Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from matplotlib import pyplot as plt
+
+from sklearn import linear_model, datasets
+
+
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+)
+model_card = f"""
+## Description
+
+**Random sample consensus (RANSAC)** is a method to estimate a mathematical model from a set of observed data that may have some wrong information. 
+The number of times it tries affects how likely it is to get a good answer. **RANSAC** is commonly used in photogrammetry to solve problems with linear or non-linear regression. 
+It works by separating the input data into two groups: inliers (which may have some noise) and outliers (which are wrong data). It estimates the model only using the inliers.
+In this demo, a simulation regression dataset with noise is created, and then compare the results of fitting data in **Linear model** and **RANSAC**.
+You can play around with different ``number of samples`` and ``number of outliers`` to see the effect
+
+## Dataset
+
+Simulation dataset
+"""
+
+
+def do_train(n_samples, n_outliers):
+
+    X, y, coef = datasets.make_regression(
+        n_samples=n_samples,
+        n_features=1,
+        n_informative=1,
+        noise=10,
+        coef=True,
+        random_state=0,
+    )
+
+    # Add outlier data
+    np.random.seed(0)
+    X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1))
+    y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers)
+
+    # Fit line using all data
+    lr = linear_model.LinearRegression()
+    lr.fit(X, y)
+
+    # Robustly fit linear model with RANSAC algorithm
+    ransac = linear_model.RANSACRegressor()
+    ransac.fit(X, y)
+    inlier_mask = ransac.inlier_mask_
+    outlier_mask = np.logical_not(inlier_mask)
+
+    # Predict data of estimated models
+    line_X = np.arange(X.min(), X.max())[:, np.newaxis]
+    line_y = lr.predict(line_X)
+    line_y_ransac = ransac.predict(line_X)
+
+    text = f"True coefficients: {coef:.4f}. Linear regression coefficients: {lr.coef_[0]:.4f}. RANSAC coefficients: {ransac.estimator_.coef_[0]:.4f}"
+
+    fig, axes = plt.subplots()
+
+    axes.scatter(
+        X[inlier_mask], y[inlier_mask], color="yellowgreen", marker=".", label="Inliers"
+    )
+    axes.scatter(
+        X[outlier_mask], y[outlier_mask], color="gold", marker=".", label="Outliers"
+    )
+    axes.plot(line_X, line_y, color="navy", linewidth=2, label="Linear regressor")
+    axes.plot(
+        line_X,
+        line_y_ransac,
+        color="cornflowerblue",
+        linewidth=2,
+        label="RANSAC regressor",
+    )
+    axes.legend(loc="lower right")
+    axes.set_xlabel("Input")
+    axes.set_ylabel("Response")
+
+    return fig, text
+
+
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>Robust linear model estimation using RANSAC</h1>
+            </div>
+        ''')
+    gr.Markdown(model_card)
+    gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py\">scikit-learn</a>")
+    n_samples = gr.Slider(minimum=500, maximum=5000, step=500, value=500, label="Number of samples")
+    n_outliers = gr.Slider(minimum=25, maximum=250, step=25, value=25, label="Number of outliers")
+    with gr.Row():
+        with gr.Column():
+            plot = gr.Plot(label="Compare Linear regressor and RANSAC")
+        with gr.Column():
+            results = gr.Textbox(label="Results")
+
+    n_samples.change(fn=do_train, inputs=[n_samples, n_outliers], outputs=[plot, results])
+    n_outliers.change(fn=do_train, inputs=[n_samples, n_outliers], outputs=[plot, results])
+
+demo.launch()