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()