import streamlit as st
import pandas as pd
import numpy as np
import pickle
import base64
import seaborn as sns
import matplotlib.pyplot as plt

st.write("""
# Heart Disease Detection App
""")

url_dataset = f'<a href="heart.csv">Download Dataset CSV File</a>'
st.markdown(url_dataset, unsafe_allow_html=True)

def user_input_features() :
    age  = st.sidebar.slider('age', 29.0, 77.0)
    sex  = st.sidebar.slider('sex', 0.0, 1.0)
    cp   = st.sidebar.slider('cp', 0.0, 3.1)
    trestbps = st.sidebar.slider('trestbps', 94.0, 200.0)
    chol = st.sidebar.slider('chol', 126.0, 564.0)
    fbs = st.sidebar.slider('fbs', 0.0, 1.0)
    restecg = st.sidebar.slider('restec', 0.0, 2.0)
    thalach = st.sidebar.slider('thalach',  71.0, 202.0)
    exang  = st.sidebar.slider('exang', 0.0, 1.0)
    oldpeak  = st.sidebar.slider('oldpeak',  0.0, 6.2)
    slope  = st.sidebar.slider('slope', 0.0, 2.0)
    ca  = st.sidebar.slider('ca', 0.0, 4.0)
    thal = st.sidebar.slider ('thal', 0.0, 3.0)
    
    
    data = {'age':[age],
    'sex':[sex],
    'cp':[cp],
    'trestbps': [trestbps],
    'chol': [chol],
    'fbs': [fbs],
    'restecg': [restecg],
    'thalach':[thalach],
    'exang':[exang],
    'oldpeak': [oldpeak],
    'slope':[slope],
    'ca':[ca],
    'thal':[thal]}
    
    features = pd.DataFrame(data)
    return features

input_df = user_input_features()

heart_raw = pd.read_csv('heart.csv')
heart_raw.fillna(0, inplace=True)
heart = heart_raw.drop(columns=['target'])
df = pd.concat([input_df, heart],axis=0)

df = df[:1] # Selects only the first row (the user input data)
df.fillna(0, inplace=True)

features = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
       'exang', 'oldpeak', 'slope', 'ca', 'thal']

df = df[features]


st.subheader('User Input features')
st.write(df)

load_clf = pickle.load(open('heart.pkl', 'rb'))
detection = load_clf.predict(df)
detection_proba = load_clf.predict_proba(df)
heart_labels = np.array(['Normal', 'Cardiovacular'])
st.subheader('Detection')
#st.write(detection)
st.write(heart_labels[int(detection)])
st.subheader('Detection Probability')
st.write(detection_proba)
#df_prob = pd.DataFrame(data=detection_proba, index=['Probability'], columns=credit_labels)
#st.write(df_prob)