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Sets matplotlib backend to agg before rendering math (#4029)
* matplotlib-agg * fix * context manager * Update CHANGELOG.md * update demos * linting * removed warning * fix test * fixes * fix tests
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| {"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} |
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| {"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} |
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