Create dd

dd

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+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()