Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+import gradio as gr
+import tempfile
+import os
+
+# ----------------- GLOBAL VARIABLES -------------------
+X, y = None, None
+X_train, X_test, y_train, y_test = None, None, None, None
+
+
+def split_dataset(test_ratio):
+    global X, y, X_train, X_test, y_train, y_test
+
+    X, y = datasets.make_blobs(
+        n_samples=300,
+        centers=3,
+        cluster_std=2.0,
+        random_state=None
+    )
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=test_ratio, random_state=None
+    )
+
+    return f"Dataset split successfully!\nTrain size: {len(X_train)}\nTest size: {len(X_test)}"
+
+
+def visualize_knn(n_neighbors):
+    global X_train, X_test, y_train, y_test
+
+    if X_train is None:
+        return None, "⚠ Please click 'Split Dataset' first!"
+
+    n_neighbors = int(n_neighbors)
+    model = KNeighborsClassifier(n_neighbors=n_neighbors)
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    acc = accuracy_score(y_test, y_pred)
+
+    x_min, x_max = min(X_train[:, 0].min(), X_test[:, 0].min()) - 1, max(X_train[:, 0].max(), X_test[:, 0].max()) + 1
+    y_min, y_max = min(X_train[:, 1].min(), X_test[:, 1].min()) - 1, max(X_train[:, 1].max(), X_test[:, 1].max()) + 1
+
+    xx, yy = np.meshgrid(
+        np.linspace(x_min, x_max, 300),
+        np.linspace(y_min, y_max, 300)
+    )
+
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    plt.figure(figsize=(7, 7))
+    plt.contourf(xx, yy, Z, alpha=0.4, cmap="Accent")
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap="Accent", edgecolors="black", marker="o")
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap="Accent", edgecolors="black", marker="^")
+    plt.title(f"KNN Decision Boundary (k = {n_neighbors})")
+
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    plt.savefig(temp_file.name)
+    plt.close()
+
+    return temp_file.name, f"Accuracy: {acc:.4f}"
+
+
+custom_css = """
+.gr-button {
+    background-color: #007bff !important;
+    color: white !important;
+    border-radius: 8px !important;
+    padding: 12px 20px !important;
+    font-weight: bold !important;
+}
+.gr-slider input {
+    accent-color: #007bff !important;
+}
+body, .gradio-container {
+    background: #1f1f1f !important;
+    color: white !important;
+}
+.gr-box, .gr-textbox, .gr-markdown {
+    color: white !important;
+}
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+
+    gr.Markdown("## 🧠 KNN Decision Boundary + Dynamic Train/Test Split Visualizer")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            split_ratio = gr.Slider(0.1, 0.5, value=0.3, step=0.05, label="Test Size Ratio")
+            split_btn = gr.Button("Split Dataset")
+            split_output = gr.Textbox(label="Split Result", interactive=False)
+
+            k_slider = gr.Slider(1, 20, value=3, step=1, label="K Value (n_neighbors)")
+            visualize_btn = gr.Button("Visualize")
+
+        with gr.Column(scale=2):
+            output_img = gr.Image()
+            accuracy_text = gr.Textbox(label="Model Accuracy", interactive=False)
+
+    split_btn.click(split_dataset, inputs=[split_ratio], outputs=[split_output])
+    visualize_btn.click(visualize_knn, inputs=[k_slider], outputs=[output_img, accuracy_text])
+
+
+demo.launch(server_name="0.0.0.0", server_port=7860)