Sets matplotlib backend to agg before rendering math

CHANGELOG.md

@@ -6,6 +6,7 @@
 
 ## Bug Fixes:
 
+- Fixes issue with `matplotlib` not rendering correctly if the backend was not set to `Agg` by [@abidlabs](https://github.com/abidlabs) in [PR 4029](https://github.com/gradio-app/gradio/pull/4029)
 - Fixes bug where rendering the same `gr.State` across different Interfaces/Blocks within
 a larger Blocks would not work by [@abidlabs](https://github.com/abidlabs) in [PR 4030](https://github.com/gradio-app/gradio/pull/4030)
 
@@ -19,11 +20,11 @@ No changes to highlight.
 
 ## Breaking Changes:
 
-No changes to highlight.
+- `gr.HuggingFaceDatasetSaver` behavior changed internally. The `flagging/` folder is not a `.git/` folder anymore when using it. `organization` parameter is now ignored in favor of passing a full dataset id as `dataset_name` (e.g. `"username/my-dataset"`).
 
 ## Full Changelog:
 
-No changes to highlight.
+- Safer version of `gr.HuggingFaceDatasetSaver` using HTTP methods instead of git pull/push by [@Wauplin](https://github.com/Wauplin) in [PR 3973](https://github.com/gradio-app/gradio/pull/3973)
 
 ## Contributors Shoutout:
 
@@ -93,15 +94,13 @@ No changes to highlight.
 
 ## Breaking Changes:
 
-- `gr.HuggingFaceDatasetSaver` behavior changed internally. The `flagging/` folder is not a `.git/` folder anymore when using it. `organization` parameter is now ignored in favor of passing a full dataset id as `dataset_name` (e.g. `"username/my-dataset"`).
 - Some re-exports in `gradio.themes` utilities (introduced in 3.24.0) have been eradicated.
   By [@akx](https://github.com/akx) in [PR 3958](https://github.com/gradio-app/gradio/pull/3958)
 
 ## Full Changelog:
 
 - Add DESCRIPTION.md to image_segmentation demo by [@aliabd](https://github.com/aliabd) in [PR 3866](https://github.com/gradio-app/gradio/pull/3866)
 - Fix error in running `gr.themes.builder()` by [@deepkyu](https://github.com/deepkyu) in [PR 3869](https://github.com/gradio-app/gradio/pull/3869)
-- Safer version of `gr.HuggingFaceDatasetSaver` using HTTP methods instead of git pull/push by [@Wauplin](https://github.com/Wauplin) in [PR 3973](https://github.com/gradio-app/gradio/pull/3973)
 - Fixed a JavaScript TypeError when loading custom JS with `_js` and setting `outputs` to `None` in `gradio.Blocks()` by [@DavG25](https://github.com/DavG25) in [PR 3883](https://github.com/gradio-app/gradio/pull/3883)
 - Fixed bg_background_fill theme property to expand to whole background, block_radius to affect form elements as well, and added block_label_shadow theme property  by [@aliabid94](https://github.com/aliabid94) in [PR 3590](https://github.com/gradio-app/gradio/pull/3590)
 

demo/blocks_interpretation/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_interpretation"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio shap matplotlib transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import shap\n", "from transformers import pipeline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "matplotlib.use('Agg')\n", "\n", "\n", "sentiment_classifier = pipeline(\"text-classification\", return_all_scores=True)\n", "\n", "\n", "def classifier(text):\n", "    pred = sentiment_classifier(text)\n", "    return {p[\"label\"]: p[\"score\"] for p in pred[0]}\n", "\n", "\n", "def interpretation_function(text):\n", "    explainer = shap.Explainer(sentiment_classifier)\n", "    shap_values = explainer([text])\n", "    # Dimensions are (batch size, text size, number of classes)\n", "    # Since we care about positive sentiment, use index 1\n", "    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))\n", "\n", "    scores_desc = sorted(scores, key=lambda t: t[1])[::-1]\n", "\n", "    # Filter out empty string added by shap\n", "    scores_desc = [t for t in scores_desc if t[0] != \"\"]\n", "\n", "    fig_m = plt.figure()\n", "    plt.bar(x=[s[0] for s in scores_desc[:5]],\n", "            height=[s[1] for s in scores_desc[:5]])\n", "    plt.title(\"Top words contributing to positive sentiment\")\n", "    plt.ylabel(\"Shap Value\")\n", "    plt.xlabel(\"Word\")\n", "    return {\"original\": text, \"interpretation\": scores}, fig_m\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            input_text = gr.Textbox(label=\"Input Text\")\n", "            with gr.Row():\n", "                classify = gr.Button(\"Classify Sentiment\")\n", "                interpret = gr.Button(\"Interpret\")\n", "        with gr.Column():\n", "            label = gr.Label(label=\"Predicted Sentiment\")\n", "        with gr.Column():\n", "            with gr.Tab(\"Display interpretation with built-in component\"):\n", "                interpretation = gr.components.Interpretation(input_text)\n", "            with gr.Tab(\"Display interpretation with plot\"):\n", "                interpretation_plot = gr.Plot()\n", "\n", "    classify.click(classifier, input_text, label)\n", "    interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_interpretation"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio shap matplotlib transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import shap\n", "from transformers import pipeline\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "sentiment_classifier = pipeline(\"text-classification\", return_all_scores=True)\n", "\n", "\n", "def classifier(text):\n", "    pred = sentiment_classifier(text)\n", "    return {p[\"label\"]: p[\"score\"] for p in pred[0]}\n", "\n", "\n", "def interpretation_function(text):\n", "    explainer = shap.Explainer(sentiment_classifier)\n", "    shap_values = explainer([text])\n", "    # Dimensions are (batch size, text size, number of classes)\n", "    # Since we care about positive sentiment, use index 1\n", "    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))\n", "\n", "    scores_desc = sorted(scores, key=lambda t: t[1])[::-1]\n", "\n", "    # Filter out empty string added by shap\n", "    scores_desc = [t for t in scores_desc if t[0] != \"\"]\n", "\n", "    fig_m = plt.figure()\n", "    plt.bar(x=[s[0] for s in scores_desc[:5]],\n", "            height=[s[1] for s in scores_desc[:5]])\n", "    plt.title(\"Top words contributing to positive sentiment\")\n", "    plt.ylabel(\"Shap Value\")\n", "    plt.xlabel(\"Word\")\n", "    return {\"original\": text, \"interpretation\": scores}, fig_m\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            input_text = gr.Textbox(label=\"Input Text\")\n", "            with gr.Row():\n", "                classify = gr.Button(\"Classify Sentiment\")\n", "                interpret = gr.Button(\"Interpret\")\n", "        with gr.Column():\n", "            label = gr.Label(label=\"Predicted Sentiment\")\n", "        with gr.Column():\n", "            with gr.Tab(\"Display interpretation with built-in component\"):\n", "                interpretation = gr.components.Interpretation(input_text)\n", "            with gr.Tab(\"Display interpretation with plot\"):\n", "                interpretation_plot = gr.Plot()\n", "\n", "    classify.click(classifier, input_text, label)\n", "    interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_interpretation/run.py

@@ -1,9 +1,7 @@
 import gradio as gr
 import shap
 from transformers import pipeline
-import matplotlib
 import matplotlib.pyplot as plt
-matplotlib.use('Agg')
 
 
 sentiment_classifier = pipeline("text-classification", return_all_scores=True)

demo/chicago-bikeshare-dashboard/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import matplotlib\n", "import pandas as pd\n", "\n", "matplotlib.use(\"Agg\")\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", "    f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "        SELECT COUNT(ride_id) as n, rideable_type\n", "        FROM rides\n", "        GROUP BY rideable_type\n", "        ORDER BY n DESC\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "    SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", "    FROM RIDES\n", "    WHERE start_station_name is NOT NULL\n", "    GROUP BY start_station_id\n", "    ORDER BY n DESC\n", "    LIMIT 5\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "    # Chicago Bike Share Dashboard\n", "    \n", "    This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", "    This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", "    is compatible with gradio.\n", "    \n", "    Connection credentials are handled by environment variables\n", "    defined as secrets in the Space.\n", "\n", "    If data were added to the database, the plots in this demo would update\n", "    whenever the webpage is reloaded.\n", "    \n", "    This demo serves as a starting point for your database-connected apps!\n", "    \"\"\"\n", "    )\n", "    with gr.Row():\n", "        bike_type = gr.BarPlot(\n", "            x=\"rideable_type\",\n", "            y='n',\n", "            title=\"Number of rides per bicycle type\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Bicycle Type\",\n", "            vertical=False,\n", "            tooltip=['rideable_type', \"n\"],\n", "            height=300,\n", "            width=300,\n", "        )\n", "        station = gr.BarPlot(\n", "            x='station',\n", "            y='n',\n", "            title=\"Most Popular Stations\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Station Name\",\n", "            vertical=False,\n", "            tooltip=['station', 'n'],\n", "            height=300,\n", "            width=300\n", "        )\n", "\n", "    demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", "    demo.load(get_most_popular_stations, inputs=None, outputs=station)\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: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import pandas as pd\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", "    f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "        SELECT COUNT(ride_id) as n, rideable_type\n", "        FROM rides\n", "        GROUP BY rideable_type\n", "        ORDER BY n DESC\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "    SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", "    FROM RIDES\n", "    WHERE start_station_name is NOT NULL\n", "    GROUP BY start_station_id\n", "    ORDER BY n DESC\n", "    LIMIT 5\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "    # Chicago Bike Share Dashboard\n", "    \n", "    This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", "    This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", "    is compatible with gradio.\n", "    \n", "    Connection credentials are handled by environment variables\n", "    defined as secrets in the Space.\n", "\n", "    If data were added to the database, the plots in this demo would update\n", "    whenever the webpage is reloaded.\n", "    \n", "    This demo serves as a starting point for your database-connected apps!\n", "    \"\"\"\n", "    )\n", "    with gr.Row():\n", "        bike_type = gr.BarPlot(\n", "            x=\"rideable_type\",\n", "            y='n',\n", "            title=\"Number of rides per bicycle type\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Bicycle Type\",\n", "            vertical=False,\n", "            tooltip=['rideable_type', \"n\"],\n", "            height=300,\n", "            width=300,\n", "        )\n", "        station = gr.BarPlot(\n", "            x='station',\n", "            y='n',\n", "            title=\"Most Popular Stations\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Station Name\",\n", "            vertical=False,\n", "            tooltip=['station', 'n'],\n", "            height=300,\n", "            width=300\n", "        )\n", "\n", "    demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", "    demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chicago-bikeshare-dashboard/run.py

@@ -1,10 +1,7 @@
 import os
 import gradio as gr
-import matplotlib
 import pandas as pd
 
-matplotlib.use("Agg")
-
 DB_USER = os.getenv("DB_USER")
 DB_PASSWORD = os.getenv("DB_PASSWORD")
 DB_HOST = os.getenv("DB_HOST")

demo/kitchen_sink_random/constants.py

@@ -1,7 +1,4 @@
 import numpy as np
-import matplotlib
-
-matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import random
 import os