Commit 1: Add 50 file(s)

demos/audio_debugger/run.ipynb

@@ -1 +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": ["import gradio as gr\n", "import subprocess\n", "\n", "# get_audio returns the path to the audio file\n", "audio_file = gr.get_audio(\"cantina.wav\")\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tab(\"Audio\"):\n", "        gr.Audio(audio_file, buttons=[\"download\"])\n", "    with gr.Tab(\"Interface\"):\n", "        gr.Interface(\n", "            lambda x: x, \n", "            gr.Audio(),\n", "            gr.Audio(),\n", "            examples=[audio_file], \n", "            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}
+{"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": ["import gradio as gr\n", "import subprocess\n", "\n", "# get_audio returns the path to the audio file\n", "audio_file = gr.get_audio(\"cantina.wav\")\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tab(\"Audio\"):\n", "        gr.Audio(audio_file, buttons=[\"download\"])\n", "    with gr.Tab(\"Interface\"):\n", "        gr.Interface(\n", "            lambda x: x, \n", "            gr.Audio(),\n", "            gr.Audio(),\n", "            examples=[audio_file], \n", "            cache_examples=True,\n", "            api_name=\"predict\"\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", "            api_name=\"predict\"\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", "            api_name=\"predict\"\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

@@ -13,7 +13,8 @@ with gr.Blocks() as demo:
             gr.Audio(),
             gr.Audio(),
             examples=[audio_file], 
-            cache_examples=True
+            cache_examples=True,
+            api_name="predict"
         )
     with gr.Tab("Streaming"):
         gr.Interface(
@@ -22,6 +23,7 @@ with gr.Blocks() as demo:
             "audio",
             examples=[audio_file],
             cache_examples=True,
+            api_name="predict"
         )
     with gr.Tab("console"):
         ip = gr.Textbox(label="User IP Address")
@@ -31,6 +33,7 @@ with gr.Blocks() as demo:
             .strip(),
             "text",
             "text",
+            api_name="predict"
         )
 
     def get_ip(request: gr.Request):

demos/calculator/run.ipynb

@@ -1 +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}
+{"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", "    api_name=\"predict\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/calculator/run.py

@@ -28,6 +28,7 @@ demo = gr.Interface(
     ],
     title="Toy Calculator",
     description="Here's a sample toy calculator.",
+    api_name="predict"
 )
 
 if __name__ == "__main__":

demos/chatbot_multimodal/run.ipynb

@@ -1 +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": ["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\")\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}
+{"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": ["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", "    user_msg = {\"role\": \"user\", \"content\": []}\n", "    for x in message[\"files\"]:\n", "        user_msg[\"content\"].append({\"path\": x})\n", "    if message[\"text\"] is not None:\n", "       user_msg[\"content\"].append(message[\"text\"])\n", "    history.append(user_msg)\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\")\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

@@ -9,10 +9,12 @@ def print_like_dislike(x: gr.LikeData):
 
 
 def add_message(history, message):
+    user_msg = {"role": "user", "content": []}
     for x in message["files"]:
-        history.append({"role": "user", "content": {"path": x}})
+        user_msg["content"].append({"path": x})
     if message["text"] is not None:
-        history.append({"role": "user", "content": message["text"]})
+       user_msg["content"].append(message["text"])
+    history.append(user_msg)
     return history, gr.MultimodalTextbox(value=None, interactive=False)
 
 

demos/code/run.ipynb

@@ -1 +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", "            show_line_numbers = False\n", "        )\n", "        code_out = gr.Code(label=\"Output\", show_line_numbers = True)\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}
+{"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\", api_name=\"predict\")\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", "            show_line_numbers = False\n", "        )\n", "        code_out = gr.Code(label=\"Output\", show_line_numbers = True)\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

@@ -15,7 +15,7 @@ def set_lang_from_path():
 def code(language, code):
     return gr.Code(code, language=language)
 
-io = gr.Interface(lambda x: x, "code", "code")
+io = gr.Interface(lambda x: x, "code", "code", api_name="predict")
 
 with gr.Blocks() as demo:
     lang = gr.Dropdown(value="python", choices=gr.Code.languages)

demos/fake_diffusion_with_gif/run.ipynb

@@ -1 +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}
+{"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", "                    api_name=\"predict\",)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/fake_diffusion_with_gif/run.py

@@ -39,7 +39,8 @@ def fake_diffusion(steps):
 
 demo = gr.Interface(fake_diffusion,
                     inputs=gr.Slider(1, 10, 3, step=1),
-                    outputs=["image", gr.Image(label="All Images", visible=False)])
+                    outputs=["image", gr.Image(label="All Images", visible=False)],
+                    api_name="predict",)
 
 if __name__ == "__main__":
     demo.launch()