# -*- coding: utf-8 -*-
"""app.py

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1U0-duve9jKnGXp7YuMutUmUjIuadAj-C
"""

!pip install streamlit
!pip install pyngrok

import streamlit as st
import numpy as np
import pickle

# --- Load models ---
diabetes_model = pickle.load(open("diabetes_model.pkl", "rb"))
heart_model = pickle.load(open("heart_model.pkl", "rb"))
parkinsons_model = pickle.load(open("parkinsons_model.pkl", "rb"))

# --- Set page config ---
st.set_page_config(page_title="🩺 Multi-Disease Predictor", layout="centered")

# --- CSS for styling ---
st.markdown("""
<style>
body {
    background-color: #f0f8ff;
}
.main {
    background: linear-gradient(to right, #cbe8f7, #e0f7fa);
    padding: 2rem;
    border-radius: 20px;
    box-shadow: 0 0 15px rgba(0,0,0,0.1);
    font-family: 'Segoe UI', sans-serif;
}
h1, h3 {
    text-align: center;
    color: #0b5394;
}
.stButton > button {
    background-color: #007acc;
    color: white;
    font-weight: bold;
    padding: 10px 20px;
    border-radius: 8px;
}
.stNumberInput label {
    font-weight: 600;
}
</style>
""", unsafe_allow_html=True)

# --- App header ---
st.markdown("<div class='main'>", unsafe_allow_html=True)
st.markdown("<h1>💙 Multi-Disease Prediction App</h1>", unsafe_allow_html=True)
st.markdown("<p style='text-align:center;'>AI-Powered Health Screening for Diabetes, Heart, and Parkinson's</p>", unsafe_allow_html=True)

# --- Disease selector ---
disease = st.selectbox("🔍 Select a disease to predict", ["Diabetes", "Heart Disease", "Parkinson's"])

# --- Inputs for each disease ---
if disease == "Diabetes":
    st.subheader("🩺 Diabetes Patient Details")
    pregnancies = st.number_input("Pregnancies", min_value=0)
    glucose = st.number_input("Glucose")
    bp = st.number_input("Blood Pressure")
    skin_thickness = st.number_input("Skin Thickness")
    insulin = st.number_input("Insulin")
    bmi = st.number_input("BMI")
    dpf = st.number_input("Diabetes Pedigree Function")
    age = st.number_input("Age", min_value=1)

    if st.button("🔍 Predict Diabetes"):
        input_data = np.array([[pregnancies, glucose, bp, skin_thickness, insulin, bmi, dpf, age]])
        prediction = diabetes_model.predict(input_data)[0]
        if prediction == 1:
            st.success("⚠️ Diabetic")
        else:
            st.success("✅ Not Diabetic")

elif disease == "Heart Disease":
    st.subheader("❤️ Heart Patient Details")
    age = st.number_input("Age", min_value=1)
    sex = st.selectbox("Sex", [1, 0])
    cp = st.number_input("Chest Pain Type")
    trestbps = st.number_input("Resting Blood Pressure")
    chol = st.number_input("Cholesterol")
    fbs = st.selectbox("Fasting Blood Sugar > 120", [1, 0])
    restecg = st.number_input("Rest ECG")
    thalach = st.number_input("Max Heart Rate")
    exang = st.selectbox("Exercise Induced Angina", [1, 0])
    oldpeak = st.number_input("Oldpeak")
    slope = st.number_input("ST Slope")
    ca = st.number_input("Major Vessels")
    thal = st.number_input("Thal")

    if st.button("🔍 Predict Heart Disease"):
        input_data = np.array([[age, sex, cp, trestbps, chol, fbs, restecg,
                                thalach, exang, oldpeak, slope, ca, thal]])
        prediction = heart_model.predict(input_data)[0]
        if prediction == 1:
            st.error("❤️ Heart Disease Detected")
        else:
            st.success("💖 No Heart Disease")

elif disease == "Parkinson's":
    st.subheader("🧠 Parkinson's Patient Details")
    features = [
        "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"
    ]
    values = [st.number_input(f) for f in features]

    if st.button("🔍 Predict Parkinson's"):
        input_data = np.array([values])
        prediction = parkinsons_model.predict(input_data)[0]
        if prediction == 1:
            st.error("🧠 Parkinson's Detected")
        else:
            st.success("😊 No Parkinson's")

st.markdown("</div>", unsafe_allow_html=True)

!pip install streamlit pyngrok

from pyngrok import ngrok
import os
import threading

# 🔑 Set your Ngrok AuthToken here
ngrok.set_auth_token("2zDIrjnvLNcwDKvW0lZsNSyzfzH_2oNc7ao3DYMuoJx2CX5jy")  # <-- REPLACE with your token

# ✅ Run streamlit app in background
def run():
    os.system("streamlit run app.py --server.headless true")

threading.Thread(target=run).start()

# ✅ Use HTTP tunnel explicitly (new format)
public_url = ngrok.connect(addr="http://localhost:8501")
print("🌐 Public URL:", public_url)