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Building_an_Interactive_Iris_Flower_Classification_Tool_Using_Gradio

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Building_an_Interactive_Iris_Flower_Classification_Tool_Using_Gradio / app.py

Create app.py

d0ecead verified about 2 years ago

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import gradio as gr
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

# Load the Iris dataset and train a model
iris = load_iris()
X, y = iris.data, iris.target
clf = RandomForestClassifier()
clf.fit(X, y)

# Define the prediction function
def predict_iris(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]]

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_iris,
    inputs=[
        gr.components.Number(label="Sepal Length (cm)"),
        gr.components.Number(label="Sepal Width (cm)"),
        gr.components.Number(label="Petal Length (cm)"),
        gr.components.Number(label="Petal Width (cm)")
    ],
    outputs="text"
)

iface.launch()

	import gradio as gr
	from sklearn.datasets import load_iris
	from sklearn.ensemble import RandomForestClassifier

	# Load the Iris dataset and train a model
	iris = load_iris()
	X, y = iris.data, iris.target
	clf = RandomForestClassifier()
	clf.fit(X, y)

	# Define the prediction function
	def predict_iris(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]]

	# Create the Gradio interface
	iface = gr.Interface(
	fn=predict_iris,
	inputs=[
	gr.components.Number(label="Sepal Length (cm)"),
	gr.components.Number(label="Sepal Width (cm)"),
	gr.components.Number(label="Petal Length (cm)"),
	gr.components.Number(label="Petal Width (cm)")
	],
	outputs="text"
	)

	iface.launch()