import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def read_glove_vecs(glove_file):
    with open(glove_file, 'r',encoding='utf-8') as f:
        words = set()
        word_to_vec_map = {}
        for line in f:
            line = line.strip().split()
            curr_word = line[0]
            words.add(curr_word)
            word_to_vec_map[curr_word] = np.array(line[1:], dtype=np.float64)
        
        i = 4
        words_to_index = {}
        index_to_words = {}
        for w in sorted(words):
            words_to_index[w] = i
            index_to_words[i] = w
            i = i + 1
    return words_to_index, index_to_words, word_to_vec_map

def softmax(x):
    """Compute softmax values for each sets of scores in x."""
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()




def convert_to_one_hot(Y, C):
    Y = np.eye(C)[Y.reshape(-1)]
    return Y




 
def predict(X, Y, W, b, word_to_vec_map):
    """
    Given X (sentences) and Y (emoji indices), predict emojis and compute the accuracy of your model over the given set.
    
    Arguments:
    X -- input data containing sentences, numpy array of shape (m, None)
    Y -- labels, containing index of the label emoji, numpy array of shape (m, 1)
    
    Returns:
    pred -- numpy array of shape (m, 1) with your predictions
    """
    m = X.shape[0]
    pred = np.zeros((m, 1))
    
    for j in range(m):                       # Loop over training examples
        
        # Split jth test example (sentence) into list of lower case words
        words = X[j].lower().split()
        
        # Average words' vectors
        avg = np.zeros((50,))
        for w in words:
            avg += word_to_vec_map[w]
        avg = avg/len(words)

        # Forward propagation
        Z = np.dot(W, avg) + b
        A = softmax(Z)
        pred[j] = np.argmax(A)
        
    print("Accuracy: "  + str(np.mean((pred[:] == Y.reshape(Y.shape[0],1)[:]))))
    
    return pred