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# import gradio as gr
#
# def greet(name):
# return "Hello " + name + "!!"
#
# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
# demo.launch()
#
import gradio as gr
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
# Training data
X = np.array([[1, 2], [2, 3], [3, 1], [6, 5], [7, 7], [8, 6]])
y = np.array([0, 0, 0, 1, 1, 1])
# Training the model
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X, y)
# Define the prediction function
def classify_point(x, y):
prediction = model.predict([[x, y]])
return "Class " + str(prediction[0])
# Create a Gradio interface
demo = gr.Interface(
fn=classify_point,
inputs=["number", "number"],
outputs="text",
description="Predict the class of a point based on its coordinates using K-Nearest Neighbors"
)
# Launch the app
demo.launch()
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