File size: 2,156 Bytes
2f9f9d9 625452f 4ce54e2 b976ff0 4ce54e2 5f8d01c 4ce54e2 5f8d01c 4ce54e2 8298371 4ce54e2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
import gradio as gr
import tensorflow as tf
from keras_preprocessing.text import tokenizer_from_json
import numpy as np
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
from nltk.corpus import stopwords
import re
from nltk.stem import WordNetLemmatizer
stemmer = WordNetLemmatizer()
from keras.preprocessing.text import Tokenizer
from keras import backend as K
#Завантажуємо наші моделі
def recall(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def f1(y_true, y_pred):
p = precision(y_true, y_pred)
r = recall(y_true, y_pred)
return 2 * ((p * r) / (p + r))
def accuracy(y_true, y_pred):
return K.mean(K.equal(y_true, K.round(y_pred)), axis=1)
with open('tokenizer.json', 'r', encoding='utf-8') as f:
tokenizer_config = f.read()
tokenizer = tokenizer_from_json(tokenizer_config)
model = tf.keras.models.load_model("model_RNN.h5", custom_objects={'f1':f1, 'recall': recall, 'precision': precision, 'accuracy':accuracy})
def get_sentiment(text):
global model
text = re.sub(r"@\S+|https?:\S+|http?:\S|[^A-Za-z0-9]+", ' ', str(text))
text = re.sub(r'\s+[a-zA-Z]\s+', ' ', text)
text = re.sub(r'\s+', ' ', text, flags=re.I)
text = text.lower()
text = text.split()
text = [stemmer.lemmatize(word) for word in text]
text = ' '.join(text)
text = tokenizer.texts_to_sequences([text])[0]
text += [0] * (200 - len(text))
text = np.array(text).reshape(-1, 200)
x = model.predict(text).tolist()[0][0]
return x
interface = gr.Interface(fn = get_sentiment, inputs = 'text', outputs = 'text', title = 'Kotsko Kyrylo IPS-42 Sentiment140', description = 'Kotsko Kyrylo IPS-42 Sentiment140')
interface.launch(share=False)
|