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run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: matrix_transpose"]}, {"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 numpy as np\n", "\n", "import gradio as gr\n", "\n", "def transpose(matrix):\n", "    return matrix.T\n", "\n", "demo = gr.Interface(\n", "    transpose,\n", "    gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n", "    \"numpy\",\n", "    examples=[\n", "        [np.zeros((3, 3)).tolist()],\n", "        [np.ones((2, 2)).tolist()],\n", "        [np.random.randint(0, 10, (3, 10)).tolist()],\n", "        [np.random.randint(0, 10, (10, 3)).tolist()],\n", "        [np.random.randint(0, 10, (10, 10)).tolist()],\n", "    ],\n", "    cache_examples=False\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+from datasets import load_dataset
+import pandas as pd
+import gradio as gr
+
+# Load the Pokémon Cards dataset from Hugging Face
+dataset = load_dataset("TheFusion21/PokemonCards")
+df = pd.DataFrame(dataset["train"])
+
+display_columns = ["name", "hp"] + [col for col in df.columns if col not in ["image_url", "name", "hp"]]
+display_df = df[display_columns]
+
+def style_df(df):
+    hp_min = df['hp'].min()
+    hp_max = df['hp'].max()
+
+    def color_hp(val):
+        norm_val = (val - hp_min) / (hp_max - hp_min)
+        r = int(255 * (1 - norm_val))
+        g = int(255 * norm_val)
+        return f'background-color: rgba({r}, {g}, 0, 0.2)'
+
+    styled = df.style.applymap(color_hp, subset=['hp'])
+    return styled
+
+# Function to filter data based on user input
+def filter_cards(set_name=None):
+    filtered_df = df.copy()
+    if set_name and set_name != "All":
+        filtered_df = filtered_df[filtered_df["set_name"] == set_name]
+    return style_df(filtered_df[display_columns])
+
+def update_display(evt: gr.SelectData):
+    selected_data = df.iloc[evt.index[0]]
+    return selected_data["image_url"]
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Pokémon Cards Explorer")
+
+    with gr.Row():
+        set_filter = gr.Dropdown(
+            choices=["All"] + df["set_name"].unique().tolist(),
+            label="Filter by Set Name"
+        )
+
+    filtered_table = gr.DataFrame(style_df(display_df), show_fullscreen_button=True, show_search="search", max_chars=20, pinned_columns=1)
+    card_image = gr.Image(label="Card Image", height=200)
+
+    filter_button = gr.Button("Apply Filters")
+    filter_button.click(
+        filter_cards,
+        inputs=[set_filter],
+        outputs=filtered_table
+    )
+
+    filtered_table.select(
+        update_display,
+        None,
+        card_image
+    )
+
+demo.launch()