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import pickle
import streamlit as st
from streamlit_option_menu import option_menu
# ================== PAGE CONFIG ==================
st.set_page_config(
page_title="Smart Health AI",
page_icon="🧠",
layout="wide",
)
# ================== LOAD MODELS ==================
def load_model(file):
try:
return pickle.load(open(file, "rb"))
except:
st.error(f"❌ Could not load model: {file}")
return None
diabetes_model = load_model("src/models/diabetes_model.sav")
heart_model = load_model("src/models/heart_model.sav")
parkinson_model = load_model("src/models/parkinsons_model.sav")
# ================== SIDEBAR MENU ==================
with st.sidebar:
st.title("🩺 Smart Health AI")
selected = option_menu(
"Main Menu",
["Diabetes Prediction", "Heart Disease Prediction", "Parkinson's Prediction"],
icons=["activity", "heart", "person"],
default_index=0
)
# ================== INPUT FUNCTIONS ==================
# --------- Diabetes Inputs ---------
def get_diabetes_input():
col1, col2, col3 = st.columns(3)
Pregnancies = col1.number_input('Pregnancies', 0)
Glucose = col2.number_input('Glucose Level', 0.0)
BloodPressure = col3.number_input('Blood Pressure', 0.0)
SkinThickness = col1.number_input('Skin Thickness', 0.0)
Insulin = col2.number_input('Insulin Level', 0.0)
BMI = col3.number_input('BMI', 0.0)
DPF = col1.number_input('Diabetes Pedigree Function', 0.0)
Age = col2.number_input('Age', 0)
return [Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DPF, Age]
# --------- Heart Inputs ---------
def get_heart_input():
cols = st.columns(4)
age = cols[0].number_input("Age", 0)
sex = cols[1].selectbox("Sex", [0,1])
cp = cols[2].selectbox("Chest Pain Type (0–3)", [0,1,2,3])
trestbps = cols[3].number_input("Resting Blood Pressure", 0)
chol = cols[0].number_input("Cholesterol", 0)
fbs = cols[1].selectbox("Fasting Sugar > 120 mg/dl", [0,1])
restecg = cols[2].selectbox("Rest ECG", [0,1,2])
thalach = cols[3].number_input("Max Heart Rate", 0)
exang = cols[0].selectbox("Exercise Angina", [0,1])
oldpeak = cols[1].number_input("ST Depression", 0.0)
slope = cols[2].selectbox("Slope", [0,1,2])
ca = cols[3].selectbox("Number of Major Vessels (0–4)", [0,1,2,3,4])
thal = cols[0].selectbox("Thal", [0,1,2,3])
return [age, sex, cp, trestbps, chol, fbs, restecg,
thalach, exang, oldpeak, slope, ca, thal]
# --------- Parkinson Inputs ---------
def get_parkinson_input():
params = [
"MDVP:Fo(Hz)", "MDVP:Fhi(Hz)", "MDVP:Flo(Hz)", "MDVP:Jitter(%)",
"MDVP:Jitter(Abs)", "MDVP:RAP", "MDVP:PPQ", "Jitter:DDP",
"MDVP:Shimmer", "MDVP:Shimmer(dB)", "Shimmer:APQ3",
"Shimmer:APQ5", "MDVP:APQ", "Shimmer:DDA", "NHR", "HNR",
"RPDE", "DFA", "spread1", "spread2", "D2", "PPE"
]
cols = st.columns(3)
values = []
for i, p in enumerate(params):
values.append(cols[i % 3].number_input(p, value=0.0))
return values
# ================== PREDICT FUNCTION ==================
def predict(model, data):
return model.predict([data])[0]
# ================== MAIN APP ==================
if selected == "Diabetes Prediction":
st.header("🩸 Diabetes Prediction")
data = get_diabetes_input()
if st.button("Predict"):
result = predict(diabetes_model, data)
if result == 1:
st.error("⚠️ The person is diabetic.")
else:
st.success("✅ The person is not diabetic.")
elif selected == "Heart Disease Prediction":
st.header("❤️ Heart Disease Prediction")
data = get_heart_input()
if st.button("Predict Heart Disease"):
result = predict(heart_model, data)
if result == 1:
st.error("⚠️ The patient is at risk of heart disease.")
else:
st.success("✅ The heart is healthy.")
elif selected == "Parkinson's Prediction":
st.header("🧠 Parkinson's Detection")
data = get_parkinson_input()
if st.button("Predict Parkinson's"):
result = predict(parkinson_model, data)
if result == 1:
st.error("⚠️ Parkinson's Detected.")
else:
st.success("✅ No Parkinson's signs detected.")
st.markdown("---")
st.markdown("Developed by Eng Youssef Samy | AI Smart Health") |