# import streamlit as st
import pandas as pd


def unique_values(rows, col):
    return set([row[col] for row in rows])

def class_counts(rows):
    counts = {}
    for row in rows:
        label = row[-1]
        if label not in counts:
            counts[label] = 0
        counts[label]+=1
    return counts


def is_numeric(value):
    return isinstance(value, int) or isinstance(value, float)


class Question:
    def __init__(self, column, value):
        self.column = column
        self.value = value

    def match(self, example):
        val = example[self.column]
        if is_numeric(val):
            return val >= self.value
        else:
            return val == self.value

    def __repr__(self):
        condition = "=="
        if is_numeric(self.value):
            condition=">="
        return f'Is {header[self.column]} {condition} {str(self.value)} ?'


def partition(rows, question):
    true_rows, false_rows = [], []
    for row in rows:
        if question.match(row):
            true_rows.append(row)
        else:
            false_rows.append(row)
    return true_rows, false_rows

def gini(rows):
    counts = class_counts(rows)
    impurity = 1
    for lbl in counts:
        prob_of_lbl = counts[lbl] / float(len(rows))
        impurity -=prob_of_lbl**2
    return impurity


def info_gain(left, right, current_uncertainty):
    p = float(len(left)) / (len(left) + len(right))
    return current_uncertainty - p * gini(left) - (1 - p) * gini(right)

def find_best_split(rows):
    best_gain = 0
    best_question = None
    current_uncertainty = gini(rows)
    n_features = len(rows[0]) - 1

    for col in range(n_features):
        values = set([row[col] for row in rows])
        for val in values:
            question = Question(col, val)
            true_rows, false_rows = partition(rows, question)
            if len(true_rows) == 0 or len(false_rows) == 0:
                continue
            gain = info_gain(true_rows, false_rows, current_uncertainty)
            if gain>= best_gain:
                best_gain, best_question = gain, question
    return best_gain, best_question

class Leaf:
    def __init__(self, rows):
        self.predictions = class_counts(rows)

class Decision_Node:
    def __init__(self, question, true_branch, false_branch):
        self.question = question
        self.true_branch = true_branch
        self.false_branch = false_branch

def build_tree(rows):
    gain, question = find_best_split(rows)
    if gain == 0:
        return Leaf(rows)

    true_rows, false_rows = partition(rows, question)
    true_branch = build_tree(true_rows)
    false_branch = build_tree(false_rows)

    return Decision_Node(question, true_branch, false_branch)

def print_tree(node, spacing=''):
    if isinstance(node, Leaf):
        print(spacing + 'Predict', node.predictions)
        return
    print(spacing + str(node.question))
    print(spacing + '--> True:')
    print_tree(node.true_branch, spacing + ' ')
    print(spacing + '--> False:')
    print_tree(node.false_branch, spacing + ' ')


def classify(row, node):
    if isinstance(node, Leaf):
        return node.predictions
    if node.question.match(row):
        return classify(row, node.true_branch)
    else:
        return classify(row, node.false_branch)


def print_leaf(counts):
    total = sum(counts.values()) * 1.0
    probs = {}
    for lbl in counts.keys():
        probs[lbl] = str(int(counts[lbl] / total * 100))+'%'
    return probs


def predict(data, header):
    header = header
    tree = build_tree(data)
    results = {}
    for row in data:
        # print(f'Actual: {row[-1]}. Predicted: {print_leaf(classify(row, tree))}')
        results[row[-1]] = print_leaf(classify(row, tree))
    return results, tree

# results, tree = predict(data, header)
# print(results)
# print_tree(tree)