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import gradio as gr
import pandas as pd
from pycaret.classification import load_model
# Load the saved model
model = load_model('best_automl_model')
# Prediction function
def predict_anemia(sex, red_pixel, green_pixel, blue_pixel):
# Map input values to a DataFrame
input_data = pd.DataFrame({
'Red Pixel': [red_pixel],
'Green Pixel': [green_pixel],
'Blue Pixel': [blue_pixel],
'Sex': [1 if sex == "Male" else 0] # Encode 'Male' as 1, 'Female' as 0
})
# Make predictions
prediction = model.predict(input_data)[0]
probability = model.predict_proba(input_data)[0][1]
# Convert results
prediction_label = "Yes" if prediction == 1 else "No"
return prediction_label, f"{probability:.2%}" # Probability as a percentage
# Define input components with better labels and layout
inputs = [
gr.Radio(["Male", "Female"], label="Sex", interactive=True),
gr.Slider(0, 100, step=0.1, label="Red Pixel Percentage (%)"),
gr.Slider(0, 100, step=0.1, label="Green Pixel Percentage (%)"),
gr.Slider(0, 100, step=0.1, label="Blue Pixel Percentage (%)"),
]
# Define output components
outputs = [
gr.Textbox(label="Prediction", interactive=False),
gr.Textbox(label="Probability of Anemia (%)", interactive=False),
]
# Create the Gradio Interface
interface = gr.Interface(
fn=predict_anemia,
inputs=inputs,
outputs=outputs,
title="Anemia Prediction App",
description=(
"This app predicts whether a person is anemic based on the percentages of red, "
"green, and blue pixels in their image and their sex. "
"Simply adjust the sliders and select the person's sex to get a prediction."
),
theme="huggingface", # Optional theme
live=False, # Turn off live predictions for better performance
examples=[
["Male", 45.0, 30.0, 25.0],
["Female", 50.0, 28.0, 22.0],
],
)
# Launch the app
interface.launch()
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