Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,125 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+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("models/diabetes_model.sav")
+heart_model      = load_model("models/heart_model.sav")
+parkinson_model  = load_model("models/parkinson_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.")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))