import gradio as gr from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn import svm from sklearn import metrics # Load Iris dataset iris = datasets.load_iris() X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42) # Train a Support Vector Classifier clf = svm.SVC(kernel='linear') clf.fit(X_train, y_train) def iris_classifier(sepal_length, sepal_width, petal_length, petal_width): prediction = clf.predict([[sepal_length, sepal_width, petal_length, petal_width]]) return iris.target_names[prediction[0]] iface = gr.Interface( fn=iris_classifier, inputs=["number", "number", "number", "number"], outputs="text", title="Iris Classifier", description="Enter the measurements of an iris flower to predict its species." ) iface.launch()