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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.50.2
+sdk_version: 4.0.2
 app_file: run.py
 pinned: false
 hf_oauth: true

requirements.txt

@@ -1,2 +1,2 @@
-https://gradio-builds.s3.amazonaws.com/5524e590577769b0444a5332b8d444aafb0c5c12/gradio-3.50.2-py3-none-any.whl
+https://gradio-builds.s3.amazonaws.com/874005938d65543c4cefe610a17e58d2ec7b3fb1/gradio-4.0.2-py3-none-any.whl
 pandas

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fraud_detector"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pandas"]}, {"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/fraud_detector/fraud.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import random\n", "import os\n", "import gradio as gr\n", "\n", "\n", "def fraud_detector(card_activity, categories, sensitivity):\n", "    activity_range = random.randint(0, 100)\n", "    drop_columns = [\n", "        column for column in [\"retail\", \"food\", \"other\"] if column not in categories\n", "    ]\n", "    if len(drop_columns):\n", "        card_activity.drop(columns=drop_columns, inplace=True)\n", "    return (\n", "        card_activity,\n", "        card_activity,\n", "        {\"fraud\": activity_range / 100.0, \"not fraud\": 1 - activity_range / 100.0},\n", "    )\n", "\n", "\n", "demo = gr.Interface(\n", "    fraud_detector,\n", "    [\n", "        gr.Timeseries(x=\"time\", y=[\"retail\", \"food\", \"other\"]),\n", "        gr.CheckboxGroup(\n", "            [\"retail\", \"food\", \"other\"], value=[\"retail\", \"food\", \"other\"]\n", "        ),\n", "        gr.Slider(1, 3),\n", "    ],\n", "    [\n", "        \"dataframe\",\n", "        gr.Timeseries(x=\"time\", y=[\"retail\", \"food\", \"other\"]),\n", "        gr.Label(label=\"Fraud Level\"),\n", "    ],\n", "    examples=[\n", "        [os.path.join(os.path.abspath(''), \"fraud.csv\"), [\"retail\", \"food\", \"other\"], 1.0],\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: fraud_detector"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pandas"]}, {"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/fraud_detector/fraud.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import random\n", "import os\n", "import gradio as gr\n", "\n", "\n", "def fraud_detector(card_activity, categories, sensitivity):\n", "    activity_range = random.randint(0, 100)\n", "    drop_columns = [\n", "        column for column in [\"retail\", \"food\", \"other\"] if column not in categories\n", "    ]\n", "    if len(drop_columns):\n", "        card_activity.drop(columns=drop_columns, inplace=True)\n", "    return (\n", "        card_activity,\n", "        card_activity,\n", "        {\"fraud\": activity_range / 100.0, \"not fraud\": 1 - activity_range / 100.0},\n", "    )\n", "\n", "\n", "demo = gr.Interface(\n", "    fraud_detector,\n", "    [\n", "        gr.CheckboxGroup(\n", "            [\"retail\", \"food\", \"other\"], value=[\"retail\", \"food\", \"other\"]\n", "        ),\n", "        gr.Slider(1, 3),\n", "    ],\n", "    [\n", "        \"dataframe\",\n", "        gr.Label(label=\"Fraud Level\"),\n", "    ],\n", "    examples=[\n", "        [os.path.join(os.path.abspath(''), \"fraud.csv\"), [\"retail\", \"food\", \"other\"], 1.0],\n", "    ],\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -20,7 +20,6 @@ def fraud_detector(card_activity, categories, sensitivity):
 demo = gr.Interface(
     fraud_detector,
     [
-        gr.Timeseries(x="time", y=["retail", "food", "other"]),
         gr.CheckboxGroup(
             ["retail", "food", "other"], value=["retail", "food", "other"]
         ),
@@ -28,7 +27,6 @@ demo = gr.Interface(
     ],
     [
         "dataframe",
-        gr.Timeseries(x="time", y=["retail", "food", "other"]),
         gr.Label(label="Fraud Level"),
     ],
     examples=[