import streamlit as st
import streamlit.components.v1 as components

import dnnlib
import legacy

import pickle
import pandas as pd
import numpy as np
from pyvis.network import Network

import random
from sklearn.metrics.pairwise import cosine_similarity

from matplotlib.backends.backend_agg import RendererAgg

from backend.disentangle_concepts import *

_lock = RendererAgg.lock

HIGHTLIGHT_COLOR = '#e7bcc5'
st.set_page_config(layout='wide')


st.title('Comparison among concept vectors')
st.write('> **How do the concept vectors relate to each other?**')
st.write('> **What is their join impact on the image?**')
st.write("""Description to write""")

    
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 = ['Provenance ADRIA']
if 'space_id' not in st.session_state:
    st.session_state.space_id = 'Z'
if 'type_col' not in st.session_state:
    st.session_state.type_col = 'Provenance'

# 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 = list(ann_df.columns)
        labels.remove('ID')
        labels.remove('Unnamed: 0')
        
        concept_ids = st.multiselect('Concepts:', tuple(labels), default=[labels[2], labels[3]])

        st.write('**Choose a latent space to disentangle**')
        # chosen_text_id_input = st.empty()
        # concept_id = chosen_text_id_input.text_input('Concept:', value=st.session_state.concept_id)
        space_id = st.selectbox('Space:', tuple(['Z', 'W']))

        choose_text_button = st.form_submit_button('Choose the defined concept and space to disentangle')
        
        if choose_text_button:
            st.session_state.concept_ids = list(concept_ids)
            space_id = str(space_id)
            st.session_state.space_id = space_id
            st.session_state.type_col = type_col
        # 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([1,1])
output_col_1, output_col_2 = st.columns([1,1])

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:
    vectors, nodes_in_common, performances = get_concepts_vectors(concept_ids, annotations, ann_df, latent_space=space_id)
    header_col_1.write(f'Concepts {", ".join(concept_ids)} - Latent space {space_id} - Relevant nodes in common: {nodes_in_common} - Performance of the concept vectors: {performances}')# - Nodes {",".join(list(imp_nodes))}')

    edges = []
    for i in range(len(concept_ids)):
        for j in range(len(concept_ids)):
            if i != j:
                print(f'Similarity between {concept_ids[i]} and {concept_ids[j]}')
                similarity = cosine_similarity(vectors[i,:].reshape(1, -1), vectors[j,:].reshape(1, -1))
                print(np.round(similarity[0][0], 3))
                edges.append((concept_ids[i], concept_ids[j], np.round(similarity[0][0], 3)))

    
    net = Network(height="750px", width="100%",)
    for e in edges:
        src = e[0]
        dst = e[1]
        w = e[2]

        net.add_node(src, src, title=src)
        net.add_node(dst, dst, title=dst)
        net.add_edge(src, dst, value=w, title=src + ' to ' + dst + ' similarity ' +str(w))
    
    # Generate network with specific layout settings
    net.repulsion(
                        node_distance=420,
                        central_gravity=0.33,
                        spring_length=110,
                        spring_strength=0.10,
                        damping=0.95
                       )

    # Save and read graph as HTML file (on Streamlit Sharing)
    try:
        path = '/tmp'
        net.save_graph(f'{path}/pyvis_graph.html')
        HtmlFile = open(f'{path}/pyvis_graph.html', 'r', encoding='utf-8')

    # Save and read graph as HTML file (locally)
    except:
        path = '/html_files'
        net.save_graph(f'{path}/pyvis_graph.html')
        HtmlFile = open(f'{path}/pyvis_graph.html', 'r', encoding='utf-8')

    # Load HTML file in HTML component for display on Streamlit page
    components.html(HtmlFile.read(), height=435)

with output_col_2:
    with open('data/CLIP_vecs_vases.pkl', 'rb') as f:
        vectors_CLIP = pickle.load(f)
        
    # st.write(f'Class ID {input_id} - {input_label}: {pred_prob*100:.3f}% confidence')
    #st.write('Concept vector', separation_vector)
    header_col_2.write(f'Concepts {", ".join(concept_ids)} - Latent space CLIP')# - Nodes {",".join(list(imp_nodes))}')

    edges_clip = []
    for c1 in concept_ids:
        for c2 in concept_ids:
            if c1 != c2:
                
                print(f'Similarity between {c1} and {c2}')
                similarity = cosine_similarity(vectors_CLIP[st.session_state.type_col + ' ' + c1].reshape(1, -1), vectors_CLIP[st.session_state.type_col + ' ' + c2].reshape(1, -1))
                print(np.round(similarity[0][0], 3))
                edges_clip.append((c1, c2, np.round(float(np.round(similarity[0][0], 3)), 3)))


    net_clip = Network(height="750px", width="100%",)
    for e in edges_clip:
        src = e[0]
        dst = e[1]
        w = e[2]

        net_clip.add_node(src, src, title=src)
        net_clip.add_node(dst, dst, title=dst)
        net_clip.add_edge(src, dst, value=w, title=src + ' to ' + dst + ' similarity ' +str(w))
    
    # Generate network with specific layout settings
    net_clip.repulsion(
                        node_distance=420,
                        central_gravity=0.33,
                        spring_length=110,
                        spring_strength=0.10,
                        damping=0.95
                       )

    # Save and read graph as HTML file (on Streamlit Sharing)
    try:
        path = '/tmp'
        net_clip.save_graph(f'{path}/pyvis_graph_clip.html')
        HtmlFile = open(f'{path}/pyvis_graph_clip.html', 'r', encoding='utf-8')

    # Save and read graph as HTML file (locally)
    except:
        path = '/html_files'
        net_clip.save_graph(f'{path}/pyvis_graph_clip.html')
        HtmlFile = open(f'{path}/pyvis_graph_clip.html', 'r', encoding='utf-8')

    # Load HTML file in HTML component for display on Streamlit page
    components.html(HtmlFile.read(), height=435)
    
# ----------------------------- 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, 50000)
            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 concepts'):
        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 ------------------------------


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)
# 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 = generate_joint_effect(model, original_image_vec, vectors, min_epsilon=-(int(epsilon)), max_epsilon=int(epsilon), latent_space=st.session_state.space_id)

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)}')