# -*- 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)