Add Gradient Descent Gradio app

app.py

@@ -0,0 +1,69 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+
+def f(x, func_name="Quadratic"):
+    if func_name == "Quadratic":
+        return (x - 2)**2 + 1
+    elif func_name == "Quartic":
+        return x**4 - 3*(x**2) + 2
+
+def grad_f(x, func_name="Quadratic"):
+    if func_name == "Quadratic":
+        return 2*(x - 2)
+    elif func_name == "Quartic":
+        return 4*(x**3) - 6*x
+
+def run_gd(func_name, x0, lr, steps, x_min, x_max):
+    xs = [float(x0)]
+    ys = [float(f(x0, func_name))]
+    x = float(x0)
+
+    for _ in range(int(steps)):
+        g = float(grad_f(x, func_name))
+        x = x - float(lr) * g
+        xs.append(x)
+        ys.append(float(f(x, func_name)))
+
+    grid = np.linspace(float(x_min), float(x_max), 400)
+    vals = f(grid, func_name)
+
+    fig1 = plt.figure()
+    plt.plot(grid, vals)
+    plt.scatter(xs, ys, s=30)
+    plt.plot(xs, ys, linestyle="--")
+    plt.title(f"Gradient Descent Path on {func_name}")
+    plt.xlabel("x")
+    plt.ylabel("f(x)")
+    plt.grid(True)
+
+    fig2 = plt.figure()
+    plt.plot(range(len(ys)), ys)
+    plt.title("Objective Value Over Iterations")
+    plt.xlabel("iteration")
+    plt.ylabel("f(x)")
+    plt.grid(True)
+
+    final = f"Final x = {xs[-1]:.6f}, f(x) = {ys[-1]:.6f}"
+    return fig1, fig2, final
+
+demo = gr.Interface(
+    fn=run_gd,
+    inputs=[
+        gr.Dropdown(["Quadratic", "Quartic"], value="Quadratic", label="Function"),
+        gr.Slider(-10, 10, value=8, step=0.1, label="Initial x0"),
+        gr.Slider(0.001, 1.0, value=0.1, step=0.001, label="Learning rate (lr)"),
+        gr.Slider(1, 200, value=30, step=1, label="Steps"),
+        gr.Slider(-15, 0, value=-5, step=0.5, label="Plot x_min"),
+        gr.Slider(0, 15, value=10, step=0.5, label="Plot x_max"),
+    ],
+    outputs=[
+        gr.Plot(label="Function + GD path"),
+        gr.Plot(label="Loss curve"),
+        gr.Textbox(label="Result"),
+    ],
+    title="Gradient Descent Visualizer (from scratch)",
+    description="Adjust learning rate, starting point, and steps to see how gradient descent moves. Update rule is implemented manually."
+)
+
+demo.launch()