import streamlit as st
from PIL import Image
from transformers import pipeline
import numpy as np
import cv2
import matplotlib.cm as cm
import time
import base64
from io import BytesIO

st.set_page_config(layout="wide")

with open("styles.css") as f:
    st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True)


st.markdown("<h1 class='title'>Segformer Semantic Segmentation</h1>", unsafe_allow_html=True)
st.markdown("""
<div class='text-center'>
This app uses the Segformer deep learning model to perform semantic segmentation on road images. The Transformer-based model is 
trained on the CityScapes dataset which contains images of urban road scenes. Upload a 
road scene and the app will return the image with semantic segmentation applied.
</div>
""", unsafe_allow_html=True)

group_members = ["Ang Ngo Ching, Josh Darren W.", "Bautista, Ryan Matthew M.", "Lacuesta, Angelo Giuseppe M.", "Reyes, Kenwin Hans", "Ting, Sidney Mitchell O."]


# model_versions = ["b1", "b2", "b3", "b4", "b5"]
# selected_model_version = st.selectbox("Select a model version:", model_versions)


semantic_segmentation = pipeline("image-segmentation", f"nvidia/segformer-b1-finetuned-cityscapes-1024-1024")

new_file_uploaded = False
uploaded_file = st.file_uploader("", type=["jpg", "png"])


def draw_masks_fromDict(image, results):
    masked_image = image.copy()
    
    colormap = cm.get_cmap('nipy_spectral')
    
    for i, result in enumerate(results):
        mask = np.array(result['mask'])  
        mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2)  
        
        color = colormap(i / len(results))[:3] 
        color = tuple(int(c * 255) for c in color)  

        masked_image = np.where(mask, color, masked_image)

    masked_image = masked_image.astype(np.uint8)
    return cv2.addWeighted(image, 0.3, masked_image, 0.7, 0)

col1, col2 = st.columns(2)

if "uploaded_file" not in st.session_state:
    st.session_state.uploaded_file = None

if uploaded_file is not None:
    st.session_state.uploaded_file = uploaded_file

if st.session_state.uploaded_file is not None:
    image = Image.open(st.session_state.uploaded_file)
    col1, col2 = st.columns(2)

    with col1:
        st.image(image, caption='Uploaded Image.', use_column_width=True)


    while True:
        with st.spinner('Processing...'):  
            segmentation_results = semantic_segmentation(image)
            image_with_masks = draw_masks_fromDict(np.array(image), segmentation_results)
            image_with_masks_pil = Image.fromarray(image_with_masks, 'RGB')

        with col2:
            st.image(image_with_masks_pil, caption='Segmented Image.', use_column_width=True)

        buffered = BytesIO()
        image_with_masks_pil.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode()
        href = f'<a href="data:file/png;base64,{img_str}" download="segmented_{st.session_state.uploaded_file.name}">Download Segmented Image</a>'
        st.markdown(href, unsafe_allow_html=True)

        new_file_uploaded = False 

        while not new_file_uploaded: 
            time.sleep(1) 



pdf_url = "https://arxiv.org/pdf/2105.15203.pdf"

st.markdown("""
<h3 class='text-center'>
Read more about the paper below👇
</h5>
""", unsafe_allow_html=True)
st.markdown(f'<iframe class="pdf" src={pdf_url}></iframe>', unsafe_allow_html=True)

st.markdown("Group Members:")
for member in group_members:
    st.markdown("- " + member)