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README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Plot Guide Filters Events
-emoji: 👀
-colorFrom: pink
-colorTo: gray
+title: plot_guide_filters_events 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 4.40.0
-app_file: app.py
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

data.py

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+import pandas as pd
+import numpy as np
+import random
+from datetime import datetime, timedelta
+
+now = datetime.now()
+
+df = pd.DataFrame({
+    'time': [now - timedelta(minutes=5*i) for i in range(25)],
+    'price': np.random.randint(100, 1000, 25),
+    'origin': [random.choice(["DFW", "DAL", "HOU"]) for _ in range(25)],
+    'destination': [random.choice(["JFK", "LGA", "EWR"]) for _ in range(25)],
+})

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: plot_guide_filters_events"]}, {"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": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/plot_guide_filters_events/data.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from data import df\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        origin = gr.Dropdown([\"All\", \"DFW\", \"DAL\", \"HOU\"], value=\"All\", label=\"Origin\")\n", "        destination = gr.Dropdown([\"All\", \"JFK\", \"LGA\", \"EWR\"], value=\"All\", label=\"Destination\")\n", "        max_price = gr.Slider(0, 1000, value=1000, label=\"Max Price\")\n", "\n", "    plt = gr.ScatterPlot(df, x=\"time\", y=\"price\", inputs=[origin, destination, max_price])\n", "\n", "    @gr.on(inputs=[origin, destination, max_price], outputs=plt)\n", "    def filtered_data(origin, destination, max_price):\n", "        _df = df[df[\"price\"] <= max_price]\n", "        if origin != \"All\":\n", "            _df = _df[_df[\"origin\"] == origin]\n", "        if destination != \"All\":\n", "            _df = _df[_df[\"destination\"] == destination]\n", "        return _df\n", "\n", "    \n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+from data import df
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        origin = gr.Dropdown(["All", "DFW", "DAL", "HOU"], value="All", label="Origin")
+        destination = gr.Dropdown(["All", "JFK", "LGA", "EWR"], value="All", label="Destination")
+        max_price = gr.Slider(0, 1000, value=1000, label="Max Price")
+
+    plt = gr.ScatterPlot(df, x="time", y="price", inputs=[origin, destination, max_price])
+
+    @gr.on(inputs=[origin, destination, max_price], outputs=plt)
+    def filtered_data(origin, destination, max_price):
+        _df = df[df["price"] <= max_price]
+        if origin != "All":
+            _df = _df[_df["origin"] == origin]
+        if destination != "All":
+            _df = _df[_df["destination"] == destination]
+        return _df
+
+    
+if __name__ == "__main__":
+    demo.launch()