Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn import linear_model
+
+def plot(seed, num_points):
+	# Error handling of non-numeric seeds
+    if seed and not seed.isnumeric():
+      raise gr.Error("Invalid seed")
+	  
+	# Setting the seed
+    if seed:
+      seed = int(seed)
+      np.random.seed(seed)
+    num_points = int(num_points)
+	
+	#Ensuring the number of points is even
+    if num_points%2 != 0:
+      num_points +=1
+    half_num_points = int(num_points/2)
+
+    X = np.r_[np.random.randn(half_num_points, 2) + [1, 1], np.random.randn(half_num_points, 2)]
+    y = [1] * half_num_points + [-1] * half_num_points
+    sample_weight = 100 * np.abs(np.random.randn(num_points))
+    # and assign a bigger weight to the last 10 samples
+    sample_weight[:half_num_points] *= 10
+
+    # plot the weighted data points
+    xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
+    fig, ax = plt.subplots()
+    ax.scatter(
+        X[:, 0],
+        X[:, 1],
+        c=y,
+        s=sample_weight,
+        alpha=0.9,
+        cmap=plt.cm.bone,
+        edgecolor="black",
+    )
+
+    # fit the unweighted model
+    clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)
+    clf.fit(X, y)
+    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    no_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["solid"])
+
+    # fit the weighted model
+    clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)
+    clf.fit(X, y, sample_weight=sample_weight)
+    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    samples_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["dashed"])
+
+    no_weights_handles, _ = no_weights.legend_elements()
+    weights_handles, _ = samples_weights.legend_elements()
+    ax.legend(
+        [no_weights_handles[0], weights_handles[0]],
+        ["no weights", "with weights"],
+        loc="lower left",
+    )
+
+    ax.set(xticks=(), yticks=())
+    return fig
+	
+info = ''' # SGD: Weighted samples\n
+This is a demonstration of a modified version of [SGD](https://scikit-learn.org/stable/modules/sgd.html#id5) that takes into account the weights of the samples. Where the size of points is proportional to its weight.\n
+Created by [@Nahrawy](https://huggingface.co/Nahrawy) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html).
+'''
+
+with gr.Blocks() as demo:
+    gr.Markdown(info)
+    with gr.Row():
+      with gr.Column():
+        seed = gr.Textbox(label="Seed", info="Leave empty to generate new random points each run ",value=None)
+        num_points = gr.Slider(label="Number of Points", value="20", minimum=5, maximum=100, step=2)
+        btn = gr.Button("Run")
+      out = gr.Plot()
+    btn.click(fn=plot, inputs=[seed,num_points] , outputs=out)
+demo.launch()