from tensorflow.keras.models import load_model
import streamlit as st
import requests
from streamlit_lottie import st_lottie
import cv2
import numpy as np
from PIL import Image
# Load the ensemble model using tf.keras.models.load_model()
loaded_ensemble_model = load_model('ensemble_model.h5')



def load_lottieurl(url):
    r = requests.get(url)
    if r.status_code != 200:
        return None
    return r.json()


# Use local CSS
def local_css(file_name):
    with open(file_name) as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)


local_css("style/styles.css")

# ---- LOAD ASSETS ----
lottie_coding = load_lottieurl("https://lottie.host/6b8742e7-e4f5-4403-8bc0-1d5c2fae6e55/ah80z8X1Hu.json")
img_contact_form = Image.open("images/yt_contact_form.png")
img_lottie_animation = Image.open("images/yt_lottie_animation.png")

# ---- HEADER SECTION ----
with st.container():
    st.subheader("Hi, Good Morning! :wave:")
    st.title("Welcome To Alzheimer Disease Detection Web App")
    st.write(
        "Alzheimer's disease is a progressive brain disorder that causes memory loss, cognitive decline, and changes in behavior."
    )
    st.write("[Learn More >](https://en.wikipedia.org/wiki/Alzheimer%27s_disease)")

# ---- FOUR TYPES OF CLASSES ----
with st.container():
    st.write("---")
    left_column, right_column = st.columns(2)
    with left_column:
        st.header("Description:")
        st.write("##")
        st.write(
            """
            We have devided the alzheimers disease into four stages namely:
            - Non-Demented: Individuals with no signs of dementia.
            - Mild Demented: Individuals with mild cognitive impairment.
            - Moderate Demented: Individuals with moderate cognitive impairment.
            - Very Mild Demented: Individuals with very mild cognitive impairment."
            We are aiming at providing the early detection of the disease using this web application, which will help the early diagnosis.
            """
        )
        # st.write("[aisa likh jo link ko specify kre](kuch bhi link daal de)")
    with right_column:
        st_lottie(lottie_coding, height=300, key="coding")

# --- INPUT-OUTPUT ---
st.header('Insert image for classification:')

upload = st.file_uploader('', type=['png', 'jpg'])


if upload is not None:
    im = Image.open(upload)
    im = im.convert('RGB')
    img = np.asarray(im)
    image = cv2.resize(img, (150, 150))
    img_array = image.reshape(1, 150, 150, 3)

    # Resize the image to 176x208 pixels
    resized_im = im.resize((176, 208))

    st.header('Input Image')

    # Display the resized image
    st.image(resized_im, use_column_width=False, width=176, caption='Uploaded Image')

    loaded_ensemble_model = load_model('ensemble_model.h5')
    pred = loaded_ensemble_model.predict([img_array, img_array, img_array])
    labels = {0: 'MildDemented', 1: 'ModerateDemented', 2: 'NonDemented', 3: 'VeryMildDemented'}

    st.header('Output')
    col1, col2 = st.columns(2)
    with col1:
        st.subheader('Predicted class:')
        st.write(labels[pred.argmax()])
    with col2:
        st.subheader('With:')
        st.write(f'{int(pred.max() * 100)}% assurity')

        
# ---- CONTACT ----
with st.container():
    st.write("---")
    st.header("For any queries or suggestions!")
    st.write("##")

    # Documention: https://formsubmit.co/ !!! CHANGE EMAIL ADDRESS !!!
    contact_form = """
    <form action="https://formsubmit.co/sainimohit572000@gmail.com" method="POST">
        <input type="hidden" name="_captcha" value="false">
        <input type="text" name="name" placeholder="Your name" required>
        <input type="email" name="email" placeholder="Your email" required>
        <textarea name="message" placeholder="Your message here" required></textarea>
        <button type="submit">Send</button>
    </form>
    """
    left_column, right_column = st.columns(2)
    with left_column:
        st.markdown(contact_form, unsafe_allow_html=True)
    with right_column:
        st.empty()