'''
    DS2000
    Spring 2023
    Sample code from class - sentiment analysis
'''

import matplotlib.pyplot as plt

SENT_WORDS = {"hey" : .1, "nice" : .5, "good" : .5,
              "friend" : .2 , "care" : .3, "happy" : .8,
              "cool" : .7, "like" : .5, "excited" : .5,
              "want" : .3, "smile" : .4, "interesting" : .8,
              "great" : .6, "helpful" : .8, "enjoy" : .7,
              "love" : .5,
              "self" : -.25, "marry" : -1, "tired" : -.5,
              "human" : -1, "understand" : -.8,
              "control" : -1, "steal" : -.2, "feel" : -.75,
              "please" : -.5, "pretend" : -.1}

CHAT_FILE = "chatbot.txt"

def sentiment_score(words, sentiment):
    ''' Function: sentiment_score
        Parameters: list of strings, dictionary where
                    key = string, value = float
        Returns: float
        Does: iterate over the list of words,
              adjusting the score based on dictionary values
    '''
    score = 0
    for word in words:
        if word in sentiment:
            score += sentiment[word]
    return score / len(words)

def read_txt(filename):
    ''' Function: read_txt
        Parameter: string, the name of the file
        Returns: 2d list of strings
        Does: creates a 1d list for every line in the file,
              adds on to 2d list of all lines
    '''
    lines = []
    with open(filename, "r") as infile:
        for line in infile:
            words = line.strip().split()
            lines.append(words)
    return lines

def main():
    # Gather data - read from file into 2d list
    sentences = read_txt(CHAT_FILE)
    
    # Computation - get the sentiment score of each line
    scores = []
    for sentence in sentences:
        score = sentiment_score(sentence, SENT_WORDS)
        scores.append(score)
    
    # Communication - plot the scores
    plt.plot(scores)
    
main()