Create app.py

app.py

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+import numpy as np
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import gradio as gr
+from sklearn import datasets
+from sklearn import linear_model
+from sklearn.svm import l1_min_c
+
+
+def train_it(solver, intersect_scaling, tol, max_iter):
+    iris = datasets.load_iris()
+    X = iris.data
+    y = iris.target
+
+    X = X[y != 2]
+    y = y[y != 2]
+
+    X /= X.max()
+
+    cs = l1_min_c(X, y, loss="log") * np.logspace(0, 7, 16)
+
+    clf = linear_model.LogisticRegression(
+        penalty="l1",
+        solver=solver,
+        tol=tol,
+        max_iter=int(max_iter),
+        warm_start=True,
+        intercept_scaling=intersect_scaling,
+    )
+
+    coefs_ = []
+    for c in cs:
+        clf.set_params(C=c)
+        clf.fit(X, y)
+        coefs_.append(clf.coef_.ravel().copy())
+
+    coefs_ = np.array(coefs_)
+
+    plt.plot(np.log10(cs), coefs_, marker="o")
+    ymin, ymax = plt.ylim()
+    plt.xlabel("log(C)")
+    plt.ylabel("Coefficients")
+    plt.title("Logistic Regression Path")
+    plt.axis("tight")
+    plt.show()
+
+    return plt
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Regularization path of L1- Logistic Regression")
+    gr.Markdown(
+        """
+    This interactive demo is based on the [Regularization path of L1- Logistic Regression] (https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_path.html).This demonstrates how to perform l1-penalized logistic regression on a binary classification problem derived from the Iris dataset. The regularization path plots the progression of the coefficients from exactly 0 to non-zero values as the regularization becomes progressively looser.
+                """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            solver = gr.Dropdown(
+                ["liblinear", "saga"], label="Solver", value="liblinear"
+            )
+            warm_start = gr.Dropdown(
+                ["True", "False"], label="Warm Start", value="True"
+            )
+        with gr.Column(align="center"):
+            intersect_scaling = gr.Slider(
+                value=10000.0,
+                minimum=0,
+                maximum=100000,
+                step=0.1,
+                label="Intersect Scaling",
+            )
+            tol = gr.Slider(
+                value=1e-6, minimum=0, maximum=1, step=0.1, label="Tolerance"
+            )
+            max_iter = gr.Slider(
+                value=1e6,
+                minimum=0,
+                maximum=1000000,
+                step=0.1,
+                label="Maximum Iterations",
+            )
+    train_buttion = gr.Button(label="Train")
+
+    train_buttion.click(
+        train_it, inputs=[solver, intersect_scaling, tol, max_iter], outputs=gr.Plot()
+    )
+
+
+demo.launch()