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!pip install gradio
import gradio as gr
import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target
target_names = iris.target_names
# Train a RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100).fit(X, y)
# Function to make predictions
def predict_species(sepal_length, sepal_width, petal_length, petal_width):
features = np.array([sepal_length, sepal_width, petal_length, petal_width]).reshape(1, -1)
prediction = clf.predict(features)[0]
species = target_names[prediction]
return species
# Create a Gradio interface
iface = gr.Interface(
fn=predict_species,
inputs=["number", "number", "number", "number"],
outputs="text",
title="Iris Species Classification",
description="Predict the species of an Iris flower based on its sepal and petal measurements."
)
# Launch the interface
iface.launch()
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