import joblib
import streamlit as st
from prediction import  predict_single_image

knn_model = joblib.load('models/knn_model.joblib')
svm_model = joblib.load('models/svm_model.joblib')
random_forest_model = joblib.load('models/random_forest_model.joblib')

def show_error_popup(message):
    st.error(message, icon="🚨")

st.set_page_config(layout="wide")

st.title('CASIA PALMPRINT DATASET')
st.markdown('By Yash Patel')

st.header('Add Palmprint Image')

uploaded_file = st.file_uploader("Choose an image", type=["jpg", "png", "jpeg"])

st.header("Available Models")
option = st.selectbox(
    "Available Models",
    ("SVM", "KNN","Random Forest"),
)

predicted_label =""
col1, col2= st.columns(2)

if uploaded_file is not None:
    with col1:
        image_data = uploaded_file.read()
        st.image(image_data, caption="Uploaded Image")
    with col2:
        if option=="SVM":
            predicted_label = predict_single_image(svm_model,image_data)
        elif option=="KNN":
            predicted_label = predict_single_image(knn_model, image_data)
        elif option=="Random Forest":
            predicted_label = predict_single_image(random_forest_model, image_data)
        else:
            p = "Other Models are still under training due to overfitting"

        print(predicted_label)

        st.markdown("""
        <style>
        .big-font {
            display: flex;
            align-items:center;
            justify-content: center;
            font-size:50px !important;
            color:green;
            height: 50vh;
        }
        </style>
        """, unsafe_allow_html=True)

        st.markdown(f'<div class="big-font">{predicted_label}</div>', unsafe_allow_html=True)

else:
    show_error_popup("Please Upload Image...")