import traceback

from common import common

from extractor import Extractor

from pathlib import Path

import inspect

import time

from numpy import mean, median, quantile

import json

import pandas as pd

SHOW_TOP_CONTEXTS = 10

MAX_PATH_LENGTH = 8

MAX_PATH_WIDTH = 2

JAR_PATH = 'JavaExtractor/JPredict/target/JavaExtractor-0.0.1-SNAPSHOT.jar'

class InteractivePredictor:

exit_keywords = ['exit', 'quit', 'q']

def __init__(self, config, model):

model.predict([])

self.model = model

self.config = config

self.model.prepare()

self.results = {}

self.java = {}

self.path_extractor = Extractor(config,

jar_path=JAR_PATH,

max_path_length=MAX_PATH_LENGTH,

max_path_width=MAX_PATH_WIDTH)

def read_file(self, input_filename):

with open(input_filename, 'r') as file:

return file.readlines()

def predict(self, path_folder):

files_list = list(Path(path_folder).glob('*.java'))

for input_filename in files_list:

file_name_str = str(input_filename)

# input_filename = Path('temp.java')

with open(file_name_str) as f:

code = f.read()

start = time.time()

predict_lines, hash_to_string_dict = self.path_extractor.extract_paths(code, file_name_str)

java_end = time.time()

end = time.time()

p_java = java_end - start

#with open('inspect.txt', 'w') as w:

#w.write(inspect.getsource(self.model))

raw_prediction_results = self.model.predict(predict_lines)

end = time.time()

self.java[file_name_str] = p_java

p = end - start

self.results[file_name_str] = p

print(f'Time passed: {p} for {file_name_str}')

method_prediction_results = common.parse_prediction_results(

raw_prediction_results, hash_to_string_dict,

self.model.vocabs.target_vocab.special_words, topk=SHOW_TOP_CONTEXTS)

for raw_prediction, method_prediction in zip(raw_prediction_results, method_prediction_results):

print('Original name:\t' + method_prediction.original_name)

for name_prob_pair in method_prediction.predictions:

print('\t(%f) predicted: %s' % (name_prob_pair['probability'], name_prob_pair['name']))

total_time_for_one_iteration = sum(self.results.values())

times_arr = list(self.results.values())

print(f'Total time of running {len(files_list)} java methods is '

f'{total_time_for_one_iteration} secs for 1 iteration. \n'

f'Average time for 1 method: {mean(times_arr):0.3f} secs. \n'

f'Min time of 1 method: {min(times_arr):0.3f} secs, \n'

f'max time of 1 method: {max(times_arr):0.3f} secs, \n'

f'median: {median(times_arr):0.3f} secs, \n'

f'quantile 75%: {quantile(times_arr, 0.75):0.3f} secs, \n'

f'quantile 95%: {quantile(times_arr, 0.95):0.3f} secs')

all_res = json.dumps(self.results)

df = pd.DataFrame(columns=['filename', 'total_time', 'preprocess', 'predict_time'])

print(f'All results: {all_res}')

for filename, total_time in self.results.items():

preprocess_time = self.java[filename]

df = df.append({'filename': filename, 'total_time': total_time, 'preprocess': preprocess_time, 'predict_time': total_time - preprocess_time}, ignore_index=True)

df.to_csv('res.csv')