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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.44.1
+sdk_version: 5.0.0
 app_file: run.py
 pinned: false
 hf_oauth: true

requirements.txt

@@ -1,2 +1,4 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@a15381b23d3f6b59180e83a94a5279feccbf79a2#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/a15381b23d3f6b59180e83a94a5279feccbf79a2/gradio-4.44.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@bbf9ba7e997022960c621f72baa891185bd03732#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/bbf9ba7e997022960c621f72baa891185bd03732/gradio-5.0.0-py3-none-any.whl
+numpy
+pandas

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: plot_guide_line"]}, {"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", "import pandas as pd\n", "import numpy as np\n", "import random\n", "\n", "df = pd.DataFrame({\n", "    'height': np.random.randint(50, 70, 25),\n", "    'weight': np.random.randint(120, 320, 25),\n", "    'age': np.random.randint(18, 65, 25),\n", "    'ethnicity': [random.choice([\"white\", \"black\", \"asian\"]) for _ in range(25)]\n", "})\n", "\n", "with gr.Blocks() as demo:\n", "    gr.LinePlot(df, x=\"weight\", y=\"height\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: plot_guide_line"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy pandas "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import numpy as np\n", "import random\n", "\n", "df = pd.DataFrame({\n", "    'height': np.random.randint(50, 70, 25),\n", "    'weight': np.random.randint(120, 320, 25),\n", "    'age': np.random.randint(18, 65, 25),\n", "    'ethnicity': [random.choice([\"white\", \"black\", \"asian\"]) for _ in range(25)]\n", "})\n", "\n", "with gr.Blocks() as demo:\n", "    gr.LinePlot(df, x=\"weight\", y=\"height\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}