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import gradio as gr
import joblib
import numpy as np
# ✅ Load trained model
model = joblib.load("random_forest_model.pkl")
# ✅ Preprocessing function (basic, assuming inputs are already encoded correctly)
def preprocess(age, gender, bmi, activity, smoking, alcohol, diabetes, hypertension,
cholesterol, family_history, cognitive_score, depression, sleep_quality,
diet, apoe4, social_engagement, stress):
# 🔢 Manual encoding just like training
gender = 1 if gender == "Male" else 0
activity_low = int(activity == "Low")
activity_medium = int(activity == "Medium")
smoking_former = int(smoking == "Former")
smoking_never = int(smoking == "Never")
alcohol_occasional = int(alcohol == "Occasionally")
alcohol_regular = int(alcohol == "Regularly")
cholesterol_normal = int(cholesterol == "Normal")
depression_low = int(depression == "Low")
depression_medium = int(depression == "Medium")
sleep_good = int(sleep_quality == "Good")
sleep_poor = int(sleep_quality == "Poor")
diet_healthy = int(diet == "Healthy")
diet_unhealthy = int(diet == "Unhealthy")
social_low = int(social_engagement == "Low")
social_medium = int(social_engagement == "Medium")
stress_low = int(stress == "Low")
stress_medium = int(stress == "Medium")
features = [
age, bmi, int(diabetes == "Yes"), int(hypertension == "Yes"),
int(family_history == "Yes"), cognitive_score, int(apoe4 == "Yes"),
gender, activity_low, activity_medium,
smoking_former, smoking_never,
alcohol_occasional, alcohol_regular,
cholesterol_normal,
depression_low, depression_medium,
sleep_good, sleep_poor,
diet_healthy, diet_unhealthy,
social_low, social_medium,
stress_low, stress_medium
]
return np.array(features).reshape(1, -1)
# ✅ Prediction function
def predict(*inputs):
features = preprocess(*inputs)
proba = model.predict_proba(features)[0][1]
percent = round(proba * 100, 2)
if proba >= 0.5:
return f"🧠 Likely Alzheimer’s Positive ({percent}%)"
else:
return f"✅ Likely Alzheimer’s Negative ({100 - percent}%)"
demo = gr.Interface(
fn=predict,
title="🧠 Alzheimer's Disease Prediction (Random Forest)",
inputs=[
gr.Slider(40, 100, value=60, label="Age"),
gr.Radio(["Male", "Female"], label="Gender"),
gr.Slider(15, 40, value=25, label="BMI"),
gr.Radio(["Low", "Medium", "High"], label="Physical Activity Level"),
gr.Radio(["Never", "Former", "Current"], label="Smoking Status"),
gr.Radio(["Never", "Occasionally", "Regularly"], label="Alcohol Consumption"),
gr.Radio(["No", "Yes"], label="Diabetes"),
gr.Radio(["No", "Yes"], label="Hypertension"),
gr.Radio(["Normal", "High", "Low"], label="Cholesterol Level"),
gr.Radio(["No", "Yes"], label="Family History of Alzheimer’s"),
gr.Slider(0, 100, value=60, label="Cognitive Test Score"),
gr.Radio(["Low", "Medium", "High"], label="Depression Level"),
gr.Radio(["Poor", "Average", "Good"], label="Sleep Quality"),
gr.Radio(["Healthy", "Average", "Unhealthy"], label="Dietary Habits"),
gr.Radio(["No", "Yes"], label="Genetic Risk Factor (APOE-ε4)"),
gr.Radio(["Low", "Medium", "High"], label="Social Engagement Level"),
gr.Radio(["Low", "Medium", "High"], label="Stress Levels"),
],
outputs=gr.Text(label="Prediction Result"), # ✅ Correct usage
theme="default"
)
if __name__ == "__main__":
demo.launch()
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