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# -*- coding: utf-8 -*-
"""app.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/10plMWPNgOBAggggGeW01XD195JH5cYlR
"""
import gradio as gr
import csv
import string
import readability
import pandas as pd
import nltk
from nltk.tokenize import word_tokenize
import torch
import gensim
import gensim.downloader as api
from sklearn.metrics.pairwise import cosine_similarity
from nltk.corpus import wordnet as wn
from transformers import DistilBertTokenizer
from nltk.corpus import stopwords
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
from transformers import pipeline
import statistics
import seaborn as sns
nltk.download('cmudict')
nltk.download('stopwords')
nltk.download('punkt')
glove_vectors = api.load('glove-wiki-gigaword-100')
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
device = torch.device('cuda' if torch.cuda.is_available else 'cpu')
#loading model
PATH = '"C:\Users\Robby\Desktop\automaticlit\pytorchBERTmodel"'
model = torch.load(PATH)
model.eval()
model.to(device)
p = pipeline("automatic-speech-recognition")
w2v = dict({})
for idx, key in enumerate(glove_vectors.wv.vocab):
w2v[key] = glove_vectors.wv.get_vector(key)
def calculate_diversity(text):
stop_words = set(stopwords.words('english'))
for i in string.punctuation:
stop_words.add(i)
tokenized_text = word_tokenize(text)
tokenized_text = list(map(lambda word: word.lower(), tokenized_text))
sim_words = {}
if len(tokenized_text) <= 1:
return 1,"More Text Required"
for idx, anc_word in enumerate(tokenized_text):
if anc_word in stop_words:
continue
if idx in sim_words:
sim_words[idx] = sim_words[idx]
continue
vocab = [anc_word]
for pos, comp_word in enumerate(tokenized_text):
try:
if not comp_word in stop_words and cosine_similarity(w2v[anc_word].reshape(1, -1), w2v[comp_word].reshape(1, -1)) > .75:
vocab.append(comp_word)
sim_words[idx] = vocab
except KeyError:
continue
scores = {}
for key, value in sim_words.items():
if len(value) == 1:
scores[key] = 1
continue
t_sim = len(value) - 1
t_rep = (len(value) - 1) - (len(set(value)) )
score = ((t_sim - t_rep)/t_sim)**2
scores[key] = score
mean_score = 0
total = 0
for value in scores.values():
mean_score += value
total += 1
return scores, mean_score/total
def dict_to_list(dictionary, max_size=10):
outer_list = []
inner_list = []
for key, value in dictionary.items():
inner_list.append(value)
if len(inner_list) == max_size:
outer_list.append(inner_list)
inner_list = []
if len(inner_list) > 0:
outer_list.append(inner_list)
return outer_list
def heatmap(scores, df):
total = 0
loops = 0
for ratio in scores.values():
#conditional to visualize the difference between no ratio and a 0 ratio score
if ratio != -.3:
total += ratio
loops += 1
diversity_average = total/loops
return sns.heatmap(df, cmap='gist_gray_r', vmin = -.3).set(title='Word Diversity Score Heatmap (Average Score: ' + str(diversity_average) + ')')
def stats(text):
results = readability.getmeasures(text, lang='en')
return results
def predict(text, tokenizer=tokenizer):
model.eval()
model.to(device)
def prepare_data(text, tokenizer):
input_ids = []
attention_masks = []
encoded_text = tokenizer.encode_plus(
text,
truncation=True,
add_special_tokens = True,
max_length = 315,
pad_to_max_length=True,
return_attention_mask = True,
return_tensors = 'pt'
)
input_ids.append(encoded_text['input_ids'])
attention_masks.append(encoded_text['attention_mask'])
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
return {'input_ids':input_ids, 'attention_masks':attention_masks}
tokenized_example_text = prepare_data(text, tokenizer)
with torch.no_grad():
result = model(
tokenized_example_text['input_ids'].to(device),
attention_mask = tokenized_example_text['attention_masks'].to(device),
return_dict=True
).logits
return result
def reading_difficulty(excerpt):
if len(excerpt) == 0:
return "No Text Provided"
windows = []
words = tokenizer.tokenize(excerpt)
if len(words) > 301:
for idx, text in enumerate(words):
if idx % 300 == 0:
if idx <= len(words) - 301:
x = ' '.join(words[idx: idx+299])
windows.append(x)
win_preds = []
for text in windows:
win_preds.append(predict(text, tokenizer).item())
result = statistics.mean(win_preds)
score = -(result * 1.786 + 6.4) + 10
return score
else:
result = predict(excerpt).item()
score = -(result * 1.786 + 6.4) + 10
return score
def calculate_stats(file_name, data_index):
#unicode escape only for essays
with open(file_name, encoding= 'unicode_escape') as f:
information = {'lines':0, 'words_per_sentence':0, 'words':0, 'syll_per_word':0, 'characters_per_word':0, 'reading_difficulty':0 }
reader = csv.reader(f)
for line in reader:
if len(line[data_index]) < 100:
continue
#if detect(line[data_index][len(line[data_index]) -400: len(line[data_index])-1]) == 'en':
try:
stat = stats(line[data_index])
except ValueError:
continue
information['lines'] += 1
print(information['lines'])
information['words_per_sentence'] += stat['sentence info']['words_per_sentence']
information['words'] += stat['sentence info']['words']
information['syll_per_word'] += stat['sentence info']['syll_per_word']
information['characters_per_word'] += stat['sentence info']['characters_per_word']
information['reading_difficulty'] += reading_difficulty(line[data_index])
for i in information:
if i != 'lines' and i != 'words':
information[i] /= information['lines']
return information
def transcribe(audio):
#speech to text using pipeline
text = p(audio)["text"]
transcription.append(text)
return text
def compute_score(target, actual):
target = target.lower()
actual = actual.lower()
return fuzz.ratio(target,actual)
def phon(text):
alph = nltk.corpus.cmudict.dict()
text = word_tokenize(text)
pronun = []
for word in text:
try:
pronun.append(alph[word][0])
except Exception as e:
pronun.append(word)
return pronun
def gradio_fn(text, audio, target, actual_audio):
if text == None and audio == None and target == None and actual_audio == None:
return "No Inputs", "No Inputs", "No Inputs", "No Inputs"
speech_score = 0
div = calculate_diversity(text)
if actual_audio != None:
actual = p(actual_audio)["text"]
print('sdfgs')
speech_score = compute_score(target, actual)
return "Difficulty Score: " + str(reading_difficulty(actual)), "Transcript: " + str(actual.lower()), "Diversity Score: " + str(div[1]), "Speech Score: " + str(speech_score)
transcription = []
if audio != None:
text = p(audio)["text"]
transcription.append(text)
state = div[0]
return "Difficulty Score: " + str(reading_difficulty(text)), "Transcript: " + str(transcription[-1].lower()), "Diversity Score: " + str(div[1]), "No Inputs"
return "Difficulty Score: " + str(reading_difficulty(text)),"Diversity Score: " + str(div[1]), "No Audio Provided", "No Inputs"
def plot():
text = state
diversity = calculate_diversity(text)[0]
print(diversity)
df = pd.DataFrame(dict_to_list(diversity))
return heatmap(diversity, df)
import csv
example_data = []
x = 0
with open('C:\Users\Robby\Desktop\automaticlit\train.csv') as f:
reader = csv.reader(f)
for line in reader:
example_data.append([line[3]])
x += 1
if x > 100:
break
state = {}
interface = gr.Interface(
fn=gradio_fn,
inputs= [gr.components.Textbox(
label="Text"),
gr.components.Audio(
label="Speech Translation",
source="microphone",
type="filepath"),
gr.components.Textbox(
label="Target Text to Recite"
),
gr.components.Audio(
label="Read Text Above for Score",
source="microphone",
type="filepath")
],
outputs = ["text", "text", "text", "text"],
theme="huggingface",
description="Enter text or speak into your microphone to have your text analyzed!",
rounded=True,
container=True,
examples=example_data,
examples_per_page = 3
).launch(debug=True) |