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import gradio as gr
import pickle
# Load the decision tree model from the pickle file
with open('best_tree.pkl', 'rb') as file:
model = pickle.load(file)
# Define the predict function
def predict(latitude, longitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income):
# Prepare the input features
features = [[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income]]
# Make predictions using the loaded model
prediction = model.predict(features)
# Return the predicted output
return prediction[0]
# Create the input interface using Gradio
inputs = [
gr.inputs.Number(label="Longitude"),
gr.inputs.Number(label="Latitude"),
gr.inputs.Number(label="Housing Median Age"),
gr.inputs.Number(label="Total Rooms"),
gr.inputs.Number(label="Total Bedrooms"),
gr.inputs.Number(label="Population"),
gr.inputs.Number(label="Households"),
gr.inputs.Number(label="Median Income")
]
# Create the output interface using Gradio
output = gr.outputs.Label(num_top_classes=1)
# Define example data for demonstration
examples = [
[37.88, -122.23, 41, 880, 129, 322, 126, 8.3252],
[37.84, -122.27, 48, 1922, 409, 1026, 335, 1.7969],
[37.83, -122.26, 52, 1656, 420, 718, 382, 2.6768]
]
# Create the Gradio interface
interface = gr.Interface(fn=predict, inputs=inputs, outputs=output, title="Decision Tree Predictor", examples=examples).launch() |