import numpy as np
import matplotlib.pyplot as plt
import gradio as gr


def modified_huber_loss(y_true, y_pred):
    z = y_pred * y_true
    loss = -4 * z
    loss[z >= -1] = (1 - z[z >= -1]) ** 2
    loss[z >= 1.0] = 0
    return loss


def plot_loss_func():
  xmin, xmax = -4, 4
  xx = np.linspace(xmin, xmax, 100)
  lw = 2
  plt.clf()

  fig = plt.figure(figsize=(10, 10), dpi=100)
  plt.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], color="gold", lw=lw, label="Zero-one loss")
  plt.plot(xx, np.where(xx < 1, 1 - xx, 0), color="teal", lw=lw, label="Hinge loss")
  plt.plot(xx, -np.minimum(xx, 0), color="yellowgreen", lw=lw, label="Perceptron loss")
  plt.plot(xx, np.log2(1 + np.exp(-xx)), color="cornflowerblue", lw=lw, label="Log loss")
  plt.plot(
      xx,
      np.where(xx < 1, 1 - xx, 0) ** 2,
      color="orange",
      lw=lw,
      label="Squared hinge loss",
  )
  plt.plot(
      xx,
      modified_huber_loss(xx, 1),
      color="darkorchid",
      lw=lw,
      linestyle="--",
      label="Modified Huber loss",
  )
  plt.ylim((0, 8))
  plt.legend(loc="upper right")
  plt.xlabel(r"Decision function $f(x)$")
  plt.ylabel("$L(y=1, f(x))$")
  return fig

title = "SGD convex loss functions"

# def greet(name):
#     return "Hello " + name + "!"
with gr.Blocks(title=title) as demo:
    gr.Markdown(f"# {title}")


    gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py)**")

    btn = gr.Button(value="SGD convex loss functions")
    btn.click(plot_loss_func, outputs= gr.Plot() ) # 

    

demo.launch()