Upload folder using huggingface_hub

helpers.py

@@ -13,7 +13,6 @@ datasets = {
 
 val = 0
 
-
 def _range(e):
     global val
     e['range'] = val
@@ -26,7 +25,6 @@ def _range(e):
 
     return e
 
-
 def _ignore_org_members(e):
     global val
     e['range_non_org'] = val
@@ -51,7 +49,6 @@ for k, v in datasets['issues'].items():
 datasets['stars'] = DatasetDict(**stars)
 datasets['issues'] = DatasetDict(**issues)
 
-
 def link_values(library_names, returned_values):
     previous_values = {library_name: None for library_name in library_names}
     for library_name in library_names:
@@ -63,13 +60,11 @@ def link_values(library_names, returned_values):
 
     return returned_values
 
-
 def running_mean(x, N, total_length=-1):
     cumsum = np.cumsum(np.insert(x, 0, 0))
     to_pad = max(total_length - len(cumsum), 0)
     return np.pad(cumsum[N:] - cumsum[:-N], (to_pad, 0)) / float(N)
 
-
 def retrieve_pip_installs(library_names, cumulated):
 
     if cumulated:
@@ -102,7 +97,6 @@ def retrieve_pip_installs(library_names, cumulated):
     output['day'] = list(returned_values.keys())
     return output
 
-
 def retrieve_stars(libraries, week_over_week):
     returned_values = {}
     dataset_dict = datasets['stars']
@@ -132,7 +126,6 @@ def retrieve_stars(libraries, week_over_week):
     output = {k: [v for i, v in enumerate(value) if i % int(len(value) / 100) == 0] for k, value in output.items()}
     return output
 
-
 def retrieve_issues(libraries, exclude_org_members, week_over_week):
 
     returned_values = {}

requirements.txt

@@ -1,3 +1,3 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@de997e67c9a7feb9e2eccebf92969366dbd67eba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/de997e67c9a7feb9e2eccebf92969366dbd67eba/gradio-4.39.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@9b42ba8f1006c05d60a62450d3036ce0d6784f86#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/9b42ba8f1006c05d60a62450d3036ce0d6784f86/gradio-4.39.0-py3-none-any.whl
 plotly

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dashboard\n", "### This demo shows how you can build an interactive dashboard with gradio. Click on a python library on the left hand side and then on the right hand side click on the metric you'd like to see plot over time. Data is pulled from HuggingFace Hub datasets.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"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/dashboard/helpers.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import plotly.express as px\n", "from helpers import retrieve_pip_installs, retrieve_stars, retrieve_issues\n", "\n", "\n", "LIBRARIES = [\"accelerate\", \"datasets\", \"diffusers\", \"evaluate\", \"gradio\", \"hub_docs\",\n", "             \"huggingface_hub\", \"optimum\", \"pytorch_image_models\", \"tokenizers\", \"transformers\"]\n", "\n", "\n", "def create_pip_plot(libraries, pip_choices):\n", "    if \"Pip\" not in pip_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_pip_installs(libraries, \"Cumulated\" in pip_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Pip installs\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_star_plot(libraries, star_choices):\n", "    if \"Stars\" not in star_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_stars(libraries, \"Week over Week\" in star_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Number of stargazers\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_issue_plot(libraries, issue_choices):\n", "    if \"Issue\" not in issue_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_issues(libraries,\n", "                             exclude_org_members=\"Exclude org members\" in issue_choices,\n", "                             week_over_week=\"Week over Week\" in issue_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Cumulated number of issues, PRs, and comments\",\n", "                   )\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select libraries to display\")\n", "            libraries = gr.CheckboxGroup(choices=LIBRARIES, show_label=False)\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select graphs to display\")\n", "            pip = gr.CheckboxGroup(choices=[\"Pip\", \"Cumulated\"], show_label=False)\n", "            stars = gr.CheckboxGroup(choices=[\"Stars\", \"Week over Week\"], show_label=False)\n", "            issues = gr.CheckboxGroup(choices=[\"Issue\", \"Exclude org members\", \"week over week\"], show_label=False)\n", "    with gr.Row():\n", "        fetch = gr.Button(value=\"Fetch\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            pip_plot = gr.Plot(visible=False)\n", "            star_plot = gr.Plot(visible=False)\n", "            issue_plot = gr.Plot(visible=False)\n", "\n", "    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)\n", "    fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)\n", "    fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dashboard\n", "### This demo shows how you can build an interactive dashboard with gradio. Click on a python library on the left hand side and then on the right hand side click on the metric you'd like to see plot over time. Data is pulled from HuggingFace Hub datasets.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"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/dashboard/helpers.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import plotly.express as px\n", "from helpers import retrieve_pip_installs, retrieve_stars, retrieve_issues\n", "\n", "LIBRARIES = [\"accelerate\", \"datasets\", \"diffusers\", \"evaluate\", \"gradio\", \"hub_docs\",\n", "             \"huggingface_hub\", \"optimum\", \"pytorch_image_models\", \"tokenizers\", \"transformers\"]\n", "\n", "def create_pip_plot(libraries, pip_choices):\n", "    if \"Pip\" not in pip_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_pip_installs(libraries, \"Cumulated\" in pip_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Pip installs\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "def create_star_plot(libraries, star_choices):\n", "    if \"Stars\" not in star_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_stars(libraries, \"Week over Week\" in star_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Number of stargazers\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "def create_issue_plot(libraries, issue_choices):\n", "    if \"Issue\" not in issue_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_issues(libraries,\n", "                             exclude_org_members=\"Exclude org members\" in issue_choices,\n", "                             week_over_week=\"Week over Week\" in issue_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Cumulated number of issues, PRs, and comments\",\n", "                   )\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select libraries to display\")\n", "            libraries = gr.CheckboxGroup(choices=LIBRARIES, show_label=False)\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select graphs to display\")\n", "            pip = gr.CheckboxGroup(choices=[\"Pip\", \"Cumulated\"], show_label=False)\n", "            stars = gr.CheckboxGroup(choices=[\"Stars\", \"Week over Week\"], show_label=False)\n", "            issues = gr.CheckboxGroup(choices=[\"Issue\", \"Exclude org members\", \"week over week\"], show_label=False)\n", "    with gr.Row():\n", "        fetch = gr.Button(value=\"Fetch\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            pip_plot = gr.Plot(visible=False)\n", "            star_plot = gr.Plot(visible=False)\n", "            issue_plot = gr.Plot(visible=False)\n", "\n", "    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)\n", "    fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)\n", "    fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -3,11 +3,9 @@ import pandas as pd
 import plotly.express as px
 from helpers import retrieve_pip_installs, retrieve_stars, retrieve_issues
 
-
 LIBRARIES = ["accelerate", "datasets", "diffusers", "evaluate", "gradio", "hub_docs",
              "huggingface_hub", "optimum", "pytorch_image_models", "tokenizers", "transformers"]
 
-
 def create_pip_plot(libraries, pip_choices):
     if "Pip" not in pip_choices:
         return gr.Plot(visible=False)
@@ -18,7 +16,6 @@ def create_pip_plot(libraries, pip_choices):
     plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
     return gr.Plot(value=plot, visible=True)
 
-
 def create_star_plot(libraries, star_choices):
     if "Stars" not in star_choices:
         return gr.Plot(visible=False)
@@ -29,7 +26,6 @@ def create_star_plot(libraries, star_choices):
     plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
     return gr.Plot(value=plot, visible=True)
 
-
 def create_issue_plot(libraries, issue_choices):
     if "Issue" not in issue_choices:
         return gr.Plot(visible=False)
@@ -43,7 +39,6 @@ def create_issue_plot(libraries, issue_choices):
     plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
     return gr.Plot(value=plot, visible=True)
 
-
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
@@ -66,6 +61,5 @@ with gr.Blocks() as demo:
     fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)
     fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()