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