#          imports
# ====================================

import numpy as np
import pandas as pd
import seaborn as sns
from random import randint
import matplotlib.pyplot as plt
import streamlit as st
import streamlit.components.v1 as components

#from sklearn.linear_model import LogisticRegression
#from sklearn.svm import SVC
#from sklearn.neighbors import KNeighborsClassifier
#from sklearn.tree import DecisionTreeClassifier
#from sklearn.ensemble import RandomForestClassifier

#from sklearn.model_selection import train_test_split
#from sklearn.model_selection import StratifiedKFold
#from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline
#from imblearn.over_sampling import SMOTE
#from sklearn.model_selection import RandomizedSearchCV
#from sklearn.metrics import classification_report, confusion_matrix, f1_score,accuracy_score, precision_score, recall_score, roc_auc_score

#from sklearn.feature_selection import SelectKBest
#from sklearn.feature_selection import f_classif

#import warnings
#warnings.filterwarnings("ignore")

#       load upper
# ==================================

components.html(
    """
        <a href="https://git.io/typing-svg"><img src="https://readme-typing-svg.herokuapp.com?font=Fira+Code&pause=1000&width=435&lines=Анализ+банкротства+компании" alt="Typing SVG" /></a>
        <a href="https://git.io/typing-svg"><img src="https://readme-typing-svg.herokuapp.com?font=Fira+Code&pause=1000&width=435&lines=методами+искуственного+интеллекта" alt="Typing SVG" /></a>
    """
)

st.markdown("<h1 style='text-align: center;'>Применение методов машинного обучения в анализе банкротства</h1>", unsafe_allow_html=True)

components.html(
    """
        <img src="https://fincult.info/upload/als-property-editorblock/4a2/4a278980ab4958de5e75aa5290842d77.png" align="center">
    """
)

#with open("D:\dev\to_git\test_task_ranhigs\Company_bankruptcy_prediction\for_web\img.png", "rb") as f:
#    st.image(f.read(), use_column_width=True)

with st.expander("ℹ️ - О приложении", expanded=True):

    st.write(
            """
                -   Это приложение — это простой в использовании интерфейс, встроенный в специальную библиотеку Streamlit.
                -   В том числе и сам алгоритм машинного обучения, который можно использовать через форму
            """
        )


st.write(
    """
        # Краткое описание

    """
)

#       cleaning data
# ==================================

data = pd.read_csv("D:\dev\to_git\test_task_ranhigs\Company_bankruptcy_prediction\for_web\dataset.csv")
data.columns = [i.title().strip() for i in list(data.columns)]

row = data.shape[0]
col = data.shape[1]

text = print("The number of rows within the dataset are {} and the number of columns is {}".format(row,col))