import streamlit as st
import pickle
import pandas as pd
import numpy as np
import random
import torch

from matplotlib.backends.backend_agg import RendererAgg

from backend.disentangle_concepts import *
import torch_utils
import dnnlib
import legacy

_lock = RendererAgg.lock


st.set_page_config(layout='wide')
BACKGROUND_COLOR = '#bcd0e7'
SECONDARY_COLOR = '#bce7db'


st.title('Disentanglement studies on the Oxford Vases Dataset')
st.markdown(
    """
    This is a demo of the Disentanglement studies on the [Oxford Vases Dataset](https://www.robots.ox.ac.uk/~vgg/data/oxbuildings/).
    """,
    unsafe_allow_html=False,)
    
annotations_file = './data/vase_annotated_files/seeds0000-20000.pkl'
with open(annotations_file, 'rb') as f:
    annotations = pickle.load(f)


if 'image_id' not in st.session_state:
    st.session_state.image_id = 0
if 'concept_ids' not in st.session_state:
    st.session_state.concept_ids =['AMPHORA']
if 'space_id' not in st.session_state:
    st.session_state.space_id = 'W'

# def on_change_random_input():
#     st.session_state.image_id = st.session_state.image_id

# ----------------------------- INPUT ----------------------------------
st.header('Input')
input_col_1, input_col_2, input_col_3 = st.columns(3)
# --------------------------- INPUT column 1 ---------------------------
with input_col_1:
    with st.form('text_form'):
        
        # image_id = st.number_input('Image ID: ', format='%d', step=1)
        st.write('**Choose two options to disentangle**')
        type_col = st.selectbox('Concept category:', tuple(['Provenance', 'Shape Name', 'Fabric', 'Technique']))
        
        ann_df = pd.read_csv(f'./data/vase_annotated_files/sim_{type_col}_seeds0000-20000.csv')
        labels = ann_df.columns

        concept_ids = st.multiselect('Concepts:', tuple(labels), max_selections=2, default=[labels[0], labels[1]])

        st.write('**Choose a latent space to disentangle**')
        space_id = st.selectbox('Space:', tuple(['W', 'Z']))

        choose_text_button = st.form_submit_button('Choose the defined concept and space to disentangle')
          
        if choose_text_button:
            concept_ids = list(concept_ids)
            st.session_state.concept_ids = concept_ids
            space_id = str(space_id)
            st.session_state.space_id = space_id
        # st.write(image_id, st.session_state.image_id)

# ---------------------------- SET UP OUTPUT ------------------------------
epsilon_container = st.empty()
st.header('Output')
st.subheader('Concept vector')

# perform attack container
# header_col_1, header_col_2, header_col_3, header_col_4, header_col_5 = st.columns([1,1,1,1,1])
# output_col_1, output_col_2, output_col_3, output_col_4, output_col_5 = st.columns([1,1,1,1,1])
header_col_1, header_col_2 = st.columns([5,1])
output_col_1, output_col_2 = st.columns([5,1])

st.subheader('Derivations along the concept vector')

# prediction error container
error_container = st.empty()
smoothgrad_header_container = st.empty()

# smoothgrad container
smooth_head_1, smooth_head_2, smooth_head_3, smooth_head_4, smooth_head_5 = st.columns([1,1,1,1,1])
smoothgrad_col_1, smoothgrad_col_2, smoothgrad_col_3, smoothgrad_col_4, smoothgrad_col_5 = st.columns([1,1,1,1,1])

# ---------------------------- DISPLAY COL 1 ROW 1 ------------------------------
with output_col_1:
    separation_vector, number_important_features, imp_nodes, performance = get_separation_space(concept_ids, annotations, ann_df, latent_space=st.session_state.space_id, samples=100)
    # st.write(f'Class ID {input_id} - {input_label}: {pred_prob*100:.3f}% confidence')
    st.write('Concept vector', separation_vector)
    header_col_1.write(f'Concept {st.session_state.concept_ids} - Space {st.session_state.space_id} - Number of relevant nodes: {number_important_features} - Val classification performance: {performance}')# - Nodes {",".join(list(imp_nodes))}')

# ----------------------------- INPUT column 2 & 3 ----------------------------        
with input_col_2:
   with st.form('image_form'):
        
        # image_id = st.number_input('Image ID: ', format='%d', step=1)
        st.write('**Choose or generate a random image to test the disentanglement**')
        chosen_image_id_input = st.empty()
        image_id = chosen_image_id_input.number_input('Image ID:', format='%d', step=1, value=st.session_state.image_id)
        
        choose_image_button = st.form_submit_button('Choose the defined image')
        random_id = st.form_submit_button('Generate a random image')

        if random_id:
            image_id = random.randint(0, 20000)
            st.session_state.image_id = image_id
            chosen_image_id_input.number_input('Image ID:', format='%d', step=1, value=st.session_state.image_id)
            
        if choose_image_button:
            image_id = int(image_id)
            st.session_state.image_id = int(image_id)
        # st.write(image_id, st.session_state.image_id)

with input_col_3:
    with st.form('Variate along the disentangled concept'):
        st.write('**Set range of change**')
        chosen_epsilon_input = st.empty()
        epsilon = chosen_epsilon_input.number_input('Lambda:', min_value=1, step=1)
        epsilon_button = st.form_submit_button('Choose the defined lambda')
        st.write('**Select hierarchical levels to manipulate**')
        layers = st.multiselect('Layers:', tuple(range(14)))
        if len(layers) == 0:
            layers = None
        print(layers)
        layers_button = st.form_submit_button('Choose the defined layers')
        

# ---------------------------- DISPLAY COL 2 ROW 1 ------------------------------

#model = torch.load('./data/model_files/pytorch_model.bin', map_location=torch.device('cpu'))
with dnnlib.util.open_url('./data/vase_model_files/network-snapshot-003800.pkl') as f:
    model = legacy.load_network_pkl(f)['G_ema'].to('cpu') # type: ignore

if st.session_state.space_id == 'Z':
    original_image_vec = annotations['z_vectors'][st.session_state.image_id]
else:
    original_image_vec = annotations['w_vectors'][st.session_state.image_id]

img = generate_original_image(original_image_vec, model, latent_space=st.session_state.space_id)
cols = list(ann_df.columns)
top_pred = ann_df.loc[st.session_state.image_id, cols].astype(float).idxmax()
# input_image = original_image_dict['image']
# input_label = original_image_dict['label']
# input_id = original_image_dict['id']

with smoothgrad_col_3:
    st.image(img)
    smooth_head_3.write(f'Base image, predicted as {top_pred}')


images, lambdas = regenerate_images(model, original_image_vec, separation_vector, min_epsilon=-(int(epsilon)), max_epsilon=int(epsilon), latent_space=st.session_state.space_id, layers=layers)

with smoothgrad_col_1:
    st.image(images[0])
    smooth_head_1.write(f'Change of {np.round(lambdas[0], 2)}')

with smoothgrad_col_2:
    st.image(images[1])
    smooth_head_2.write(f'Change of {np.round(lambdas[1], 2)}')

with smoothgrad_col_4:
    st.image(images[3])
    smooth_head_4.write(f'Change of {np.round(lambdas[3], 2)}')

with smoothgrad_col_5:
    st.image(images[4])
    smooth_head_5.write(f'Change of {np.round(lambdas[4], 2)}')