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d24ef27
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744492e
Create app.py
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app.py
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import gradio as gr
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.colors import ListedColormap
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.neighbors import NearestCentroid
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from sklearn import datasets
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def classify_iris(sepal_length, sepal_width, shrinkage):
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# Convert input to float values
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sepal_length = float(sepal_length)
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sepal_width = float(sepal_width)
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# Load the Iris dataset
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iris = datasets.load_iris()
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X = iris.data[:, :2]
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y = iris.target
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# Create color maps
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cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"])
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cmap_bold = ListedColormap(["darkorange", "c", "darkblue"])
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# Create an instance of Nearest Centroid Classifier and fit the data
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clf = NearestCentroid(shrink_threshold=shrinkage)
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clf.fit(X, y)
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# Create a new data point based on user input
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new_data_point = np.array([[sepal_length, sepal_width]])
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# Predict the class of the new data point
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prediction = clf.predict(new_data_point)[0]
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# Create a plot to display the decision boundary and data points
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_, ax = plt.subplots()
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DecisionBoundaryDisplay.from_estimator(
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clf, X, cmap=cmap_light, ax=ax, response_method="predict"
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)
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# Plot the new data point
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plt.scatter(new_data_point[:, 0], new_data_point[:, 1], color='black', marker='x', s=100, label="New Data Point")
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# Plot the training points
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20, label="Training Points")
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plt.title("3-Class classification")
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plt.axis("tight")
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plt.legend()
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# Save the plot to a file
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plot_path = "decision_boundary.png"
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plt.savefig(plot_path)
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plt.close()
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return f"Predicted Class: {prediction}", plot_path
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# Create the Gradio interface
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iface = gr.Interface(
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fn=classify_iris,
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inputs=["text", "text", "number"],
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outputs=["text", "image"],
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layout="vertical",
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title="Iris Classification App",
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description="Enter the sepal length and sepal width of a new data point.",
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examples=[
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["5.0", "3.5", 0.2],
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["6.2", "2.8", 0.5]
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]
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)
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# Run the Gradio app
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iface.launch()
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