Fix broken spaces in docs (#3698)

* fix examples in sentence_builder

* fix sklearn error in titanic demo

* regenerate notebooks

* changelgo

CHANGELOG.md

@@ -23,6 +23,7 @@
 - Fixed bug in Serializer ValueError text by [@osanseviero](https://github.com/osanseviero) in [PR 3669](https://github.com/gradio-app/gradio/pull/3669)
 - Fix default parameter argument and `gr.Progress` used in same function, by [@space-nuko](https://github.com/space-nuko) in [PR 3671](https://github.com/gradio-app/gradio/pull/3671)
 - Hide `Remove All` button in `gr.Dropdown` single-select mode by [@space-nuko](https://github.com/space-nuko) in [PR 3678](https://github.com/gradio-app/gradio/pull/3678)
+- Fix broken spaces in docs by [@aliabd](https://github.com/aliabd) in [PR 3698](https://github.com/gradio-app/gradio/pull/3698)
 - Fix items in `gr.Dropdown` besides the selected item receiving a checkmark, by [@space-nuko](https://github.com/space-nuko) in [PR 3644](https://github.com/gradio-app/gradio/pull/3644)
 
 ## Documentation Changes:

demo/sentence_builder/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: sentence_builder"]}, {"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", "\n", "def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n", "    return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n", "\n", "\n", "demo = gr.Interface(\n", "    sentence_builder,\n", "    [\n", "        gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose betwen 2 and 20\"),\n", "        gr.Dropdown(\n", "            [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n", "        ),\n", "        gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n", "        gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n", "        gr.Dropdown(\n", "            [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n", "        ),\n", "        gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n", "    ],\n", "    \"text\",\n", "    examples=[\n", "        [2, \"cat\", \"park\", [\"ran\", \"swam\"], True],\n", "        [4, \"dog\", \"zoo\", [\"ate\", \"swam\"], False],\n", "        [10, \"bird\", \"road\", [\"ran\"], False],\n", "        [8, \"cat\", \"zoo\", [\"ate\"], True],\n", "    ],\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: sentence_builder"]}, {"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", "\n", "def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n", "    return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n", "\n", "\n", "demo = gr.Interface(\n", "    sentence_builder,\n", "    [\n", "        gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose betwen 2 and 20\"),\n", "        gr.Dropdown(\n", "            [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n", "        ),\n", "        gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n", "        gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n", "        gr.Dropdown(\n", "            [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n", "        ),\n", "        gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n", "    ],\n", "    \"text\",\n", "    examples=[\n", "        [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n", "        [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n", "        [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n", "        [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n", "    ]\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/sentence_builder/run.py

@@ -21,11 +21,11 @@ demo = gr.Interface(
     ],
     "text",
     examples=[
-        [2, "cat", "park", ["ran", "swam"], True],
-        [4, "dog", "zoo", ["ate", "swam"], False],
-        [10, "bird", "road", ["ran"], False],
-        [8, "cat", "zoo", ["ate"], True],
-    ],
+        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
+        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
+        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
+        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
+    ]
 )
 
 if __name__ == "__main__":

demo/titanic_survival/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: titanic_survival"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scikit-learn numpy pandas"]}, {"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/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/titanic_survival/files/titanic.csv"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import os\n", "\n", "import pandas as pd\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split\n", "\n", "import gradio as gr\n", "\n", "current_dir = os.path.dirname(os.path.realpath(__file__))\n", "data = pd.read_csv(os.path.join(current_dir, \"files/titanic.csv\"))\n", "\n", "\n", "def encode_age(df):\n", "    df.Age = df.Age.fillna(-0.5)\n", "    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)\n", "    categories = pd.cut(df.Age, bins, labels=False)\n", "    df.Age = categories\n", "    return df\n", "\n", "\n", "def encode_fare(df):\n", "    df.Fare = df.Fare.fillna(-0.5)\n", "    bins = (-1, 0, 8, 15, 31, 1000)\n", "    categories = pd.cut(df.Fare, bins, labels=False)\n", "    df.Fare = categories\n", "    return df\n", "\n", "\n", "def encode_df(df):\n", "    df = encode_age(df)\n", "    df = encode_fare(df)\n", "    sex_mapping = {\"male\": 0, \"female\": 1}\n", "    df = df.replace({\"Sex\": sex_mapping})\n", "    embark_mapping = {\"S\": 1, \"C\": 2, \"Q\": 3}\n", "    df = df.replace({\"Embarked\": embark_mapping})\n", "    df.Embarked = df.Embarked.fillna(0)\n", "    df[\"Company\"] = 0\n", "    df.loc[(df[\"SibSp\"] > 0), \"Company\"] = 1\n", "    df.loc[(df[\"Parch\"] > 0), \"Company\"] = 2\n", "    df.loc[(df[\"SibSp\"] > 0) & (df[\"Parch\"] > 0), \"Company\"] = 3\n", "    df = df[\n", "        [\n", "            \"PassengerId\",\n", "            \"Pclass\",\n", "            \"Sex\",\n", "            \"Age\",\n", "            \"Fare\",\n", "            \"Embarked\",\n", "            \"Company\",\n", "            \"Survived\",\n", "        ]\n", "    ]\n", "    return df\n", "\n", "\n", "train = encode_df(data)\n", "\n", "X_all = train.drop([\"Survived\", \"PassengerId\"], axis=1)\n", "y_all = train[\"Survived\"]\n", "\n", "num_test = 0.20\n", "X_train, X_test, y_train, y_test = train_test_split(\n", "    X_all, y_all, test_size=num_test, random_state=23\n", ")\n", "\n", "clf = RandomForestClassifier()\n", "clf.fit(X_train, y_train)\n", "predictions = clf.predict(X_test)\n", "\n", "\n", "def predict_survival(passenger_class, is_male, age, company, fare, embark_point):\n", "    if passenger_class is None or embark_point is None:\n", "        return None\n", "    df = pd.DataFrame.from_dict(\n", "        {\n", "            \"Pclass\": [passenger_class + 1],\n", "            \"Sex\": [0 if is_male else 1],\n", "            \"Age\": [age],\n", "            \"Company\": [\n", "                (1 if \"Sibling\" in company else 0) + (2 if \"Child\" in company else 0)\n", "            ],\n", "            \"Fare\": [fare],\n", "            \"Embarked\": [embark_point + 1],\n", "        }\n", "    )\n", "    df = encode_age(df)\n", "    df = encode_fare(df)\n", "    pred = clf.predict_proba(df)[0]\n", "    return {\"Perishes\": float(pred[0]), \"Survives\": float(pred[1])}\n", "\n", "\n", "demo = gr.Interface(\n", "    predict_survival,\n", "    [\n", "        gr.Dropdown([\"first\", \"second\", \"third\"], type=\"index\"),\n", "        \"checkbox\",\n", "        gr.Slider(0, 80, value=25),\n", "        gr.CheckboxGroup([\"Sibling\", \"Child\"], label=\"Travelling with (select all)\"),\n", "        gr.Number(value=20),\n", "        gr.Radio([\"S\", \"C\", \"Q\"], type=\"index\"),\n", "    ],\n", "    \"label\",\n", "    examples=[\n", "        [\"first\", True, 30, [], 50, \"S\"],\n", "        [\"second\", False, 40, [\"Sibling\", \"Child\"], 10, \"Q\"],\n", "        [\"third\", True, 30, [\"Child\"], 20, \"S\"],\n", "    ],\n", "    interpretation=\"default\",\n", "    live=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: titanic_survival"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scikit-learn numpy pandas"]}, {"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/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/titanic_survival/files/titanic.csv"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import os\n", "\n", "import pandas as pd\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split\n", "\n", "import gradio as gr\n", "\n", "current_dir = os.path.dirname(os.path.realpath(__file__))\n", "data = pd.read_csv(os.path.join(current_dir, \"files/titanic.csv\"))\n", "\n", "\n", "def encode_age(df):\n", "    df.Age = df.Age.fillna(-0.5)\n", "    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)\n", "    categories = pd.cut(df.Age, bins, labels=False)\n", "    df.Age = categories\n", "    return df\n", "\n", "\n", "def encode_fare(df):\n", "    df.Fare = df.Fare.fillna(-0.5)\n", "    bins = (-1, 0, 8, 15, 31, 1000)\n", "    categories = pd.cut(df.Fare, bins, labels=False)\n", "    df.Fare = categories\n", "    return df\n", "\n", "\n", "def encode_df(df):\n", "    df = encode_age(df)\n", "    df = encode_fare(df)\n", "    sex_mapping = {\"male\": 0, \"female\": 1}\n", "    df = df.replace({\"Sex\": sex_mapping})\n", "    embark_mapping = {\"S\": 1, \"C\": 2, \"Q\": 3}\n", "    df = df.replace({\"Embarked\": embark_mapping})\n", "    df.Embarked = df.Embarked.fillna(0)\n", "    df[\"Company\"] = 0\n", "    df.loc[(df[\"SibSp\"] > 0), \"Company\"] = 1\n", "    df.loc[(df[\"Parch\"] > 0), \"Company\"] = 2\n", "    df.loc[(df[\"SibSp\"] > 0) & (df[\"Parch\"] > 0), \"Company\"] = 3\n", "    df = df[\n", "        [\n", "            \"PassengerId\",\n", "            \"Pclass\",\n", "            \"Sex\",\n", "            \"Age\",\n", "            \"Fare\",\n", "            \"Embarked\",\n", "            \"Company\",\n", "            \"Survived\",\n", "        ]\n", "    ]\n", "    return df\n", "\n", "\n", "train = encode_df(data)\n", "\n", "X_all = train.drop([\"Survived\", \"PassengerId\"], axis=1)\n", "y_all = train[\"Survived\"]\n", "\n", "num_test = 0.20\n", "X_train, X_test, y_train, y_test = train_test_split(\n", "    X_all, y_all, test_size=num_test, random_state=23\n", ")\n", "\n", "clf = RandomForestClassifier()\n", "clf.fit(X_train, y_train)\n", "predictions = clf.predict(X_test)\n", "\n", "\n", "def predict_survival(passenger_class, is_male, age, company, fare, embark_point):\n", "    if passenger_class is None or embark_point is None:\n", "        return None\n", "    df = pd.DataFrame.from_dict(\n", "        {\n", "            \"Pclass\": [passenger_class + 1],\n", "            \"Sex\": [0 if is_male else 1],\n", "            \"Age\": [age],\n", "            \"Fare\": [fare],\n", "            \"Embarked\": [embark_point + 1],\n", "            \"Company\": [\n", "                (1 if \"Sibling\" in company else 0) + (2 if \"Child\" in company else 0)\n", "            ]\n", "        }\n", "    )\n", "    df = encode_age(df)\n", "    df = encode_fare(df)\n", "    pred = clf.predict_proba(df)[0]\n", "    return {\"Perishes\": float(pred[0]), \"Survives\": float(pred[1])}\n", "\n", "\n", "demo = gr.Interface(\n", "    predict_survival,\n", "    [\n", "        gr.Dropdown([\"first\", \"second\", \"third\"], type=\"index\"),\n", "        \"checkbox\",\n", "        gr.Slider(0, 80, value=25),\n", "        gr.CheckboxGroup([\"Sibling\", \"Child\"], label=\"Travelling with (select all)\"),\n", "        gr.Number(value=20),\n", "        gr.Radio([\"S\", \"C\", \"Q\"], type=\"index\"),\n", "    ],\n", "    \"label\",\n", "    examples=[\n", "        [\"first\", True, 30, [], 50, \"S\"],\n", "        [\"second\", False, 40, [\"Sibling\", \"Child\"], 10, \"Q\"],\n", "        [\"third\", True, 30, [\"Child\"], 20, \"S\"],\n", "    ],\n", "    interpretation=\"default\",\n", "    live=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/titanic_survival/run.py

@@ -76,11 +76,11 @@ def predict_survival(passenger_class, is_male, age, company, fare, embark_point)
             "Pclass": [passenger_class + 1],
             "Sex": [0 if is_male else 1],
             "Age": [age],
-            "Company": [
-                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
-            ],
             "Fare": [fare],
             "Embarked": [embark_point + 1],
+            "Company": [
+                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
+            ]
         }
     )
     df = encode_age(df)