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README.md

@@ -5,7 +5,7 @@ emoji: 💩
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.16.0
+sdk_version: 4.19.0
 app_file: app.py
 pinned: false
 ---

demos/chatinterface_streaming_echo/run.ipynb

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

demos/chatinterface_streaming_echo/run.py

@@ -1,10 +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]
+        yield "You typed: " + message[: i + 1]
+
 
 demo = gr.ChatInterface(slow_echo).queue()
 

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 "]}, {"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/fake_diffusion_with_gif/image.gif"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "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", "\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", "\n", "def fake_diffusion(steps):\n", "    images = []\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = np.random.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", "\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", "demo.queue()\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 "]}, {"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/fake_diffusion_with_gif/image.gif"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "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", "\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", "\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", "\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", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/fake_diffusion_with_gif/run.py

@@ -24,10 +24,11 @@ def create_gif(images):
 
 
 def fake_diffusion(steps):
+    rng = np.random.default_rng()
     images = []
     for _ in range(steps):
         time.sleep(1)
-        image = np.random.random((600, 600, 3))
+        image = rng.random((600, 600, 3))
         images.append(image)
         yield image, gr.Image(visible=False)
 

demos/file_explorer_component_events/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: file_explorer_component_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('dir1')\n", "!wget -q -O dir1/bar.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/bar.txt\n", "!wget -q -O dir1/foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/foo.txt\n", "os.mkdir('dir2')\n", "!wget -q -O dir2/baz.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/baz.png\n", "!wget -q -O dir2/foo.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/foo.png\n", "os.mkdir('dir3')\n", "!wget -q -O dir3/dir3_bar.log https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_bar.log\n", "!wget -q -O dir3/dir3_foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_foo.txt\n", "!wget -q -O dir3/dir4 https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from pathlib import Path\n", "\n", "base_root = Path(__file__).parent.resolve()\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        dd = gr.Dropdown(label=\"Select File Explorer Root\",\n", "                        value=str(base_root / \"dir1\"),\n", "                        choices=[str(base_root / \"dir1\"), str(base_root / \"dir2\"),\n", "                                 str(base_root / \"dir3\")])\n", "        with gr.Group():\n", "            txt_only_glob = gr.Checkbox(label=\"Show only text files\", value=False)\n", "            ignore_txt_in_glob = gr.Checkbox(label=\"Ignore text files in glob\", value=False)\n", "\n", "    fe = gr.FileExplorer(root_dir=str(base_root / \"dir1\"),\n", "                         glob=\"**/*\", interactive=True)\n", "    textbox = gr.Textbox(label=\"Selected Directory\")\n", "    run = gr.Button(\"Run\")\n", "    \n", "    txt_only_glob.select(lambda s: gr.FileExplorer(glob=\"*.txt\" if s else \"*\") ,\n", "                         inputs=[txt_only_glob], outputs=[fe])\n", "    ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob=\"*.txt\" if s else None),\n", "                            inputs=[ignore_txt_in_glob], outputs=[fe])            \n", "\n", "    dd.select(lambda s: gr.FileExplorer(root=s), inputs=[dd], outputs=[fe])\n", "    run.click(lambda s: \",\".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])\n", "\n", "    with gr.Row():\n", "        a = gr.Textbox(elem_id=\"input-box\")\n", "        a.change(lambda x: x, inputs=[a])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/file_explorer_component_events/run.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from pathlib import Path
+
+base_root = Path(__file__).parent.resolve()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        dd = gr.Dropdown(label="Select File Explorer Root",
+                        value=str(base_root / "dir1"),
+                        choices=[str(base_root / "dir1"), str(base_root / "dir2"),
+                                 str(base_root / "dir3")])
+        with gr.Group():
+            txt_only_glob = gr.Checkbox(label="Show only text files", value=False)
+            ignore_txt_in_glob = gr.Checkbox(label="Ignore text files in glob", value=False)
+
+    fe = gr.FileExplorer(root_dir=str(base_root / "dir1"),
+                         glob="**/*", interactive=True)
+    textbox = gr.Textbox(label="Selected Directory")
+    run = gr.Button("Run")
+    
+    txt_only_glob.select(lambda s: gr.FileExplorer(glob="*.txt" if s else "*") ,
+                         inputs=[txt_only_glob], outputs=[fe])
+    ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob="*.txt" if s else None),
+                            inputs=[ignore_txt_in_glob], outputs=[fe])            
+
+    dd.select(lambda s: gr.FileExplorer(root=s), inputs=[dd], outputs=[fe])
+    run.click(lambda s: ",".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])
+
+    with gr.Row():
+        a = gr.Textbox(elem_id="input-box")
+        a.change(lambda x: x, inputs=[a])
+
+
+if __name__ == "__main__":
+    demo.launch()

demos/model3D/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: model3D"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/Bunny.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Bunny.obj\n", "!wget -q -O files/Duck.glb https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Duck.glb\n", "!wget -q -O files/Fox.gltf https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Fox.gltf\n", "!wget -q -O files/face.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/face.obj\n", "!wget -q -O files/sofia.stl https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/sofia.stl\n", "!wget -q -O files/source.txt https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/source.txt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def load_mesh(mesh_file_name):\n", "    return mesh_file_name\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=load_mesh,\n", "    inputs=gr.Model3D(),\n", "    outputs=gr.Model3D(\n", "            clear_color=[0.0, 0.0, 0.0, 0.0],  label=\"3D Model\"),\n", "    examples=[\n", "        [os.path.join(os.path.abspath(''), \"files/Bunny.obj\")],\n", "        [os.path.join(os.path.abspath(''), \"files/Duck.glb\")],\n", "        [os.path.join(os.path.abspath(''), \"files/Fox.gltf\")],\n", "        [os.path.join(os.path.abspath(''), \"files/face.obj\")],\n", "        [os.path.join(os.path.abspath(''), \"files/sofia.stl\")],\n", "    ],\n", "    cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: model3D"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/Bunny.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Bunny.obj\n", "!wget -q -O files/Duck.glb https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Duck.glb\n", "!wget -q -O files/Fox.gltf https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Fox.gltf\n", "!wget -q -O files/face.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/face.obj\n", "!wget -q -O files/sofia.stl https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/sofia.stl\n", "!wget -q -O files/source.txt https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/source.txt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def load_mesh(mesh_file_name):\n", "    return mesh_file_name\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=load_mesh,\n", "    inputs=gr.Model3D(),\n", "    outputs=gr.Model3D(\n", "            clear_color=[0.0, 0.0, 0.0, 0.0],  label=\"3D Model\"),\n", "    examples=[\n", "        [os.path.join(os.path.abspath(''), \"files/Bunny.obj\")],\n", "        [os.path.join(os.path.abspath(''), \"files/Duck.glb\")],\n", "        [os.path.join(os.path.abspath(''), \"files/Fox.gltf\")],\n", "        [os.path.join(os.path.abspath(''), \"files/face.obj\")],\n", "        [os.path.join(os.path.abspath(''), \"files/sofia.stl\")],\n", "        [\"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat\"],\n", "        [\"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply\"],\n", "    ],\n", "    cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/model3D/run.py

@@ -17,6 +17,8 @@ demo = gr.Interface(
         [os.path.join(os.path.dirname(__file__), "files/Fox.gltf")],
         [os.path.join(os.path.dirname(__file__), "files/face.obj")],
         [os.path.join(os.path.dirname(__file__), "files/sofia.stl")],
+        ["https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat"],
+        ["https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply"],
     ],
     cache_examples=True
 )

demos/stt_or_tts/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stt_or_tts"]}, {"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", "tts_examples = [\n", "    \"I love learning machine learning\",\n", "    \"How do you do?\",\n", "]\n", "\n", "tts_demo = gr.load(\n", "    \"huggingface/facebook/fastspeech2-en-ljspeech\",\n", "    title=None,\n", "    examples=tts_examples,\n", "    description=\"Give me something to say!\",\n", "    cache_examples=False\n", ")\n", "\n", "stt_demo = gr.load(\n", "    \"huggingface/facebook/wav2vec2-base-960h\",\n", "    title=None,\n", "    inputs=\"mic\",\n", "    description=\"Let me try to guess what you're saying!\",\n", ")\n", "\n", "demo = gr.TabbedInterface([tts_demo, stt_demo], [\"Text-to-speech\", \"Speech-to-text\"])\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: stt_or_tts"]}, {"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", "tts_examples = [\n", "    \"I love learning machine learning\",\n", "    \"How do you do?\",\n", "]\n", "\n", "tts_demo = gr.load(\n", "    \"huggingface/facebook/fastspeech2-en-ljspeech\",\n", "    title=None,\n", "    examples=tts_examples,\n", "    description=\"Give me something to say!\",\n", ")\n", "\n", "stt_demo = gr.load(\n", "    \"huggingface/facebook/wav2vec2-base-960h\",\n", "    title=None,\n", "    inputs=gr.Microphone(type=\"filepath\"),\n", "    description=\"Let me try to guess what you're saying!\",\n", ")\n", "\n", "demo = gr.TabbedInterface([tts_demo, stt_demo], [\"Text-to-speech\", \"Speech-to-text\"])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/stt_or_tts/run.py

@@ -10,13 +10,12 @@ tts_demo = gr.load(
     title=None,
     examples=tts_examples,
     description="Give me something to say!",
-    cache_examples=False
 )
 
 stt_demo = gr.load(
     "huggingface/facebook/wav2vec2-base-960h",
     title=None,
-    inputs="mic",
+    inputs=gr.Microphone(type="filepath"),
     description="Let me try to guess what you're saying!",
 )
 

requirements.txt

@@ -1,6 +1,6 @@
 
-gradio-client @ git+https://github.com/gradio-app/gradio@8ff3a228bca9e09a634d9c880031b3f0592a280c#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/8ff3a228bca9e09a634d9c880031b3f0592a280c/gradio-4.16.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@766f25ab18c1fa401296b17e779a3036ccc7729e#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/766f25ab18c1fa401296b17e779a3036ccc7729e/gradio-4.19.0-py3-none-any.whl
 pypistats==1.1.0
 plotly==5.10.0
 opencv-python==4.6.0.66