Upload folder using huggingface_hub

.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
+files/world.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Chatbot Core Components Main
-emoji: 🐠
-colorFrom: gray
-colorTo: pink
+title: chatbot_core_components_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 4.36.1
-app_file: app.py
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

files/sample.txt

@@ -0,0 +1 @@
+hello friends

files/world.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71944d7430c461f0cd6e7fd10cee7eb72786352a3678fc7bc0ae3d410f72aece
+size 1570024

requirements.txt

@@ -0,0 +1,2 @@
+gradio-client @ git+https://github.com/gradio-app/gradio@bb504b494947a287d6386e0e7ead3860c0f15223#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/bb504b494947a287d6386e0e7ead3860c0f15223/gradio-4.36.1-py3-none-any.whl

run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_core_components"]}, {"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/audio.wav https://github.com/gradio-app/gradio/raw/main/demo/chatbot_core_components/files/audio.wav\n", "!wget -q -O files/avatar.png https://github.com/gradio-app/gradio/raw/main/demo/chatbot_core_components/files/avatar.png\n", "!wget -q -O files/sample.txt https://github.com/gradio-app/gradio/raw/main/demo/chatbot_core_components/files/sample.txt\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/chatbot_core_components/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import plotly.express as px\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 random_plot():\n", "    df = px.data.iris()\n", "    fig = px.scatter(\n", "        df,\n", "        x=\"sepal_width\",\n", "        y=\"sepal_length\",\n", "        color=\"species\",\n", "        size=\"petal_length\",\n", "        hover_data=[\"petal_width\"],\n", "    )\n", "    return fig\n", "\n", "\n", "def print_like_dislike(x: gr.LikeData):\n", "    print(x.index, x.value, x.liked)\n", "\n", "\n", "def random_bokeh_plot():\n", "    from bokeh.models import ColumnDataSource, Whisker\n", "    from bokeh.plotting import figure\n", "    from bokeh.sampledata.autompg2 import autompg2 as df\n", "    from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "    classes = list(sorted(df[\"class\"].unique()))\n", "\n", "    p = figure(\n", "        height=400,\n", "        x_range=classes,\n", "        background_fill_color=\"#efefef\",\n", "        title=\"Car class vs HWY mpg with quintile ranges\",\n", "    )\n", "    p.xgrid.grid_line_color = None\n", "\n", "    g = df.groupby(\"class\")\n", "    upper = g.hwy.quantile(0.80)\n", "    lower = g.hwy.quantile(0.20)\n", "    source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", "    error = Whisker(\n", "        base=\"base\",\n", "        upper=\"upper\",\n", "        lower=\"lower\",\n", "        source=source,\n", "        level=\"annotation\",\n", "        line_width=2,\n", "    )\n", "    error.upper_head.size = 20\n", "    error.lower_head.size = 20\n", "    p.add_layout(error)\n", "\n", "    p.circle(\n", "        jitter(\"class\", 0.3, range=p.x_range),\n", "        \"hwy\",\n", "        source=df,\n", "        alpha=0.5,\n", "        size=13,\n", "        line_color=\"white\",\n", "        color=factor_cmap(\"class\", \"Light6\", classes),\n", "    )\n", "    return p\n", "\n", "\n", "def random_matplotlib_plot():\n", "    import numpy as np\n", "    import pandas as pd\n", "    import matplotlib.pyplot as plt\n", "\n", "    countries = [\"USA\", \"Canada\", \"Mexico\", \"UK\"]\n", "    months = [\"January\", \"February\", \"March\", \"April\", \"May\"]\n", "    m = months.index(\"January\")\n", "    r = 3.2\n", "    start_day = 30 * m\n", "    final_day = 30 * (m + 1)\n", "    x = np.arange(start_day, final_day + 1)\n", "    pop_count = {\"USA\": 350, \"Canada\": 40, \"Mexico\": 300, \"UK\": 120}\n", "    df = pd.DataFrame({\"day\": x})\n", "    for country in countries:\n", "        df[country] = x ** (r) * (pop_count[country] + 1)\n", "\n", "    fig = plt.figure()\n", "    plt.plot(df[\"day\"], df[countries].to_numpy())\n", "    plt.title(\"Outbreak in \" + \"January\")\n", "    plt.ylabel(\"Cases\")\n", "    plt.xlabel(\"Days since Day 0\")\n", "    plt.legend(countries)\n", "    return fig\n", "\n", "\n", "def add_message(history, message):\n", "    for x in message[\"files\"]:\n", "        history.append(((x,), None))\n", "    if message[\"text\"] is not None:\n", "        history.append((message[\"text\"], None))\n", "    return history, gr.MultimodalTextbox(value=None, interactive=False)\n", "\n", "\n", "def bot(history, response_type):\n", "    if response_type == \"plot\":\n", "        history[-1][1] = gr.Plot(random_plot())\n", "    elif response_type == \"bokeh_plot\":\n", "        history[-1][1] = gr.Plot(random_bokeh_plot())\n", "    elif response_type == \"matplotlib_plot\":\n", "        history[-1][1] = gr.Plot(random_matplotlib_plot())\n", "    elif response_type == \"gallery\":\n", "        history[-1][1] = gr.Gallery(\n", "            [os.path.join(\"files\", \"avatar.png\"), os.path.join(\"files\", \"avatar.png\")]\n", "        )\n", "    elif response_type == \"image\":\n", "        history[-1][1] = gr.Image(os.path.join(\"files\", \"avatar.png\"))\n", "    elif response_type == \"video\":\n", "        history[-1][1] = gr.Video(os.path.join(\"files\", \"world.mp4\"))\n", "    elif response_type == \"audio\":\n", "        history[-1][1] = gr.Audio(os.path.join(\"files\", \"audio.wav\"))\n", "    elif response_type == \"audio_file\":\n", "        history[-1][1] = (os.path.join(\"files\", \"audio.wav\"), \"description\")\n", "    elif response_type == \"image_file\":\n", "        history[-1][1] = (os.path.join(\"files\", \"avatar.png\"), \"description\")\n", "    elif response_type == \"video_file\":\n", "        history[-1][1] = (os.path.join(\"files\", \"world.mp4\"), \"description\")\n", "    elif response_type == \"txt_file\":\n", "        history[-1][1] = (os.path.join(\"files\", \"sample.txt\"), \"description\")\n", "    else:\n", "        history[-1][1] = \"Cool!\"\n", "    return history\n", "\n", "\n", "fig = random_plot()\n", "\n", "with gr.Blocks(fill_height=True) as demo:\n", "    chatbot = gr.Chatbot(\n", "        elem_id=\"chatbot\",\n", "        bubble_full_width=False,\n", "        scale=1,\n", "    )\n", "    response_type = gr.Radio(\n", "        [\n", "            \"audio_file\",\n", "            \"image_file\",\n", "            \"video_file\",\n", "            \"txt_file\",\n", "            \"plot\",\n", "            \"matplotlib_plot\",\n", "            \"bokeh_plot\",\n", "            \"image\",\n", "            \"text\",\n", "            \"gallery\",\n", "            \"video\",\n", "            \"audio\",\n", "        ],\n", "        value=\"text\",\n", "        label=\"Response Type\",\n", "    )\n", "\n", "    chat_input = gr.MultimodalTextbox(\n", "        interactive=True,\n", "        placeholder=\"Enter message or upload file...\",\n", "        show_label=False,\n", "    )\n", "\n", "    chat_msg = chat_input.submit(\n", "        add_message, [chatbot, chat_input], [chatbot, chat_input]\n", "    )\n", "    bot_msg = chat_msg.then(\n", "        bot, [chatbot, response_type], chatbot, api_name=\"bot_response\"\n", "    )\n", "    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n", "\n", "    chatbot.like(print_like_dislike, None, None)\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -0,0 +1,179 @@
+import gradio as gr
+import os
+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 random_bokeh_plot():
+    from bokeh.models import ColumnDataSource, Whisker
+    from bokeh.plotting import figure
+    from bokeh.sampledata.autompg2 import autompg2 as df
+    from bokeh.transform import factor_cmap, jitter, factor_mark
+
+    classes = list(sorted(df["class"].unique()))
+
+    p = figure(
+        height=400,
+        x_range=classes,
+        background_fill_color="#efefef",
+        title="Car class vs HWY mpg with quintile ranges",
+    )
+    p.xgrid.grid_line_color = None
+
+    g = df.groupby("class")
+    upper = g.hwy.quantile(0.80)
+    lower = g.hwy.quantile(0.20)
+    source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))
+
+    error = Whisker(
+        base="base",
+        upper="upper",
+        lower="lower",
+        source=source,
+        level="annotation",
+        line_width=2,
+    )
+    error.upper_head.size = 20
+    error.lower_head.size = 20
+    p.add_layout(error)
+
+    p.circle(
+        jitter("class", 0.3, range=p.x_range),
+        "hwy",
+        source=df,
+        alpha=0.5,
+        size=13,
+        line_color="white",
+        color=factor_cmap("class", "Light6", classes),
+    )
+    return p
+
+
+def random_matplotlib_plot():
+    import numpy as np
+    import pandas as pd
+    import matplotlib.pyplot as plt
+
+    countries = ["USA", "Canada", "Mexico", "UK"]
+    months = ["January", "February", "March", "April", "May"]
+    m = months.index("January")
+    r = 3.2
+    start_day = 30 * m
+    final_day = 30 * (m + 1)
+    x = np.arange(start_day, final_day + 1)
+    pop_count = {"USA": 350, "Canada": 40, "Mexico": 300, "UK": 120}
+    df = pd.DataFrame({"day": x})
+    for country in countries:
+        df[country] = x ** (r) * (pop_count[country] + 1)
+
+    fig = plt.figure()
+    plt.plot(df["day"], df[countries].to_numpy())
+    plt.title("Outbreak in " + "January")
+    plt.ylabel("Cases")
+    plt.xlabel("Days since Day 0")
+    plt.legend(countries)
+    return fig
+
+
+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, response_type):
+    if response_type == "plot":
+        history[-1][1] = gr.Plot(random_plot())
+    elif response_type == "bokeh_plot":
+        history[-1][1] = gr.Plot(random_bokeh_plot())
+    elif response_type == "matplotlib_plot":
+        history[-1][1] = gr.Plot(random_matplotlib_plot())
+    elif response_type == "gallery":
+        history[-1][1] = gr.Gallery(
+            [os.path.join("files", "avatar.png"), os.path.join("files", "avatar.png")]
+        )
+    elif response_type == "image":
+        history[-1][1] = gr.Image(os.path.join("files", "avatar.png"))
+    elif response_type == "video":
+        history[-1][1] = gr.Video(os.path.join("files", "world.mp4"))
+    elif response_type == "audio":
+        history[-1][1] = gr.Audio(os.path.join("files", "audio.wav"))
+    elif response_type == "audio_file":
+        history[-1][1] = (os.path.join("files", "audio.wav"), "description")
+    elif response_type == "image_file":
+        history[-1][1] = (os.path.join("files", "avatar.png"), "description")
+    elif response_type == "video_file":
+        history[-1][1] = (os.path.join("files", "world.mp4"), "description")
+    elif response_type == "txt_file":
+        history[-1][1] = (os.path.join("files", "sample.txt"), "description")
+    else:
+        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,
+    )
+    response_type = gr.Radio(
+        [
+            "audio_file",
+            "image_file",
+            "video_file",
+            "txt_file",
+            "plot",
+            "matplotlib_plot",
+            "bokeh_plot",
+            "image",
+            "text",
+            "gallery",
+            "video",
+            "audio",
+        ],
+        value="text",
+        label="Response Type",
+    )
+
+    chat_input = gr.MultimodalTextbox(
+        interactive=True,
+        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, response_type], chatbot, api_name="bot_response"
+    )
+    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
+
+    chatbot.like(print_like_dislike, None, None)
+
+demo.queue()
+if __name__ == "__main__":
+    demo.launch()