Create app.py

app.py

@@ -0,0 +1,61 @@
+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()