import pandas as pd
import numpy as np
import streamlit as st
import easyocr
import PIL
from PIL import Image, ImageDraw

from utils import construct_possible_nickname, text_dict


file = None

col1, col2, col3 = st.columns(3)

with col1:
   st.image('ubahn.png', width=100)

with col2:
    st.title("WIENER UBAHN / VIENNA SUBWAY - GEOCACHE")

with col3:
   st.image('gc_logo_grey.png', width=100)


language = st.selectbox(
    'Sprache / Language:',
    ('Deutsch', 'English'))


# subtitle
st.markdown(f"#### {text_dict['step1'][language]}")
nickname = st.text_input("Nickname", value="")

if nickname is not None and nickname != "":
    with st.spinner(f"{text_dict['wait'][language]}"):
        stations, possible_nickname = construct_possible_nickname(nickname)
        df_list = []

        for key in stations:
            df_list.append([key, ", ".join(stations[key])])

        possible_stations_table = pd.DataFrame(df_list, columns=[text_dict['letter'][language], text_dict['stations'][language]])

    st.divider()

    st.markdown(f"#### {text_dict['step2'][language]}")
    st.markdown(f"<h3 style='text-align: center;'>{possible_nickname}</3>", 
                unsafe_allow_html=True)

    with st.expander(f"{text_dict['explanation'][language]}"):
        st.table(possible_stations_table)

    st.divider()

    # subtitle
    st.markdown(f"#### {text_dict['step3'][language]}")

    # upload image file
    file = st.file_uploader(label = "Upload", type=['png', 'jpg', 'jpeg'])

#read the csv file and display the dataframe
if file is not None:
    
    with st.spinner(f"{text_dict['wait'][language]}"):
        image = Image.open(file) # read image with PIL library
        st.image(image) #display
    
    with st.spinner(f"{text_dict['wait'][language]}"):
        # it will only detect the English and Turkish part of the image as text
        reader = easyocr.Reader(['en','de'], gpu=False) 
        result = reader.readtext(np.array(image))  # turn image to numpy array

    with st.spinner(f"{text_dict['wait'][language]}"):
        # collect the results in the dictionary:
        textdic_easyocr = {}
        for idx in range(len(result)):
            pred_coor = result[idx][0]
            pred_text = result[idx][1]
            pred_confidence = result[idx][2]
            textdic_easyocr[pred_text] = {}
            textdic_easyocr[pred_text]['Text confidence'] = pred_confidence

    # create a data frame which shows the predicted text and prediction confidence
    df = pd.DataFrame.from_dict(textdic_easyocr).T
    st.markdown(f"### {text_dict['detect'][language]}")
    st.table(df)

if nickname is not None and nickname != "" and file is not None:
    
    detected_text = [key.lower() for key in textdic_easyocr.keys()]

    if possible_nickname.lower() in detected_text:
        st.markdown(f"<h3 style='text-align: center; color: green'>{text_dict['correct'][language]}</h3>",
                    unsafe_allow_html=True)
        
    else:
        st.markdown(f"<h3 style='text-align: center; color: red'>{text_dict['wrong'][language]}</h3>", 
                    unsafe_allow_html=True)