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')
st.write('> **What concepts can be disentangled in the latent spae of a model?**')
st.write("""Explanation on the functionalities to come.""")

instruction_text = """Instruction to input:
1. Choosing concept:
2. Choosing image: Users can choose a specific image by entering **Image ID** and hit the _Choose the defined image_ button or can generate an image randomly by hitting the _Generate a random image_ button.
3. Choosing epsilon: **Epsilon** is the lambda amount of translation along the disentangled concept axis. A negative epsilon changes the image in the direction of the concept, a positive one pushes the image away from the concept. 
"""
st.write("To use the functionality below, users need to input the **concept** to disentangle, an **image** id and the **epsilon** of variation along the disentangled axis.")
with st.expander("See more instruction", expanded=False):
    st.write(instruction_text)

    
annotations_file = './data/annotated_files/seeds0000-100000.pkl'
with open(annotations_file, 'rb') as f:
    annotations = pickle.load(f)

ann_df = pd.read_csv('./data/annotated_files/sim_seeds0000-100000.csv')
concepts = './data/concepts.txt'

with open(concepts) as f:
    labels = [line.strip() for line in f.readlines()]

if 'image_id' not in st.session_state:
    st.session_state.image_id = 0
if 'concept_id' not in st.session_state:
    st.session_state.concept_id = 'Abstract'

# 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 a concept to disentangle**')
        # chosen_text_id_input = st.empty()
        # concept_id = chosen_text_id_input.text_input('Concept:', value=st.session_state.concept_id)
        concept_id = st.selectbox('Concept:', tuple(labels))

        choose_text_button = st.form_submit_button('Choose the defined concept')
        # random_text = st.form_submit_button('Select a random concept')

        # if random_text:
        #     concept_id = random.choice(labels)
        #     st.session_state.concept_id = concept_id
        #     chosen_text_id_input.text_input('Concept:', value=st.session_state.concept_id)
            
        if choose_text_button:
            concept_id = str(concept_id)
            st.session_state.concept_id = concept_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_id, annotations, ann_df)
    # 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 {concept_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, 100000)
            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('Epsilon:', min_value=1, step=1)
        epsilon_button = st.form_submit_button('Choose the defined epsilon')

# ---------------------------- 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/model_files/network-snapshot-010600.pkl') as f:
    model = legacy.load_network_pkl(f)['G_ema'].to('cpu') # type: ignore

original_image_vec = annotations['z_vectors'][st.session_state.image_id]
img = generate_original_image(original_image_vec, model)
# 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')


images, lambdas = regenerate_images(model, original_image_vec, separation_vector, min_epsilon=-(int(epsilon)), max_epsilon=int(epsilon))

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)}')