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import gradio as gr
import numpy as np
# Load trained model parameters
theta = np.load("theta_final.npy")
# Define prediction function
def predict_stroke_risk(*symptoms):
symptoms_array = np.array(symptoms).astype(float)
risk = np.dot(symptoms_array, theta) * 100 # Convert to percentage
return round(risk, 2)
# Define UI
symptoms_list = ["Symptom 1", "Symptom 2", "Symptom 3", "Symptom 4", "Symptom 5"]
with gr.Blocks() as demo:
gr.Markdown("# 🏥 Stroke Risk Predictor 🚑")
gr.Markdown("Select symptoms and get your stroke risk percentage.")
checkboxes = [gr.Checkbox(label=s) for s in symptoms_list]
submit = gr.Button("Predict")
output = gr.Textbox(label="Stroke Risk %")
submit.click(predict_stroke_risk, checkboxes, output)
demo.launch()
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