root_dir=str(base_root / "dir1"),
17
+ glob="**/*", interactive=True)
18
+ textbox = gr.Textbox(label="Selected Directory")
19
+ run = gr.Button("Run")
20
+ total_changes = gr.Number(0, elem_id="total-changes")
21
+
22
+ txt_only_glob.select(lambda s: gr.FileExplorer(glob="*.txt" if s else "*") ,
23
+ inputs=[txt_only_glob], outputs=[fe])
24
+ ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob="*.txt" if s else None),
25
+ inputs=[ignore_txt_in_glob], outputs=[fe])
26
+
27
+ dd.select(lambda s: gr.FileExplorer(root_dir=s), inputs=[dd], outputs=[fe])
28
+ run.click(lambda s: ",".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])
29
+ fe.change(lambda num: num + 1, inputs=total_changes, outputs=total_changes)
30
+
31
+ with gr.Row():
32
+ a = gr.Textbox(elem_id="input-box")
33
+ a.change(lambda x: x, inputs=[a])
34
+
35
+ if __name__ == "__main__":
36
+ demo.launch()
demos/image_editor_events/run.ipynb ADDED
@@ -0,0 +1 @@
 
 
1
+ {"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\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 ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+
3
+ def predict(im):
4
+ return im["composite"]
5
+
6
+ with gr.Blocks() as demo:
7
+ with gr.Group():
8
+ with gr.Row():
9
+ im = gr.ImageEditor(
10
+ type="numpy",
11
+ crop_size="1:1",
12
+ elem_id="image_editor",
13
+ )
14
+ im_preview = gr.Image()
15
+ with gr.Group():
16
+ with gr.Row():
17
+
18
+ n_upload = gr.Label(
19
+ 0,
20
+ label="upload",
21
+ elem_id="upload",
22
+ )
23
+ n_change = gr.Label(
24
+ 0,
25
+ label="change",
26
+ elem_id="change",
27
+ )
28
+ n_input = gr.Label(
29
+ 0,
30
+ label="input",
31
+ elem_id="input",
32
+ )
33
+ n_apply = gr.Label(
34
+ 0,
35
+ label="apply",
36
+ elem_id="apply",
37
+ )
38
+ clear_btn = gr.Button("Clear", elem_id="clear")
39
+
40
+ im.upload(
41
+ lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress="hidden"
42
+ )
43
+ im.change(
44
+ lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress="hidden"
45
+ )
46
+ im.input(
47
+ lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress="hidden"
48
+ )
49
+ im.apply(
50
+ lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress="hidden"
51
+ )
52
+ im.change(predict, outputs=im_preview, inputs=im, show_progress="hidden")
53
+ clear_btn.click(
54
+ lambda: None,
55
+ None,
56
+ im,
57
+ )
58
+
59
+ if __name__ == "__main__":
60
+ demo.launch()
demos/image_mod_default_image/images/cheetah1.jpg ADDED
demos/image_mod_default_image/images/lion.jpg ADDED
demos/image_mod_default_image/images/logo.png ADDED
demos/image_mod_default_image/run.ipynb ADDED
@@ -0,0 +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": ["# 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 ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import os
3
+
4
+ def image_mod(image):
5
+ return image.rotate(45)
6
+
7
+ cheetah = os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg")
8
+
9
+ demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image",
10
+ flagging_options=["blurry", "incorrect", "other"], examples=[
11
+ os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
12
+ os.path.join(os.path.dirname(__file__), "images/logo.png")
13
+ ])
14
+
15
+ if __name__ == "__main__":
16
+ demo.launch(max_file_size="70kb")
demos/image_segmentation/DESCRIPTION.md ADDED
@@ -0,0 +1 @@
 
 
1
+ Simple image segmentation using gradio's AnnotatedImage component.
demos/image_segmentation/requirements.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ numpy
demos/image_segmentation/run.ipynb ADDED
@@ -0,0 +1 @@
 
 
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 ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import numpy as np
3
+ import random
4
+
5
+ with gr.Blocks() as demo:
6
+ section_labels = [
7
+ "apple",
8
+ "banana",
9
+ "carrot",
10
+ "donut",
11
+ "eggplant",
12
+ "fish",
13
+ "grapes",
14
+ "hamburger",
15
+ "ice cream",
16
+ "juice",
17
+ ]
18
+
19
+ with gr.Row():
20
+ num_boxes = gr.Slider(0, 5, 2, step=1, label="Number of boxes")
21
+ num_segments = gr.Slider(0, 5, 1, step=1, label="Number of segments")
22
+
23
+ with gr.Row():
24
+ img_input = gr.Image()
25
+ img_output = gr.AnnotatedImage(
26
+ color_map={"banana": "#a89a00", "carrot": "#ffae00"}
27
+ )
28
+
29
+ section_btn = gr.Button("Identify Sections")
30
+ selected_section = gr.Textbox(label="Selected Section")
31
+
32
+ def section(img, num_boxes, num_segments):
33
+ sections = []
34
+ for a in range(num_boxes):
35
+ x = random.randint(0, img.shape[1])
36
+ y = random.randint(0, img.shape[0])
37
+ w = random.randint(0, img.shape[1] - x)
38
+ h = random.randint(0, img.shape[0] - y)
39
+ sections.append(((x, y, x + w, y + h), section_labels[a]))
40
+ for b in range(num_segments):
41
+ x = random.randint(0, img.shape[1])
42
+ y = random.randint(0, img.shape[0])
43
+ r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
44
+ mask = np.zeros(img.shape[:2])
45
+ for i in range(img.shape[0]):
46
+ for j in range(img.shape[1]):
47
+ dist_square = (i - y) ** 2 + (j - x) ** 2
48
+ if dist_square < r**2:
49
+ mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
50
+ sections.append((mask, section_labels[b + num_boxes]))
51
+ return (img, sections)
52
+
53
+ section_btn.click(section, [img_input, num_boxes, num_segments], img_output)
54
+
55
+ def select_section(evt: gr.SelectData):
56
+ return section_labels[evt.index]
57
+
58
+ img_output.select(select_section, None, selected_section)
59
+
60
+ if __name__ == "__main__":
61
+ demo.launch()
demos/interface_random_slider/run.ipynb ADDED
@@ -0,0 +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}
demos/interface_random_slider/run.py ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+
3
+ def func(slider_1, slider_2, *args):
4
+ return slider_1 + slider_2 * 5
5
+
6
+ demo = gr.Interface(
7
+ func,
8
+ [
9
+ gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"),
10
+ gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"),
11
+ gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"),
12
+ gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"),
13
+ gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"),
14
+ gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),
15
+ ],
16
+ "number",
17
+ )
18
+
19
+ if __name__ == "__main__":
20
+ demo.launch()
demos/kitchen_sink/files/cantina.wav ADDED
Binary file (132 kB). View file
 
demos/kitchen_sink/files/cheetah1.jpg ADDED
demos/kitchen_sink/files/lion.jpg ADDED
demos/kitchen_sink/files/logo.png ADDED
demos/kitchen_sink/files/time.csv ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ time,value,price
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+ 1,1,4
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+ 2,3,8
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+ 3,6,12
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+ 4,10,16
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+ 5,15,20
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+ 6,21,24
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+ 7,28,28
demos/kitchen_sink/files/titanic.csv ADDED
@@ -0,0 +1,892 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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demos/kitchen_sink/files/tower.jpg ADDED
demos/kitchen_sink/files/world.mp4 ADDED
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demos/kitchen_sink/requirements.txt ADDED
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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 ADDED
@@ -0,0 +1,159 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import json
3
+
4
+ import numpy as np
5
+
6
+ import gradio as gr
7
+
8
+ CHOICES = ["foo", "bar", "baz"]
9
+ 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" }]}]}}"""
10
+
11
+ def fn(
12
+ text1,
13
+ text2,
14
+ num,
15
+ slider1,
16
+ slider2,
17
+ single_checkbox,
18
+ checkboxes,
19
+ radio,
20
+ dropdown,
21
+ multi_dropdown,
22
+ im1,
23
+ # im2,
24
+ # im3,
25
+ im4,
26
+ video,
27
+ audio1,
28
+ audio2,
29
+ file,
30
+ df1,
31
+ ):
32
+ return (
33
+ (text1 if single_checkbox else text2)
34
+ + ", selected:"
35
+ + ", ".join(checkboxes), # Text
36
+ {
37
+ "positive": num / (num + slider1 + slider2),
38
+ "negative": slider1 / (num + slider1 + slider2),
39
+ "neutral": slider2 / (num + slider1 + slider2),
40
+ }, # Label
41
+ (audio1[0], np.flipud(audio1[1]))
42
+ if audio1 is not None
43
+ else os.path.join(os.path.dirname(__file__), "files/cantina.wav"), # Audio
44
+ np.flipud(im1)
45
+ if im1 is not None
46
+ else os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), # Image
47
+ video
48
+ if video is not None
49
+ else os.path.join(os.path.dirname(__file__), "files/world.mp4"), # Video
50
+ [
51
+ ("The", "art"),
52
+ ("quick brown", "adj"),
53
+ ("fox", "nn"),
54
+ ("jumped", "vrb"),
55
+ ("testing testing testing", None),
56
+ ("over", "prp"),
57
+ ("the", "art"),
58
+ ("testing", None),
59
+ ("lazy", "adj"),
60
+ ("dogs", "nn"),
61
+ (".", "punc"),
62
+ ]
63
+ + [(f"test {x}", f"test {x}") for x in range(10)], # HighlightedText
64
+ # [("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
65
+ [
66
+ ("The testing testing testing", None),
67
+ ("over", 0.6),
68
+ ("the", 0.2),
69
+ ("testing", None),
70
+ ("lazy", -0.1),
71
+ ("dogs", 0.4),
72
+ (".", 0),
73
+ ]
74
+ + [("test", x / 10) for x in range(-10, 10)], # HighlightedText
75
+ json.loads(JSONOBJ), # JSON
76
+ "<button style='background-color: red'>Click Me: "
77
+ + radio
78
+ + "</button>", # HTML
79
+ os.path.join(os.path.dirname(__file__), "files/titanic.csv"),
80
+ df1, # Dataframe
81
+ np.random.randint(0, 10, (4, 4)), # Dataframe
82
+ )
83
+
84
+ demo = gr.Interface(
85
+ fn,
86
+ inputs=[
87
+ gr.Textbox(value="Lorem ipsum", label="Textbox"),
88
+ gr.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"),
89
+ gr.Number(label="Number", value=42),
90
+ gr.Slider(10, 20, value=15, label="Slider: 10 - 20"),
91
+ gr.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"),
92
+ gr.Checkbox(label="Checkbox"),
93
+ gr.CheckboxGroup(label="CheckboxGroup", choices=CHOICES, value=CHOICES[0:2]),
94
+ gr.Radio(label="Radio", choices=CHOICES, value=CHOICES[2]),
95
+ gr.Dropdown(label="Dropdown", choices=CHOICES),
96
+ gr.Dropdown(
97
+ label="Multiselect Dropdown (Max choice: 2)",
98
+ choices=CHOICES,
99
+ multiselect=True,
100
+ max_choices=2,
101
+ ),
102
+ gr.Image(label="Image"),
103
+ # gr.Image(label="Image w/ Cropper", tool="select"),
104
+ # gr.Image(label="Sketchpad", source="canvas"),
105
+ gr.Image(label="Webcam", sources=["webcam"]),
106
+ gr.Video(label="Video"),
107
+ gr.Audio(label="Audio"),
108
+ gr.Audio(label="Microphone", sources=["microphone"]),
109
+ gr.File(label="File"),
110
+ gr.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]),
111
+ ],
112
+ outputs=[
113
+ gr.Textbox(label="Textbox"),
114
+ gr.Label(label="Label"),
115
+ gr.Audio(label="Audio"),
116
+ gr.Image(label="Image", elem_id="output-img"),
117
+ gr.Video(label="Video"),
118
+ gr.HighlightedText(
119
+ label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}
120
+ ),
121
+ gr.HighlightedText(label="HighlightedText", show_legend=True),
122
+ gr.JSON(label="JSON"),
123
+ gr.HTML(label="HTML"),
124
+ gr.File(label="File"),
125
+ gr.Dataframe(label="Dataframe"),
126
+ gr.Dataframe(label="Numpy"),
127
+ ],
128
+ examples=[
129
+ [
130
+ "the quick brown fox",
131
+ "jumps over the lazy dog",
132
+ 10,
133
+ 12,
134
+ 4,
135
+ True,
136
+ ["foo", "baz"],
137
+ "baz",
138
+ "bar",
139
+ ["foo", "bar"],
140
+ os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
141
+ # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
142
+ # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
143
+ os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
144
+ os.path.join(os.path.dirname(__file__), "files/world.mp4"),
145
+ os.path.join(os.path.dirname(__file__), "files/cantina.wav"),
146
+ os.path.join(os.path.dirname(__file__), "files/cantina.wav"),
147
+ os.path.join(os.path.dirname(__file__), "files/titanic.csv"),
148
+ [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],
149
+ ]
150
+ ]
151
+ * 3,
152
+ title="Kitchen Sink",
153
+ description="Try out all the components!",
154
+ article="Learn more about [Gradio](http://gradio.app)",
155
+ cache_examples=True,
156
+ )
157
+
158
+ if __name__ == "__main__":
159
+ demo.launch()
demos/kitchen_sink_random/__init__.py ADDED
File without changes
demos/kitchen_sink_random/constants.py ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import matplotlib.pyplot as plt
3
+ import random
4
+ import os
5
+
6
+ def random_plot():
7
+ start_year = 2020
8
+ x = np.arange(start_year, start_year + random.randint(0, 10))
9
+ year_count = x.shape[0]
10
+ plt_format = "-"
11
+ fig = plt.figure()
12
+ ax = fig.add_subplot(111)
13
+ series = np.arange(0, year_count, dtype=float)
14
+ series = series**2
15
+ series += np.random.rand(year_count)
16
+ ax.plot(x, series, plt_format)
17
+ return fig
18
+
19
+ img_dir = os.path.join(os.path.dirname(__file__), "files")
20
+ file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files")
21
+ model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files")
22
+ highlighted_text_output_1 = [
23
+ {
24
+ "entity": "I-LOC",
25
+ "score": 0.9988978,
26
+ "index": 2,
27
+ "word": "Chicago",
28
+ "start": 5,
29
+ "end": 12,
30
+ },
31
+ {
32
+ "entity": "I-MISC",
33
+ "score": 0.9958592,
34
+ "index": 5,
35
+ "word": "Pakistani",
36
+ "start": 22,
37
+ "end": 31,
38
+ },
39
+ ]
40
+ highlighted_text_output_2 = [
41
+ {
42
+ "entity": "I-LOC",
43
+ "score": 0.9988978,
44
+ "index": 2,
45
+ "word": "Chicago",
46
+ "start": 5,
47
+ "end": 12,
48
+ },
49
+ {
50
+ "entity": "I-LOC",
51
+ "score": 0.9958592,
52
+ "index": 5,
53
+ "word": "Pakistan",
54
+ "start": 22,
55
+ "end": 30,
56
+ },
57
+ ]
58
+
59
+ highlighted_text = "Does Chicago have any Pakistani restaurants"
60
+
61
+ def random_model3d():
62
+ model_3d = random.choice(
63
+ [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"]
64
+ )
65
+ return model_3d
demos/kitchen_sink_random/files/cheetah1.jpeg ADDED
demos/kitchen_sink_random/files/cheetah1.jpg ADDED
demos/kitchen_sink_random/files/lion.jpg ADDED
demos/kitchen_sink_random/requirements.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ matplotlib
2
+ pandas
demos/kitchen_sink_random/run.py ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from datetime import datetime
3
+ import random
4
+ import string
5
+ import os
6
+ import pandas as pd
7
+
8
+ from constants import (
9
+ file_dir,
10
+ img_dir,
11
+ highlighted_text,
12
+ highlighted_text_output_2,
13
+ highlighted_text_output_1,
14
+ random_plot,
15
+ random_model3d,
16
+ )
17
+
18
+ demo = gr.Interface(
19
+ lambda *args: args[0],
20
+ inputs=[
21
+ gr.Textbox(value=lambda: datetime.now(), label="Current Time"),
22
+ gr.Number(value=lambda: random.random(), label="Ranom Percentage"),
23
+ gr.Slider(minimum=-1, maximum=1, randomize=True, label="Slider with randomize"),
24
+ gr.Slider(
25
+ minimum=0,
26
+ maximum=1,
27
+ value=lambda: random.random(),
28
+ label="Slider with value func",
29
+ ),
30
+ gr.Checkbox(value=lambda: random.random() > 0.5, label="Random Checkbox"),
31
+ gr.CheckboxGroup(
32
+ choices=["a", "b", "c", "d"],
33
+ value=lambda: random.choice(["a", "b", "c", "d"]),
34
+ label="Random CheckboxGroup",
35
+ ),
36
+ gr.Radio(
37
+ choices=list(string.ascii_lowercase),
38
+ value=lambda: random.choice(string.ascii_lowercase),
39
+ ),
40
+ gr.Dropdown(
41
+ choices=["a", "b", "c", "d", "e"],
42
+ value=lambda: random.choice(["a", "b", "c"]),
43
+ ),
44
+ gr.Image(
45
+ value=lambda: random.choice(
46
+ [os.path.join(img_dir, img) for img in os.listdir(img_dir)]
47
+ )
48
+ ),
49
+ gr.Video(value=lambda: os.path.join(file_dir, "world.mp4")),
50
+ gr.Audio(value=lambda: os.path.join(file_dir, "cantina.wav")),
51
+ gr.File(
52
+ value=lambda: random.choice(
53
+ [os.path.join(file_dir, img) for img in os.listdir(file_dir)]
54
+ )
55
+ ),
56
+ gr.Dataframe(
57
+ value=lambda: pd.DataFrame(
58
+ {"random_number_rows": range(random.randint(0, 10))}
59
+ )
60
+ ),
61
+ gr.State(value=lambda: random.choice(string.ascii_lowercase)),
62
+ gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])),
63
+ gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])),
64
+ gr.HighlightedText(
65
+ value=lambda: random.choice(
66
+ [
67
+ {"text": highlighted_text, "entities": highlighted_text_output_1},
68
+ {"text": highlighted_text, "entities": highlighted_text_output_2},
69
+ ]
70
+ ),
71
+ ),
72
+ gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])),
73
+ gr.HTML(
74
+ value=lambda: random.choice(
75
+ [
76
+ '<p style="color:red;">I am red</p>',
77
+ '<p style="color:blue;">I am blue</p>',
78
+ ]
79
+ )
80
+ ),
81
+ gr.Gallery(
82
+ value=lambda: [os.path.join(img_dir, img) for img in os.listdir(img_dir)]
83
+ ),
84
+ gr.Chatbot(
85
+ value=lambda: random.choice([[("hello", "hi!")], [("bye", "goodbye!")]])
86
+ ),
87
+ gr.Model3D(value=random_model3d),
88
+ gr.Plot(value=random_plot),
89
+ gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"),
90
+ ],
91
+ outputs=[
92
+ gr.State(value=lambda: random.choice(string.ascii_lowercase))
93
+ ],
94
+ )
95
+
96
+ if __name__ == "__main__":
97
+ demo.launch()
demos/matrix_transpose/run.ipynb ADDED
@@ -0,0 +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),\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 ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+
3
+ import gradio as gr
4
+
5
+ def transpose(matrix):
6
+ return matrix.T
7
+
8
+ demo = gr.Interface(
9
+ transpose,
10
+ gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
11
+ "numpy",
12
+ examples=[
13
+ [np.zeros((3, 3)).tolist()],
14
+ [np.ones((2, 2)).tolist()],
15
+ [np.random.randint(0, 10, (3, 10)).tolist()],
16
+ [np.random.randint(0, 10, (10, 3)).tolist()],
17
+ [np.random.randint(0, 10, (10, 10)).tolist()],
18
+ ],
19
+ cache_examples=False
20
+ )
21
+
22
+ if __name__ == "__main__":
23
+ demo.launch()
demos/matrix_transpose/screenshot.png ADDED
demos/mini_leaderboard/assets/__init__.py ADDED
File without changes
demos/mini_leaderboard/assets/custom_css.css ADDED
@@ -0,0 +1,87 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /* Hides the final AutoEvalColumn */
2
+ #llm-benchmark-tab-table table td:last-child,
3
+ #llm-benchmark-tab-table table th:last-child {
4
+ display: none;
5
+ }
6
+
7
+ /* Limit the width of the first AutoEvalColumn so that names don't expand too much */
8
+ table td:first-child,
9
+ table th:first-child {
10
+ max-width: 400px;
11
+ overflow: auto;
12
+ white-space: nowrap;
13
+ }
14
+
15
+ /* Full width space */
16
+ .gradio-container {
17
+ max-width: 95%!important;
18
+ }
19
+
20
+ /* Text style and margins */
21
+ .markdown-text {
22
+ font-size: 16px !important;
23
+ }
24
+
25
+ #models-to-add-text {
26
+ font-size: 18px !important;
27
+ }
28
+
29
+ #citation-button span {
30
+ font-size: 16px !important;
31
+ }
32
+
33
+ #citation-button textarea {
34
+ font-size: 16px !important;
35
+ }
36
+
37
+ #citation-button > label > button {
38
+ margin: 6px;
39
+ transform: scale(1.3);
40
+ }
41
+
42
+ #search-bar-table-box > div:first-child {
43
+ background: none;
44
+ border: none;
45
+ }
46
+
47
+ #search-bar {
48
+ padding: 0px;
49
+ }
50
+
51
+ .tab-buttons button {
52
+ font-size: 20px;
53
+ }
54
+
55
+ /* Filters style */
56
+ #filter_type{
57
+ border: 0;
58
+ padding-left: 0;
59
+ padding-top: 0;
60
+ }
61
+ #filter_type label {
62
+ display: flex;
63
+ }
64
+ #filter_type label > span{
65
+ margin-top: var(--spacing-lg);
66
+ margin-right: 0.5em;
67
+ }
68
+ #filter_type label > .wrap{
69
+ width: 103px;
70
+ }
71
+ #filter_type label > .wrap .wrap-inner{
72
+ padding: 2px;
73
+ }
74
+ #filter_type label > .wrap .wrap-inner input{
75
+ width: 1px
76
+ }
77
+ #filter-columns-type{
78
+ border:0;
79
+ padding:0.5;
80
+ }
81
+ #filter-columns-size{
82
+ border:0;
83
+ padding:0.5;
84
+ }
85
+ #box-filter > .form{
86
+ border: 0
87
+ }
demos/mini_leaderboard/assets/leaderboard_data.json ADDED
The diff for this file is too large to render. See raw diff
 
demos/mini_leaderboard/requirements.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ pandas
demos/mini_leaderboard/run.ipynb ADDED
@@ -0,0 +1 @@
 
 
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 ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import pandas as pd
3
+ from pathlib import Path
4
+
5
+ abs_path = Path(__file__).parent.absolute()
6
+
7
+ df = pd.read_json(str(abs_path / "assets/leaderboard_data.json"))
8
+ invisible_df = df.copy()
9
+
10
+ COLS = [
11
+ "T",
12
+ "Model",
13
+ "Average ⬆️",
14
+ "ARC",
15
+ "HellaSwag",
16
+ "MMLU",
17
+ "TruthfulQA",
18
+ "Winogrande",
19
+ "GSM8K",
20
+ "Type",
21
+ "Architecture",
22
+ "Precision",
23
+ "Merged",
24
+ "Hub License",
25
+ "#Params (B)",
26
+ "Hub ❤️",
27
+ "Model sha",
28
+ "model_name_for_query",
29
+ ]
30
+ ON_LOAD_COLS = [
31
+ "T",
32
+ "Model",
33
+ "Average ⬆️",
34
+ "ARC",
35
+ "HellaSwag",
36
+ "MMLU",
37
+ "TruthfulQA",
38
+ "Winogrande",
39
+ "GSM8K",
40
+ "model_name_for_query",
41
+ ]
42
+ TYPES = [
43
+ "str",
44
+ "markdown",
45
+ "number",
46
+ "number",
47
+ "number",
48
+ "number",
49
+ "number",
50
+ "number",
51
+ "number",
52
+ "str",
53
+ "str",
54
+ "str",
55
+ "str",
56
+ "bool",
57
+ "str",
58
+ "number",
59
+ "number",
60
+ "bool",
61
+ "str",
62
+ "bool",
63
+ "bool",
64
+ "str",
65
+ ]
66
+ NUMERIC_INTERVALS = {
67
+ "?": pd.Interval(-1, 0, closed="right"),
68
+ "~1.5": pd.Interval(0, 2, closed="right"),
69
+ "~3": pd.Interval(2, 4, closed="right"),
70
+ "~7": pd.Interval(4, 9, closed="right"),
71
+ "~13": pd.Interval(9, 20, closed="right"),
72
+ "~35": pd.Interval(20, 45, closed="right"),
73
+ "~60": pd.Interval(45, 70, closed="right"),
74
+ "70+": pd.Interval(70, 10000, closed="right"),
75
+ }
76
+ MODEL_TYPE = [str(s) for s in df["T"].unique()]
77
+ Precision = [str(s) for s in df["Precision"].unique()]
78
+
79
+ # Searching and filtering
80
+ def update_table(
81
+ hidden_df: pd.DataFrame,
82
+ columns: list,
83
+ type_query: list,
84
+ precision_query: str,
85
+ size_query: list,
86
+ query: str,
87
+ ):
88
+ filtered_df = filter_models(hidden_df, type_query, size_query, precision_query) # type: ignore
89
+ filtered_df = filter_queries(query, filtered_df)
90
+ df = select_columns(filtered_df, columns)
91
+ return df
92
+
93
+ def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
94
+ return df[(df["model_name_for_query"].str.contains(query, case=False))] # type: ignore
95
+
96
+ def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
97
+ # We use COLS to maintain sorting
98
+ filtered_df = df[[c for c in COLS if c in df.columns and c in columns]]
99
+ return filtered_df # type: ignore
100
+
101
+ def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
102
+ final_df = []
103
+ if query != "":
104
+ queries = [q.strip() for q in query.split(";")]
105
+ for _q in queries:
106
+ _q = _q.strip()
107
+ if _q != "":
108
+ temp_filtered_df = search_table(filtered_df, _q)
109
+ if len(temp_filtered_df) > 0:
110
+ final_df.append(temp_filtered_df)
111
+ if len(final_df) > 0:
112
+ filtered_df = pd.concat(final_df)
113
+ filtered_df = filtered_df.drop_duplicates( # type: ignore
114
+ subset=["Model", "Precision", "Model sha"]
115
+ )
116
+
117
+ return filtered_df
118
+
119
+ def filter_models(
120
+ df: pd.DataFrame,
121
+ type_query: list,
122
+ size_query: list,
123
+ precision_query: list,
124
+ ) -> pd.DataFrame:
125
+ # Show all models
126
+ filtered_df = df
127
+
128
+ type_emoji = [t[0] for t in type_query]
129
+ filtered_df = filtered_df.loc[df["T"].isin(type_emoji)]
130
+ filtered_df = filtered_df.loc[df["Precision"].isin(precision_query + ["None"])]
131
+
132
+ numeric_interval = pd.IntervalIndex(
133
+ sorted([NUMERIC_INTERVALS[s] for s in size_query]) # type: ignore
134
+ )
135
+ params_column = pd.to_numeric(df["#Params (B)"], errors="coerce")
136
+ mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) # type: ignore
137
+ filtered_df = filtered_df.loc[mask]
138
+
139
+ return filtered_df
140
+
141
+ demo = gr.Blocks(css=str(abs_path / "assets/leaderboard_data.json"))
142
+ with demo:
143
+ gr.Markdown("""Test Space of the LLM Leaderboard""", elem_classes="markdown-text")
144
+
145
+ with gr.Tabs(elem_classes="tab-buttons") as tabs:
146
+ with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
147
+ with gr.Row():
148
+ with gr.Column():
149
+ with gr.Row():
150
+ search_bar = gr.Textbox(
151
+ placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
152
+ show_label=False,
153
+ elem_id="search-bar",
154
+ )
155
+ with gr.Row():
156
+ shown_columns = gr.CheckboxGroup(
157
+ choices=COLS,
158
+ value=ON_LOAD_COLS,
159
+ label="Select columns to show",
160
+ elem_id="column-select",
161
+ interactive=True,
162
+ )
163
+ with gr.Column(min_width=320):
164
+ filter_columns_type = gr.CheckboxGroup(
165
+ label="Model types",
166
+ choices=MODEL_TYPE,
167
+ value=MODEL_TYPE,
168
+ interactive=True,
169
+ elem_id="filter-columns-type",
170
+ )
171
+ filter_columns_precision = gr.CheckboxGroup(
172
+ label="Precision",
173
+ choices=Precision,
174
+ value=Precision,
175
+ interactive=True,
176
+ elem_id="filter-columns-precision",
177
+ )
178
+ filter_columns_size = gr.CheckboxGroup(
179
+ label="Model sizes (in billions of parameters)",
180
+ choices=list(NUMERIC_INTERVALS.keys()),
181
+ value=list(NUMERIC_INTERVALS.keys()),
182
+ interactive=True,
183
+ elem_id="filter-columns-size",
184
+ )
185
+
186
+ leaderboard_table = gr.components.Dataframe(
187
+ value=df[ON_LOAD_COLS], # type: ignore
188
+ headers=ON_LOAD_COLS,
189
+ datatype=TYPES,
190
+ elem_id="leaderboard-table",
191
+ interactive=False,
192
+ visible=True,
193
+ column_widths=["2%", "33%"],
194
+ )
195
+
196
+ # Dummy leaderboard for handling the case when the user uses backspace key
197
+ hidden_leaderboard_table_for_search = gr.components.Dataframe(
198
+ value=invisible_df[COLS], # type: ignore
199
+ headers=COLS,
200
+ datatype=TYPES,
201
+ visible=False,
202
+ )
203
+ search_bar.submit(
204
+ update_table,
205
+ [
206
+ hidden_leaderboard_table_for_search,
207
+ shown_columns,
208
+ filter_columns_type,
209
+ filter_columns_precision,
210
+ filter_columns_size,
211
+ search_bar,
212
+ ],
213
+ leaderboard_table,
214
+ )
215
+ for selector in [
216
+ shown_columns,
217
+ filter_columns_type,
218
+ filter_columns_precision,
219
+ filter_columns_size,
220
+ ]:
221
+ selector.change(
222
+ update_table,
223
+ [
224
+ hidden_leaderboard_table_for_search,
225
+ shown_columns,
226
+ filter_columns_type,
227
+ filter_columns_precision,
228
+ filter_columns_size,
229
+ search_bar,
230
+ ],
231
+ leaderboard_table,
232
+ queue=True,
233
+ )
234
+
235
+ if __name__ == "__main__":
236
+ demo.queue(default_concurrency_limit=40).launch()
demos/model3D/files/Bunny.obj ADDED
The diff for this file is too large to render. See raw diff
 
demos/model3D/files/Duck.glb ADDED
Binary file (120 kB). View file
 
demos/model3D/files/Fox.gltf ADDED
The diff for this file is too large to render. See raw diff
 
demos/model3D/files/face.obj ADDED
@@ -0,0 +1,2471 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ####
2
+ #
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+ # OBJ File Generated by Meshlab
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+ #
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+ ####
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+ # Object face.obj
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+ #
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+ # Vertices: 845
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+ # End of File
demos/model3D/files/sofia.stl ADDED
Binary file (675 kB). View file
 
demos/model3D/files/source.txt ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Stanford Bunny:
2
+ https://graphics.stanford.edu/data/3Dscanrep/
3
+ https://graphics.stanford.edu/~mdfisher/Data/Meshes/bunny.obj
4
+
5
+ Duck & Fox:
6
+ https://github.com/KhronosGroup/glTF-Sample-Models
7
+
8
+ Face:
9
+ https://github.com/mikedh/trimesh/tree/main/models
10
+
11
+ NASA SOFIA:
12
+ https://nasa3d.arc.nasa.gov/detail/sofia
13
+ https://github.com/nasa/NASA-3D-Resources/blob/master/3D%20Models/SOFIA/Fuselage_top.stl