Create app.py

app.py

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