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| import pandas as pd | |
| import numpy as np | |
| import json | |
| import gradio as gr | |
| from keras.models import load_model | |
| from keras.preprocessing.text import tokenizer_from_json, Tokenizer | |
| from keras.preprocessing.sequence import pad_sequences | |
| import spacy | |
| from string import punctuation | |
| import re | |
| nlp = spacy.load('en_core_web_sm') | |
| stopwords = nlp.Defaults.stop_words | |
| def clean_text(text): | |
| text = text.translate(punctuation) | |
| text = re.sub(r"[^\w\s]", " ",text) | |
| text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ",text) | |
| doc = nlp(text) | |
| text = [token for token in doc if str(token) not in stopwords] | |
| lemmatized = [token.lemma_ for token in text] | |
| text = ' '.join(lemmatized) | |
| return text | |
| with open("tokenizer.json", "r") as read_file: | |
| tokenizer = json.load(read_file) | |
| tokenizer = tokenizer_from_json(tokenizer) | |
| model = load_model('tweets_disaster_model.h5') | |
| def tweets_predictions(text): | |
| text = clean_text(text) | |
| text = tokenizer.texts_to_sequences([text]) | |
| text = pad_sequences(text, padding='post', maxlen=50) | |
| pred = model.predict(text.reshape(1,-1)).tolist()[0] | |
| dic = {} | |
| dic['No disaster'] = 1 - pred[0] | |
| dic['Disaster'] = pred[0] | |
| return dic | |
| interface = gr.Interface(fn=tweets_predictions, inputs='textbox', outputs='label', theme='darkdefault', | |
| title='Tweets Disaster', description='Ecrire une phrase en anglais et cliquer sur "Submit". Le modèle retourne la probabilité que le message annonce une catastrophe').launch(share=True) |