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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
 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.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}
+{"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", "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", "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

@@ -2,7 +2,6 @@ import random
 import os
 import gradio as gr
 
-
 def fraud_detector(card_activity, categories, sensitivity):
     activity_range = random.randint(0, 100)
     drop_columns = [
@@ -16,7 +15,6 @@ def fraud_detector(card_activity, categories, sensitivity):
         {"fraud": activity_range / 100.0, "not fraud": 1 - activity_range / 100.0},
     )
 
-
 demo = gr.Interface(
     fraud_detector,
     [