import os
import pandas as pd
import re

from NaturalLanguageAnalyzer import NaturalLanguageAnalyzer

data_directory = 'data'

file_list = os.listdir(data_directory)
dfs = []
for file_name in file_list:
    file_path = os.path.join(data_directory, file_name)
    df = pd.read_csv(file_path, sep='\t', encoding='utf-8')
    df_user = df[df['role'] == 'user'].drop('role', axis=1)
    dfs.append(df_user)

combined_df = pd.concat(dfs, ignore_index=True)

change_act_format = {
    "thankyou": "thank",
    "bye": "thank",
    "hello": "greet",
    "inform": "inform",
    "request": "request",
    "reqmore": "request"
}

correct = 0
incorrect = 0
for text, ground_act in zip(combined_df["value"].values, combined_df["act"].values):
    nla = NaturalLanguageAnalyzer()
    nla_output = nla.predict(text)
    predicted_act = set([i[0] for i in nla_output])

    pattern = re.compile(r'([^(&]+)(?=\()')
    matches = re.findall(pattern, ground_act)
    ground_act_processed = set()
    for match in matches:
        if match in change_act_format:
            ground_act_processed.add(change_act_format[match])

    for i in ground_act_processed:
        if i in predicted_act:
            correct += 1
        else:
            incorrect += 1

    print("Predicted:", predicted_act)
    print("Ground truth:", ground_act_processed)
    print()

accuracy = correct/(correct+incorrect)
print("Accuracy: ", accuracy)