Commit 1: Add 50 file(s)

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip 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

README.md

@@ -1,12 +1,11 @@
+
 ---
-title: Pr 10229 All Demos
-emoji: 🏆
-colorFrom: pink
-colorTo: yellow
+title: pr-10229-all-demos 
+emoji: 💩
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 5.9.1
-app_file: app.py
+app_file: run.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

demos/audio_debugger/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: audio_debugger"]}, {"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", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/audio_debugger/cantina.wav"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import subprocess\n", "import os\n", "\n", "audio_file = os.path.join(os.path.abspath(''), \"cantina.wav\")\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tab(\"Audio\"):\n", "        gr.Audio(audio_file)\n", "    with gr.Tab(\"Interface\"):\n", "        gr.Interface(\n", "            lambda x: x, \"audio\", \"audio\", examples=[audio_file], cache_examples=True\n", "        )\n", "    with gr.Tab(\"Streaming\"):\n", "        gr.Interface(\n", "            lambda x: x,\n", "            gr.Audio(streaming=True),\n", "            \"audio\",\n", "            examples=[audio_file],\n", "            cache_examples=True,\n", "        )\n", "    with gr.Tab(\"console\"):\n", "        ip = gr.Textbox(label=\"User IP Address\")\n", "        gr.Interface(\n", "            lambda cmd: subprocess.run([cmd], capture_output=True, shell=True, check=False)\n", "            .stdout.decode(\"utf-8\")\n", "            .strip(),\n", "            \"text\",\n", "            \"text\",\n", "        )\n", "\n", "    def get_ip(request: gr.Request):\n", "        return request.client.host\n", "\n", "    demo.load(get_ip, None, ip)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/audio_debugger/run.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import subprocess
+import os
+
+audio_file = os.path.join(os.path.dirname(__file__), "cantina.wav")
+
+with gr.Blocks() as demo:
+    with gr.Tab("Audio"):
+        gr.Audio(audio_file)
+    with gr.Tab("Interface"):
+        gr.Interface(
+            lambda x: x, "audio", "audio", examples=[audio_file], cache_examples=True
+        )
+    with gr.Tab("Streaming"):
+        gr.Interface(
+            lambda x: x,
+            gr.Audio(streaming=True),
+            "audio",
+            examples=[audio_file],
+            cache_examples=True,
+        )
+    with gr.Tab("console"):
+        ip = gr.Textbox(label="User IP Address")
+        gr.Interface(
+            lambda cmd: subprocess.run([cmd], capture_output=True, shell=True, check=False)
+            .stdout.decode("utf-8")
+            .strip(),
+            "text",
+            "text",
+        )
+
+    def get_ip(request: gr.Request):
+        return request.client.host
+
+    demo.load(get_ip, None, ip)
+
+if __name__ == "__main__":
+    demo.launch()

demos/blocks_essay/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_essay"]}, {"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", "countries_cities_dict = {\n", "    \"USA\": [\"New York\", \"Los Angeles\", \"Chicago\"],\n", "    \"Canada\": [\"Toronto\", \"Montreal\", \"Vancouver\"],\n", "    \"Pakistan\": [\"Karachi\", \"Lahore\", \"Islamabad\"],\n", "}\n", "\n", "def change_textbox(choice):\n", "    if choice == \"short\":\n", "        return gr.Textbox(lines=2, visible=True), gr.Button(interactive=True)\n", "    elif choice == \"long\":\n", "        return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\"), gr.Button(interactive=True)\n", "    else:\n", "        return gr.Textbox(visible=False), gr.Button(interactive=False)\n", "\n", "with gr.Blocks() as demo:\n", "    radio = gr.Radio(\n", "        [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n", "    )\n", "    text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n", "\n", "    with gr.Row():\n", "        num = gr.Number(minimum=0, maximum=100, label=\"input\")\n", "        out = gr.Number(label=\"output\")\n", "    minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\n", "    maximum_slider = gr.Slider(0, 100, 100, label=\"max\")\n", "    submit_btn = gr.Button(\"Submit\", variant=\"primary\")\n", "\n", "    with gr.Row():\n", "        country = gr.Dropdown(list(countries_cities_dict.keys()), label=\"Country\")\n", "        cities = gr.Dropdown([], label=\"Cities\")\n", "    @country.change(inputs=country, outputs=cities)\n", "    def update_cities(country):\n", "        cities = list(countries_cities_dict[country])\n", "        return gr.Dropdown(choices=cities, value=cities[0], interactive=True)\n", "\n", "    def reset_bounds(minimum, maximum):\n", "        return gr.Number(minimum=minimum, maximum=maximum)\n", "\n", "    radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn])\n", "    gr.on(\n", "        [minimum_slider.change, maximum_slider.change],\n", "        reset_bounds,\n", "        [minimum_slider, maximum_slider],\n", "        outputs=num,\n", "    )\n", "    num.submit(lambda x: x, num, out)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/blocks_essay/run.py

@@ -0,0 +1,51 @@
+import gradio as gr
+
+countries_cities_dict = {
+    "USA": ["New York", "Los Angeles", "Chicago"],
+    "Canada": ["Toronto", "Montreal", "Vancouver"],
+    "Pakistan": ["Karachi", "Lahore", "Islamabad"],
+}
+
+def change_textbox(choice):
+    if choice == "short":
+        return gr.Textbox(lines=2, visible=True), gr.Button(interactive=True)
+    elif choice == "long":
+        return gr.Textbox(lines=8, visible=True, value="Lorem ipsum dolor sit amet"), gr.Button(interactive=True)
+    else:
+        return gr.Textbox(visible=False), gr.Button(interactive=False)
+
+with gr.Blocks() as demo:
+    radio = gr.Radio(
+        ["short", "long", "none"], label="What kind of essay would you like to write?"
+    )
+    text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)
+
+    with gr.Row():
+        num = gr.Number(minimum=0, maximum=100, label="input")
+        out = gr.Number(label="output")
+    minimum_slider = gr.Slider(0, 100, 0, label="min")
+    maximum_slider = gr.Slider(0, 100, 100, label="max")
+    submit_btn = gr.Button("Submit", variant="primary")
+
+    with gr.Row():
+        country = gr.Dropdown(list(countries_cities_dict.keys()), label="Country")
+        cities = gr.Dropdown([], label="Cities")
+    @country.change(inputs=country, outputs=cities)
+    def update_cities(country):
+        cities = list(countries_cities_dict[country])
+        return gr.Dropdown(choices=cities, value=cities[0], interactive=True)
+
+    def reset_bounds(minimum, maximum):
+        return gr.Number(minimum=minimum, maximum=maximum)
+
+    radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn])
+    gr.on(
+        [minimum_slider.change, maximum_slider.change],
+        reset_bounds,
+        [minimum_slider, maximum_slider],
+        outputs=num,
+    )
+    num.submit(lambda x: x, num, out)
+
+if __name__ == "__main__":
+    demo.launch()

demos/blocks_group/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_group"]}, {"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 greet(name):\n", "    return \"Hello \" + name + \"!\"\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"### This is a couple of elements without any gr.Group. Form elements naturally group together anyway.\")\n", "    gr.Textbox(\"A\")\n", "    gr.Number(3)\n", "    gr.Button()\n", "    gr.Image()\n", "    gr.Slider()\n", "\n", "    gr.Markdown(\"### This is the same set put in a gr.Group.\")\n", "    with gr.Group():\n", "        gr.Textbox(\"A\")\n", "        gr.Number(3)\n", "        gr.Button()\n", "        gr.Image()\n", "        gr.Slider()\n", "\n", "    gr.Markdown(\"### Now in a Row, no group.\")\n", "    with gr.Row():\n", "        gr.Textbox(\"A\")\n", "        gr.Number(3)\n", "        gr.Button()\n", "        gr.Image()\n", "        gr.Slider()\n", "\n", "    gr.Markdown(\"### Now in a Row in a group.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Textbox(\"A\")\n", "            gr.Number(3)\n", "            gr.Button()\n", "            gr.Image()\n", "            gr.Slider()\n", "\n", "    gr.Markdown(\"### Several rows grouped together.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Textbox(\"A\")\n", "            gr.Number(3)\n", "            gr.Button()\n", "        with gr.Row():\n", "            gr.Image()\n", "            gr.Audio()\n", "\n", "    gr.Markdown(\"### Several columns grouped together. If columns are uneven, there is a gray group background.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            with gr.Column():\n", "                name = gr.Textbox(label=\"Name\")\n", "                btn = gr.Button(\"Hello\")\n", "                gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", "                gr.Number()\n", "                gr.Textbox()\n", "            with gr.Column():\n", "                gr.Image()\n", "                gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", "                with gr.Row():\n", "                    gr.Number(scale=2)\n", "                    gr.Textbox()\n", "\n", "    gr.Markdown(\"### container=False removes label, padding, and block border, placing elements 'directly' on background.\")\n", "    gr.Radio([1,2,3], container=False)\n", "    gr.Textbox(container=False)\n", "    gr.Image(\"https://picsum.photos/id/237/200/300\", container=False, height=200)\n", "\n", "    gr.Markdown(\"### Textbox, Dropdown, and Number input boxes takes up full space when within a group without a container.\")\n", "\n", "    with gr.Group():\n", "        name = gr.Textbox(label=\"Name\")\n", "        output = gr.Textbox(show_label=False, container=False)\n", "        greet_btn = gr.Button(\"Greet\")\n", "        with gr.Row():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False)\n", "            gr.Textbox(container=False)\n", "            gr.Number(container=False)\n", "            gr.Image(height=100)\n", "    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name=\"greet\")\n", "\n", "    gr.Markdown(\"### More examples\")\n", "\n", "    with gr.Group():\n", "        gr.Chatbot()\n", "        with gr.Row():\n", "            name = gr.Textbox(label=\"Prompot\", container=False)\n", "            go = gr.Button(\"go\", scale=0)\n", "\n", "    with gr.Column():\n", "        gr.Radio([1,2,3], container=False)\n", "        gr.Slider(0, 20, container=False)\n", "\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", "            gr.Number(container=False)\n", "            gr.Textbox(container=False)\n", "\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", "        with gr.Column():\n", "           gr.Number(container=False)\n", "        with gr.Column():\n", "            gr.Textbox(container=False)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/blocks_group/run.py

@@ -0,0 +1,110 @@
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!"
+
+with gr.Blocks() as demo:
+    gr.Markdown("### This is a couple of elements without any gr.Group. Form elements naturally group together anyway.")
+    gr.Textbox("A")
+    gr.Number(3)
+    gr.Button()
+    gr.Image()
+    gr.Slider()
+
+    gr.Markdown("### This is the same set put in a gr.Group.")
+    with gr.Group():
+        gr.Textbox("A")
+        gr.Number(3)
+        gr.Button()
+        gr.Image()
+        gr.Slider()
+
+    gr.Markdown("### Now in a Row, no group.")
+    with gr.Row():
+        gr.Textbox("A")
+        gr.Number(3)
+        gr.Button()
+        gr.Image()
+        gr.Slider()
+
+    gr.Markdown("### Now in a Row in a group.")
+    with gr.Group():
+        with gr.Row():
+            gr.Textbox("A")
+            gr.Number(3)
+            gr.Button()
+            gr.Image()
+            gr.Slider()
+
+    gr.Markdown("### Several rows grouped together.")
+    with gr.Group():
+        with gr.Row():
+            gr.Textbox("A")
+            gr.Number(3)
+            gr.Button()
+        with gr.Row():
+            gr.Image()
+            gr.Audio()
+
+    gr.Markdown("### Several columns grouped together. If columns are uneven, there is a gray group background.")
+    with gr.Group():
+        with gr.Row():
+            with gr.Column():
+                name = gr.Textbox(label="Name")
+                btn = gr.Button("Hello")
+                gr.Dropdown(["a", "b", "c"], interactive=True)
+                gr.Number()
+                gr.Textbox()
+            with gr.Column():
+                gr.Image()
+                gr.Dropdown(["a", "b", "c"], interactive=True)
+                with gr.Row():
+                    gr.Number(scale=2)
+                    gr.Textbox()
+
+    gr.Markdown("### container=False removes label, padding, and block border, placing elements 'directly' on background.")
+    gr.Radio([1,2,3], container=False)
+    gr.Textbox(container=False)
+    gr.Image("https://picsum.photos/id/237/200/300", container=False, height=200)
+
+    gr.Markdown("### Textbox, Dropdown, and Number input boxes takes up full space when within a group without a container.")
+
+    with gr.Group():
+        name = gr.Textbox(label="Name")
+        output = gr.Textbox(show_label=False, container=False)
+        greet_btn = gr.Button("Greet")
+        with gr.Row():
+            gr.Dropdown(["a", "b", "c"], interactive=True, container=False)
+            gr.Textbox(container=False)
+            gr.Number(container=False)
+            gr.Image(height=100)
+    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
+
+    gr.Markdown("### More examples")
+
+    with gr.Group():
+        gr.Chatbot()
+        with gr.Row():
+            name = gr.Textbox(label="Prompot", container=False)
+            go = gr.Button("go", scale=0)
+
+    with gr.Column():
+        gr.Radio([1,2,3], container=False)
+        gr.Slider(0, 20, container=False)
+
+    with gr.Group():
+        with gr.Row():
+            gr.Dropdown(["a", "b", "c"], interactive=True, container=False, elem_id="here2")
+            gr.Number(container=False)
+            gr.Textbox(container=False)
+
+    with gr.Row():
+        with gr.Column():
+            gr.Dropdown(["a", "b", "c"], interactive=True, container=False, elem_id="here2")
+        with gr.Column():
+           gr.Number(container=False)
+        with gr.Column():
+            gr.Textbox(container=False)
+
+if __name__ == "__main__":
+    demo.launch()

demos/blocks_js_methods/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_js_methods"]}, {"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", "blocks = gr.Blocks()\n", "\n", "with blocks as demo:\n", "    subject = gr.Textbox(placeholder=\"subject\")\n", "    verb = gr.Radio([\"ate\", \"loved\", \"hated\"])\n", "    object = gr.Textbox(placeholder=\"object\")\n", "\n", "    with gr.Row():\n", "        btn = gr.Button(\"Create sentence.\")\n", "        reverse_btn = gr.Button(\"Reverse sentence.\")\n", "        foo_bar_btn = gr.Button(\"Append foo\")\n", "        reverse_then_to_the_server_btn = gr.Button(\n", "            \"Reverse sentence and send to server.\"\n", "        )\n", "\n", "    def sentence_maker(w1, w2, w3):\n", "        return f\"{w1} {w2} {w3}\"\n", "\n", "    output1 = gr.Textbox(label=\"output 1\")\n", "    output2 = gr.Textbox(label=\"verb\")\n", "    output3 = gr.Textbox(label=\"verb reversed\")\n", "    output4 = gr.Textbox(label=\"front end process and then send to backend\")\n", "\n", "    btn.click(sentence_maker, [subject, verb, object], output1)\n", "    reverse_btn.click(\n", "        None, [subject, verb, object], output2, js=\"(s, v, o) => o + ' ' + v + ' ' + s\"\n", "    )\n", "    verb.change(lambda x: x, verb, output3, js=\"(x) => [...x].reverse().join('')\")\n", "    foo_bar_btn.click(None, [], subject, js=\"(x) => x + ' foo'\")\n", "\n", "    reverse_then_to_the_server_btn.click(\n", "        sentence_maker,\n", "        [subject, verb, object],\n", "        output4,\n", "        js=\"(s, v, o) => [s, v, o].map(x => [...x].reverse().join(''))\",\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/blocks_js_methods/run.py

@@ -0,0 +1,41 @@
+import gradio as gr
+
+blocks = gr.Blocks()
+
+with blocks as demo:
+    subject = gr.Textbox(placeholder="subject")
+    verb = gr.Radio(["ate", "loved", "hated"])
+    object = gr.Textbox(placeholder="object")
+
+    with gr.Row():
+        btn = gr.Button("Create sentence.")
+        reverse_btn = gr.Button("Reverse sentence.")
+        foo_bar_btn = gr.Button("Append foo")
+        reverse_then_to_the_server_btn = gr.Button(
+            "Reverse sentence and send to server."
+        )
+
+    def sentence_maker(w1, w2, w3):
+        return f"{w1} {w2} {w3}"
+
+    output1 = gr.Textbox(label="output 1")
+    output2 = gr.Textbox(label="verb")
+    output3 = gr.Textbox(label="verb reversed")
+    output4 = gr.Textbox(label="front end process and then send to backend")
+
+    btn.click(sentence_maker, [subject, verb, object], output1)
+    reverse_btn.click(
+        None, [subject, verb, object], output2, js="(s, v, o) => o + ' ' + v + ' ' + s"
+    )
+    verb.change(lambda x: x, verb, output3, js="(x) => [...x].reverse().join('')")
+    foo_bar_btn.click(None, [], subject, js="(x) => x + ' foo'")
+
+    reverse_then_to_the_server_btn.click(
+        sentence_maker,
+        [subject, verb, object],
+        output4,
+        js="(s, v, o) => [s, v, o].map(x => [...x].reverse().join(''))",
+    )
+
+if __name__ == "__main__":
+    demo.launch()

demos/blocks_layout/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_layout"]}, {"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", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    with gr.Row():\n", "        gr.Image(interactive=True, scale=2)\n", "        gr.Image()\n", "    with gr.Row():\n", "        gr.Textbox(label=\"Text\")\n", "        gr.Number(label=\"Count\", scale=2)\n", "        gr.Radio(choices=[\"One\", \"Two\"])\n", "    with gr.Row():\n", "        gr.Button(\"500\", scale=0, min_width=500)\n", "        gr.Button(\"A\", scale=0)\n", "        gr.Button(\"grow\")\n", "    with gr.Row():\n", "        gr.Textbox()\n", "        gr.Textbox()\n", "        gr.Button()\n", "    with gr.Row():\n", "        with gr.Row():\n", "            with gr.Column():\n", "                gr.Textbox(label=\"Text\")\n", "                gr.Number(label=\"Count\")\n", "                gr.Radio(choices=[\"One\", \"Two\"])\n", "            gr.Image()\n", "            with gr.Column():\n", "                gr.Image(interactive=True)\n", "                gr.Image()\n", "    gr.Image()\n", "    gr.Textbox(label=\"Text\")\n", "    gr.Number(label=\"Count\")\n", "    gr.Radio(choices=[\"One\", \"Two\"])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/blocks_layout/run.py

@@ -0,0 +1,37 @@
+import gradio as gr
+
+demo = gr.Blocks()
+
+with demo:
+    with gr.Row():
+        gr.Image(interactive=True, scale=2)
+        gr.Image()
+    with gr.Row():
+        gr.Textbox(label="Text")
+        gr.Number(label="Count", scale=2)
+        gr.Radio(choices=["One", "Two"])
+    with gr.Row():
+        gr.Button("500", scale=0, min_width=500)
+        gr.Button("A", scale=0)
+        gr.Button("grow")
+    with gr.Row():
+        gr.Textbox()
+        gr.Textbox()
+        gr.Button()
+    with gr.Row():
+        with gr.Row():
+            with gr.Column():
+                gr.Textbox(label="Text")
+                gr.Number(label="Count")
+                gr.Radio(choices=["One", "Two"])
+            gr.Image()
+            with gr.Column():
+                gr.Image(interactive=True)
+                gr.Image()
+    gr.Image()
+    gr.Textbox(label="Text")
+    gr.Number(label="Count")
+    gr.Radio(choices=["One", "Two"])
+
+if __name__ == "__main__":
+    demo.launch()

demos/blocks_multiple_event_triggers/requirements.txt

@@ -0,0 +1,3 @@
+plotly
+pypistats
+python-dateutil

demos/blocks_multiple_event_triggers/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_multiple_event_triggers"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly pypistats python-dateutil "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pypistats\n", "from datetime import date\n", "from dateutil.relativedelta import relativedelta\n", "import pandas as pd\n", "\n", "def get_plot(lib, time):\n", "    data = pypistats.overall(lib, total=True, format=\"pandas\")\n", "    data = data.groupby(\"category\").get_group(\"with_mirrors\").sort_values(\"date\")\n", "    start_date = date.today() - relativedelta(months=int(time.split(\" \")[0]))\n", "    data = data[(data['date'] > str(start_date))]\n", "    data.date = pd.to_datetime(pd.to_datetime(data.date))\n", "    return gr.LinePlot(value=data, x=\"date\", y=\"downloads\",\n", "                              tooltip=['date', 'downloads'],\n", "                              title=f\"Pypi downloads of {lib} over last {time}\",\n", "                              overlay_point=True,\n", "                              height=400,\n", "                              width=900)\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "        ## Pypi Download Stats \ud83d\udcc8\n", "        See live download stats for all of Hugging Face's open-source libraries \ud83e\udd17\n", "        \"\"\")\n", "    with gr.Row():\n", "        lib = gr.Dropdown([\"transformers\", \"datasets\", \"huggingface-hub\", \"gradio\", \"accelerate\"],\n", "                          value=\"gradio\", label=\"Library\")\n", "        time = gr.Dropdown([\"3 months\", \"6 months\", \"9 months\", \"12 months\"],\n", "                           value=\"3 months\", label=\"Downloads over the last...\")\n", "\n", "    plt = gr.LinePlot()\n", "    # You can add multiple event triggers in 2 lines like this\n", "    for event in [lib.change, time.change, demo.load]:\n", "        event(get_plot, [lib, time], [plt])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/blocks_multiple_event_triggers/run.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import pypistats
+from datetime import date
+from dateutil.relativedelta import relativedelta
+import pandas as pd
+
+def get_plot(lib, time):
+    data = pypistats.overall(lib, total=True, format="pandas")
+    data = data.groupby("category").get_group("with_mirrors").sort_values("date")
+    start_date = date.today() - relativedelta(months=int(time.split(" ")[0]))
+    data = data[(data['date'] > str(start_date))]
+    data.date = pd.to_datetime(pd.to_datetime(data.date))
+    return gr.LinePlot(value=data, x="date", y="downloads",
+                              tooltip=['date', 'downloads'],
+                              title=f"Pypi downloads of {lib} over last {time}",
+                              overlay_point=True,
+                              height=400,
+                              width=900)
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        ## Pypi Download Stats 📈
+        See live download stats for all of Hugging Face's open-source libraries 🤗
+        """)
+    with gr.Row():
+        lib = gr.Dropdown(["transformers", "datasets", "huggingface-hub", "gradio", "accelerate"],
+                          value="gradio", label="Library")
+        time = gr.Dropdown(["3 months", "6 months", "9 months", "12 months"],
+                           value="3 months", label="Downloads over the last...")
+
+    plt = gr.LinePlot()
+    # You can add multiple event triggers in 2 lines like this
+    for event in [lib.change, time.change, demo.load]:
+        event(get_plot, [lib, time], [plt])
+
+if __name__ == "__main__":
+    demo.launch()

demos/blocks_update/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_update"]}, {"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", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "    # Animal Generator\n", "    Once you select a species, the detail panel should be visible.\n", "    \"\"\"\n", "    )\n", "\n", "    species = gr.Radio(label=\"Animal Class\", choices=[\"Mammal\", \"Fish\", \"Bird\"])\n", "    animal = gr.Dropdown(label=\"Animal\", choices=[])\n", "\n", "    with gr.Column(visible=False) as details_col:\n", "        weight = gr.Slider(0, 20)\n", "        details = gr.Textbox(label=\"Extra Details\")\n", "        generate_btn = gr.Button(\"Generate\")\n", "        output = gr.Textbox(label=\"Output\")\n", "\n", "    species_map = {\n", "        \"Mammal\": [\"Elephant\", \"Giraffe\", \"Hamster\"],\n", "        \"Fish\": [\"Shark\", \"Salmon\", \"Tuna\"],\n", "        \"Bird\": [\"Chicken\", \"Eagle\", \"Hawk\"],\n", "    }\n", "\n", "    def filter_species(species):\n", "        return gr.Dropdown(\n", "            choices=species_map[species], value=species_map[species][1]\n", "        ), gr.Column(visible=True)\n", "\n", "    species.change(filter_species, species, [animal, details_col])\n", "\n", "    def filter_weight(animal):\n", "        if animal in (\"Elephant\", \"Shark\", \"Giraffe\"):\n", "            return gr.Slider(maximum=100)\n", "        else:\n", "            return gr.Slider(maximum=20)\n", "\n", "    animal.change(filter_weight, animal, weight)\n", "    weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details)\n", "\n", "    generate_btn.click(lambda x: x, details, output)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/blocks_update/run.py

@@ -0,0 +1,45 @@
+import gradio as gr
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    # Animal Generator
+    Once you select a species, the detail panel should be visible.
+    """
+    )
+
+    species = gr.Radio(label="Animal Class", choices=["Mammal", "Fish", "Bird"])
+    animal = gr.Dropdown(label="Animal", choices=[])
+
+    with gr.Column(visible=False) as details_col:
+        weight = gr.Slider(0, 20)
+        details = gr.Textbox(label="Extra Details")
+        generate_btn = gr.Button("Generate")
+        output = gr.Textbox(label="Output")
+
+    species_map = {
+        "Mammal": ["Elephant", "Giraffe", "Hamster"],
+        "Fish": ["Shark", "Salmon", "Tuna"],
+        "Bird": ["Chicken", "Eagle", "Hawk"],
+    }
+
+    def filter_species(species):
+        return gr.Dropdown(
+            choices=species_map[species], value=species_map[species][1]
+        ), gr.Column(visible=True)
+
+    species.change(filter_species, species, [animal, details_col])
+
+    def filter_weight(animal):
+        if animal in ("Elephant", "Shark", "Giraffe"):
+            return gr.Slider(maximum=100)
+        else:
+            return gr.Slider(maximum=20)
+
+    animal.change(filter_weight, animal, weight)
+    weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details)
+
+    generate_btn.click(lambda x: x, details, output)
+
+if __name__ == "__main__":
+    demo.launch()

demos/calculator/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator"]}, {"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('examples')\n", "!wget -q -O examples/log.csv https://github.com/gradio-app/gradio/raw/main/demo/calculator/examples/log.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        if num2 == 0:\n", "            raise gr.Error(\"Cannot divide by zero!\")\n", "        return num1 / num2\n", "\n", "demo = gr.Interface(\n", "    calculator,\n", "    [\n", "        \"number\",\n", "        gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", "        \"number\"\n", "    ],\n", "    \"number\",\n", "    examples=[\n", "        [45, \"add\", 3],\n", "        [3.14, \"divide\", 2],\n", "        [144, \"multiply\", 2.5],\n", "        [0, \"subtract\", 1.2],\n", "    ],\n", "    title=\"Toy Calculator\",\n", "    description=\"Here's a sample toy calculator.\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/calculator/run.py

@@ -0,0 +1,34 @@
+import gradio as gr
+
+def calculator(num1, operation, num2):
+    if operation == "add":
+        return num1 + num2
+    elif operation == "subtract":
+        return num1 - num2
+    elif operation == "multiply":
+        return num1 * num2
+    elif operation == "divide":
+        if num2 == 0:
+            raise gr.Error("Cannot divide by zero!")
+        return num1 / num2
+
+demo = gr.Interface(
+    calculator,
+    [
+        "number",
+        gr.Radio(["add", "subtract", "multiply", "divide"]),
+        "number"
+    ],
+    "number",
+    examples=[
+        [45, "add", 3],
+        [3.14, "divide", 2],
+        [144, "multiply", 2.5],
+        [0, "subtract", 1.2],
+    ],
+    title="Toy Calculator",
+    description="Here's a sample toy calculator.",
+)
+
+if __name__ == "__main__":
+    demo.launch()

demos/cancel_events/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: cancel_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 time\n", "import gradio as gr\n", "import atexit\n", "import pathlib\n", "\n", "log_file = pathlib.Path(__file__).parent / \"cancel_events_output_log.txt\"\n", "\n", "def fake_diffusion(steps):\n", "    log_file.write_text(\"\")\n", "    for i in range(steps):\n", "        print(f\"Current step: {i}\")\n", "        with log_file.open(\"a\") as f:\n", "            f.write(f\"Current step: {i}\\n\")\n", "        time.sleep(0.2)\n", "        yield str(i)\n", "\n", "def long_prediction(*args, **kwargs):\n", "    time.sleep(10)\n", "    return 42\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", "            run = gr.Button(value=\"Start Iterating\")\n", "            output = gr.Textbox(label=\"Iterative Output\")\n", "            stop = gr.Button(value=\"Stop Iterating\")\n", "        with gr.Column():\n", "            textbox = gr.Textbox(label=\"Prompt\")\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Change\"\n", "            )\n", "            cancel_on_submit = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Submit\"\n", "            )\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(\n", "                sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True\n", "            )\n", "        with gr.Column():\n", "            video = gr.Video(\n", "                sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True\n", "            )\n", "\n", "    click_event = run.click(fake_diffusion, n, output)\n", "    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", "    pred_event = run_pred.click(\n", "        fn=long_prediction, inputs=[textbox], outputs=prediction\n", "    )\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(\n", "        lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]\n", "    )\n", "    image.clear(None, None, None, cancels=[click_event, pred_event])\n", "    video.start_recording(None, None, None, cancels=[click_event, pred_event])\n", "\n", "    demo.queue(max_size=20)\n", "    atexit.register(lambda: log_file.unlink())\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/cancel_events/run.py

@@ -0,0 +1,67 @@
+import time
+import gradio as gr
+import atexit
+import pathlib
+
+log_file = pathlib.Path(__file__).parent / "cancel_events_output_log.txt"
+
+def fake_diffusion(steps):
+    log_file.write_text("")
+    for i in range(steps):
+        print(f"Current step: {i}")
+        with log_file.open("a") as f:
+            f.write(f"Current step: {i}\n")
+        time.sleep(0.2)
+        yield str(i)
+
+def long_prediction(*args, **kwargs):
+    time.sleep(10)
+    return 42
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            n = gr.Slider(1, 10, value=9, step=1, label="Number Steps")
+            run = gr.Button(value="Start Iterating")
+            output = gr.Textbox(label="Iterative Output")
+            stop = gr.Button(value="Stop Iterating")
+        with gr.Column():
+            textbox = gr.Textbox(label="Prompt")
+            prediction = gr.Number(label="Expensive Calculation")
+            run_pred = gr.Button(value="Run Expensive Calculation")
+        with gr.Column():
+            cancel_on_change = gr.Textbox(
+                label="Cancel Iteration and Expensive Calculation on Change"
+            )
+            cancel_on_submit = gr.Textbox(
+                label="Cancel Iteration and Expensive Calculation on Submit"
+            )
+            echo = gr.Textbox(label="Echo")
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(
+                sources=["webcam"], label="Cancel on clear", interactive=True
+            )
+        with gr.Column():
+            video = gr.Video(
+                sources=["webcam"], label="Cancel on start recording", interactive=True
+            )
+
+    click_event = run.click(fake_diffusion, n, output)
+    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])
+    pred_event = run_pred.click(
+        fn=long_prediction, inputs=[textbox], outputs=prediction
+    )
+
+    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])
+    cancel_on_submit.submit(
+        lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]
+    )
+    image.clear(None, None, None, cancels=[click_event, pred_event])
+    video.start_recording(None, None, None, cancels=[click_event, pred_event])
+
+    demo.queue(max_size=20)
+    atexit.register(lambda: log_file.unlink())
+
+if __name__ == "__main__":
+    demo.launch()

demos/chatbot_multimodal/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"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", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/tuples_testcase.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def print_like_dislike(x: gr.LikeData):\n", "    print(x.index, x.value, x.liked)\n", "\n", "\n", "def add_message(history, message):\n", "    for x in message[\"files\"]:\n", "        history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n", "    if message[\"text\"] is not None:\n", "        history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n", "    return history, gr.MultimodalTextbox(value=None, interactive=False)\n", "\n", "\n", "def bot(history: list):\n", "    response = \"**That's cool!**\"\n", "    history.append({\"role\": \"assistant\", \"content\": \"\"})\n", "    for character in response:\n", "        history[-1][\"content\"] += character\n", "        time.sleep(0.05)\n", "        yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot(elem_id=\"chatbot\", bubble_full_width=False, type=\"messages\")\n", "\n", "    chat_input = gr.MultimodalTextbox(\n", "        interactive=True,\n", "        file_count=\"multiple\",\n", "        placeholder=\"Enter message or upload file...\",\n", "        show_label=False,\n", "        sources=[\"microphone\", \"upload\"],\n", "    )\n", "\n", "    chat_msg = chat_input.submit(\n", "        add_message, [chatbot, chat_input], [chatbot, chat_input]\n", "    )\n", "    bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\n", "    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n", "\n", "    chatbot.like(print_like_dislike, None, None, like_user_message=True)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/chatbot_multimodal/run.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import time
+
+# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
+
+
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+
+def add_message(history, message):
+    for x in message["files"]:
+        history.append({"role": "user", "content": {"path": x}})
+    if message["text"] is not None:
+        history.append({"role": "user", "content": message["text"]})
+    return history, gr.MultimodalTextbox(value=None, interactive=False)
+
+
+def bot(history: list):
+    response = "**That's cool!**"
+    history.append({"role": "assistant", "content": ""})
+    for character in response:
+        history[-1]["content"] += character
+        time.sleep(0.05)
+        yield history
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(elem_id="chatbot", bubble_full_width=False, type="messages")
+
+    chat_input = gr.MultimodalTextbox(
+        interactive=True,
+        file_count="multiple",
+        placeholder="Enter message or upload file...",
+        show_label=False,
+        sources=["microphone", "upload"],
+    )
+
+    chat_msg = chat_input.submit(
+        add_message, [chatbot, chat_input], [chatbot, chat_input]
+    )
+    bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
+    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
+
+    chatbot.like(print_like_dislike, None, None, like_user_message=True)
+
+if __name__ == "__main__":
+    demo.launch()

demos/chatbot_multimodal/tuples_testcase.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import plotly.express as px
+
+# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
+
+def random_plot():
+    df = px.data.iris()
+    fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
+                    size='petal_length', hover_data=['petal_width'])
+    return fig
+
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+def add_message(history, message):
+    for x in message["files"]:
+        history.append(((x,), None))
+    if message["text"] is not None:
+        history.append((message["text"], None))
+    return history, gr.MultimodalTextbox(value=None, interactive=False)
+
+def bot(history):
+    history[-1][1] = "Cool!"
+    return history
+
+fig = random_plot()
+
+with gr.Blocks(fill_height=True) as demo:
+    chatbot = gr.Chatbot(
+        elem_id="chatbot",
+        bubble_full_width=False,
+        scale=1,
+    )
+
+    chat_input = gr.MultimodalTextbox(interactive=True,
+                                      file_count="multiple",
+                                      placeholder="Enter message or upload file...", show_label=False)
+
+    chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
+    bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
+    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
+
+    chatbot.like(print_like_dislike, None, None)
+
+if __name__ == "__main__":
+    demo.launch()

demos/chatinterface_streaming_echo/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_streaming_echo"]}, {"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 time\n", "import gradio as gr\n", "\n", "def slow_echo(message, history):\n", "    for i in range(len(message)):\n", "        time.sleep(0.05)\n", "        yield \"You typed: \" + message[: i + 1]\n", "\n", "demo = gr.ChatInterface(slow_echo, type=\"messages\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/chatinterface_streaming_echo/run.py

@@ -0,0 +1,12 @@
+import time
+import gradio as gr
+
+def slow_echo(message, history):
+    for i in range(len(message)):
+        time.sleep(0.05)
+        yield "You typed: " + message[: i + 1]
+
+demo = gr.ChatInterface(slow_echo, type="messages")
+
+if __name__ == "__main__":
+    demo.launch()

demos/clear_components/requirements.txt

@@ -0,0 +1,3 @@
+numpy
+pandas
+matplotlib

demos/clear_components/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clear_components"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy pandas matplotlib "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def random_plot():\n", "    start_year = 2020\n", "    x = np.arange(start_year, start_year + 5)\n", "    year_count = x.shape[0]\n", "    plt_format = \"-\"\n", "    fig = plt.figure()\n", "    ax = fig.add_subplot(111)\n", "    series = np.arange(0, year_count, dtype=float)\n", "    series = series**2\n", "    series += np.random.rand(year_count)\n", "    ax.plot(x, series, plt_format)\n", "    return fig\n", "\n", "images = [\n", "    \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", "    \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", "    \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "]\n", "file_dir = os.path.join(os.path.abspath(''), \"..\", \"kitchen_sink\", \"files\")\n", "model3d_dir = os.path.join(os.path.abspath(''), \"..\", \"model3D\", \"files\")\n", "highlighted_text_output_1 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-MISC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistani\",\n", "        \"start\": 22,\n", "        \"end\": 31,\n", "    },\n", "]\n", "highlighted_text_output_2 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistan\",\n", "        \"start\": 22,\n", "        \"end\": 30,\n", "    },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "def random_model3d():\n", "    model_3d = random.choice(\n", "        [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != \"source.txt\"]\n", "    )\n", "    return model_3d\n", "\n", "components = [\n", "    gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", "    gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", "    gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", "    gr.Slider(\n", "        minimum=0,\n", "        maximum=1,\n", "        value=lambda: random.random(),\n", "        label=\"Slider with value func\",\n", "    ),\n", "    gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", "    gr.CheckboxGroup(\n", "        choices=[\"a\", \"b\", \"c\", \"d\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", "        label=\"Random CheckboxGroup\",\n", "    ),\n", "    gr.Radio(\n", "        choices=list(string.ascii_lowercase),\n", "        value=lambda: random.choice(string.ascii_lowercase),\n", "    ),\n", "    gr.Dropdown(\n", "        choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", "    ),\n", "    gr.Image(\n", "        value=lambda: random.choice(images)\n", "    ),\n", "    gr.Video(value=lambda: os.path.join(file_dir, \"world.mp4\")),\n", "    gr.Audio(value=lambda: os.path.join(file_dir, \"cantina.wav\")),\n", "    gr.File(\n", "        value=lambda: random.choice(\n", "            [os.path.join(file_dir, img) for img in os.listdir(file_dir)]\n", "        )\n", "    ),\n", "    gr.Dataframe(\n", "        value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"])  # type: ignore\n", "    ),\n", "    gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", "    gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", "    gr.HighlightedText(\n", "        value=lambda: random.choice(\n", "            [\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", "            ]\n", "        ),\n", "    ),\n", "    gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", "    gr.HTML(\n", "        value=lambda: random.choice(\n", "            [\n", "                '<p style=\"color:red;\">I am red</p>',\n", "                '<p style=\"color:blue;\">I am blue</p>',\n", "            ]\n", "        )\n", "    ),\n", "    gr.Gallery(\n", "        value=lambda: images\n", "    ),\n", "    gr.Model3D(value=random_model3d),\n", "    gr.Plot(value=random_plot),\n", "    gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "def evaluate_values(*args):\n", "    are_false = []\n", "    for a in args:\n", "        if isinstance(a, (pd.DataFrame, np.ndarray)):\n", "            are_false.append(not a.any().any())  # type: ignore\n", "        elif isinstance(a, str) and a.startswith(\"#\"):\n", "            are_false.append(a == \"#000000\")\n", "        else:\n", "            are_false.append(not a)\n", "    return all(are_false)\n", "\n", "with gr.Blocks() as demo:\n", "    for i, component in enumerate(components):\n", "        component.label = f\"component_{str(i).zfill(2)}\"\n", "        component.render()\n", "    clear = gr.ClearButton(value=\"Clear\", components=components)\n", "    result = gr.Textbox(label=\"Are all cleared?\")\n", "    hide = gr.Button(value=\"Hide\")\n", "    reveal = gr.Button(value=\"Reveal\")\n", "    clear_button_and_components = components + [clear]\n", "    hide.click(\n", "        lambda: [c.__class__(visible=False) for c in clear_button_and_components],\n", "        inputs=[],\n", "        outputs=clear_button_and_components\n", "    )\n", "    reveal.click(\n", "        lambda: [c.__class__(visible=True) for c in clear_button_and_components],\n", "        inputs=[],\n", "        outputs=clear_button_and_components\n", "    )\n", "    get_value = gr.Button(value=\"Get Values\")\n", "    get_value.click(evaluate_values, components, result)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/clear_components/run.py

@@ -0,0 +1,174 @@
+import gradio as gr
+from datetime import datetime
+import os
+import random
+import string
+import pandas as pd
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def random_plot():
+    start_year = 2020
+    x = np.arange(start_year, start_year + 5)
+    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
+
+images = [
+    "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
+    "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
+    "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
+]
+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
+
+components = [
+    gr.Textbox(value=lambda: datetime.now(), label="Current Time"),
+    gr.Number(value=lambda: random.random(), label="Random Percentage"),
+    gr.Slider(minimum=0, maximum=100, 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(images)
+    ),
+    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(5)}, columns=["one", "two", "three"])  # type: ignore
+    ),
+    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: images
+    ),
+    gr.Model3D(value=random_model3d),
+    gr.Plot(value=random_plot),
+    gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"),
+]
+
+def evaluate_values(*args):
+    are_false = []
+    for a in args:
+        if isinstance(a, (pd.DataFrame, np.ndarray)):
+            are_false.append(not a.any().any())  # type: ignore
+        elif isinstance(a, str) and a.startswith("#"):
+            are_false.append(a == "#000000")
+        else:
+            are_false.append(not a)
+    return all(are_false)
+
+with gr.Blocks() as demo:
+    for i, component in enumerate(components):
+        component.label = f"component_{str(i).zfill(2)}"
+        component.render()
+    clear = gr.ClearButton(value="Clear", components=components)
+    result = gr.Textbox(label="Are all cleared?")
+    hide = gr.Button(value="Hide")
+    reveal = gr.Button(value="Reveal")
+    clear_button_and_components = components + [clear]
+    hide.click(
+        lambda: [c.__class__(visible=False) for c in clear_button_and_components],
+        inputs=[],
+        outputs=clear_button_and_components
+    )
+    reveal.click(
+        lambda: [c.__class__(visible=True) for c in clear_button_and_components],
+        inputs=[],
+        outputs=clear_button_and_components
+    )
+    get_value = gr.Button(value="Get Values")
+    get_value.click(evaluate_values, components, result)
+
+if __name__ == "__main__":
+    demo.launch()

demos/code/file.css

@@ -0,0 +1,11 @@
+.class {
+  color: blue;
+}
+
+#id {
+  color: pink;
+}
+
+div {
+  color: purple;
+}

demos/code/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: code"]}, {"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", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/code/file.css"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "from time import sleep\n", "\n", "css_file = os.path.join(os.path.abspath(''), \"file.css\")\n", "\n", "def set_lang(language):\n", "    print(language)\n", "    return gr.Code(language=language)\n", "\n", "def set_lang_from_path():\n", "    sleep(1)\n", "    return gr.Code(open(css_file).read(), language=\"css\")\n", "\n", "def code(language, code):\n", "    return gr.Code(code, language=language)\n", "\n", "io = gr.Interface(lambda x: x, \"code\", \"code\")\n", "\n", "with gr.Blocks() as demo:\n", "    lang = gr.Dropdown(value=\"python\", choices=gr.Code.languages)\n", "    with gr.Row():\n", "        code_in = gr.Code(\n", "            language=\"python\",\n", "            label=\"Input\",\n", "            value='def all_odd_elements(sequence):\\n    \"\"\"Returns every odd element of the sequence.\"\"\"',\n", "        )\n", "        code_out = gr.Code(label=\"Output\")\n", "    btn = gr.Button(\"Run\")\n", "    btn_two = gr.Button(\"Load File\")\n", "\n", "    lang.change(set_lang, inputs=lang, outputs=code_in)\n", "    btn.click(code, inputs=[lang, code_in], outputs=code_out)\n", "    btn_two.click(set_lang_from_path, inputs=None, outputs=code_out)\n", "    io.render()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/code/run.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import os
+from time import sleep
+
+css_file = os.path.join(os.path.dirname(__file__), "file.css")
+
+def set_lang(language):
+    print(language)
+    return gr.Code(language=language)
+
+def set_lang_from_path():
+    sleep(1)
+    return gr.Code(open(css_file).read(), language="css")
+
+def code(language, code):
+    return gr.Code(code, language=language)
+
+io = gr.Interface(lambda x: x, "code", "code")
+
+with gr.Blocks() as demo:
+    lang = gr.Dropdown(value="python", choices=gr.Code.languages)
+    with gr.Row():
+        code_in = gr.Code(
+            language="python",
+            label="Input",
+            value='def all_odd_elements(sequence):\n    """Returns every odd element of the sequence."""',
+        )
+        code_out = gr.Code(label="Output")
+    btn = gr.Button("Run")
+    btn_two = gr.Button("Load File")
+
+    lang.change(set_lang, inputs=lang, outputs=code_in)
+    btn.click(code, inputs=[lang, code_in], outputs=code_out)
+    btn_two.click(set_lang_from_path, inputs=None, outputs=code_out)
+    io.render()
+
+if __name__ == "__main__":
+    demo.launch()

demos/fake_diffusion_with_gif/requirements.txt

@@ -0,0 +1,3 @@
+numpy
+requests
+Pillow

demos/fake_diffusion_with_gif/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_diffusion_with_gif"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy requests Pillow "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "import os\n", "from PIL import Image\n", "import requests\n", "from io import BytesIO\n", "\n", "def create_gif(images):\n", "    pil_images = []\n", "    for image in images:\n", "        if isinstance(image, str):\n", "            response = requests.get(image)\n", "            image = Image.open(BytesIO(response.content))\n", "        else:\n", "            image = Image.fromarray((image * 255).astype(np.uint8))\n", "        pil_images.append(image)\n", "    fp_out = os.path.join(os.path.abspath(''), \"image.gif\")\n", "    img = pil_images.pop(0)\n", "    img.save(fp=fp_out, format='GIF', append_images=pil_images,\n", "            save_all=True, duration=400, loop=0)\n", "    return fp_out\n", "\n", "def fake_diffusion(steps):\n", "    rng = np.random.default_rng()\n", "    images = []\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = rng.random((600, 600, 3))\n", "        images.append(image)\n", "        yield image, gr.Image(visible=False)\n", "\n", "    time.sleep(1)\n", "    image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\"\n", "    images.append(image)\n", "    gif_path = create_gif(images)\n", "\n", "    yield image, gr.Image(value=gif_path, visible=True)\n", "\n", "demo = gr.Interface(fake_diffusion,\n", "                    inputs=gr.Slider(1, 10, 3, step=1),\n", "                    outputs=[\"image\", gr.Image(label=\"All Images\", visible=False)])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/fake_diffusion_with_gif/run.py

@@ -0,0 +1,45 @@
+import gradio as gr
+import numpy as np
+import time
+import os
+from PIL import Image
+import requests
+from io import BytesIO
+
+def create_gif(images):
+    pil_images = []
+    for image in images:
+        if isinstance(image, str):
+            response = requests.get(image)
+            image = Image.open(BytesIO(response.content))
+        else:
+            image = Image.fromarray((image * 255).astype(np.uint8))
+        pil_images.append(image)
+    fp_out = os.path.join(os.path.dirname(__file__), "image.gif")
+    img = pil_images.pop(0)
+    img.save(fp=fp_out, format='GIF', append_images=pil_images,
+            save_all=True, duration=400, loop=0)
+    return fp_out
+
+def fake_diffusion(steps):
+    rng = np.random.default_rng()
+    images = []
+    for _ in range(steps):
+        time.sleep(1)
+        image = rng.random((600, 600, 3))
+        images.append(image)
+        yield image, gr.Image(visible=False)
+
+    time.sleep(1)
+    image = "https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg"
+    images.append(image)
+    gif_path = create_gif(images)
+
+    yield image, gr.Image(value=gif_path, visible=True)
+
+demo = gr.Interface(fake_diffusion,
+                    inputs=gr.Slider(1, 10, 3, step=1),
+                    outputs=["image", gr.Image(label="All Images", visible=False)])
+
+if __name__ == "__main__":
+    demo.launch()

demos/fake_gan/DESCRIPTION.md

@@ -0,0 +1 @@
+This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/

demos/fake_gan/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_gan\n", "### This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/\n", "        "]}, {"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": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import random\n", "\n", "import gradio as gr\n", "\n", "def fake_gan():\n", "    images = [\n", "        (random.choice(\n", "            [\n", "                \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg\",\n", "                \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg\",\n", "                \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg\",\n", "                \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg\",\n", "                \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\",\n", "            ]\n", "        ), f\"label {i}\")\n", "        for i in range(3)\n", "    ]\n", "    return images\n", "\n", "with gr.Blocks() as demo:\n", "    gallery = gr.Gallery(\n", "        label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", "    , columns=[3], rows=[1], object_fit=\"contain\", height=\"auto\")\n", "    btn = gr.Button(\"Generate images\", scale=0)\n", "\n", "    btn.click(fake_gan, None, gallery)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/fake_gan/run.py

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+# This demo needs to be run from the repo folder.
+# python demo/fake_gan/run.py
+import random
+
+import gradio as gr
+
+def fake_gan():
+    images = [
+        (random.choice(
+            [
+                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg",
+                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg",
+                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg",
+                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg",
+                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg",
+            ]
+        ), f"label {i}")
+        for i in range(3)
+    ]
+    return images
+
+with gr.Blocks() as demo:
+    gallery = gr.Gallery(
+        label="Generated images", show_label=False, elem_id="gallery"
+    , columns=[3], rows=[1], object_fit="contain", height="auto")
+    btn = gr.Button("Generate images", scale=0)
+
+    btn.click(fake_gan, None, gallery)
+
+if __name__ == "__main__":
+    demo.launch()