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e963d84 7e6e555 e963d84 125da18 e963d84 | 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import gradio
import keras
import nltk
import numpy as np
import pandas as pd
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
nltk.download("stopwords")
import re
vocab_size = 25000
embedding_dim = 64
max_length = 50
trunc_type = 'pre'
padding_type = 'pre'
oov_tok = '<OOV>'
labels = {
1: 'admiration',
2: 'anger',
3: 'curiosity',
4: 'disappointment',
5: 'disgust',
6: 'embarrassment',
7: 'excitement',
8: 'fear',
9: 'gratitude',
10: 'love',
11: 'nervousness',
12: 'pride',
13: 'sadness',
14: 'neutral'
}
def get_tokenizer():
df = pd.read_csv('util/temp.csv', ).reset_index(drop=True)
X = df['text']
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(X)
return tokenizer
def clean_text(text):
stemmer = PorterStemmer()
stp_words = set(stopwords.words('english'))
sent = text.replace("NAME", "")
sent = re.sub('[^a-zA-Z]', " ", sent)
sent = sent.lower()
sent = sent.split()
sent = [stemmer.stem(words) for words in sent if words not in stp_words]
text = [' '.join(sent)]
# print(text)
# print((sent))
train_seq = get_tokenizer().texts_to_sequences(text)
# print(train_seq)
test_padding = pad_sequences(train_seq, max_length, padding=padding_type, truncating=trunc_type)
return test_padding
def predict(text):
model = keras.models.load_model('model/cnn_model.h5')
pred = model.predict(text)
pred_class = np.argmax(pred, axis=-1)
return labels.get(pred_class[0])
def my_inference_function(text):
# TODO: do the pre-processing of text to be able to feed it
# in the model
# 1. Preprocess
inp = clean_text(text)
return predict(inp)
gradio_interface = gradio.Interface(
fn=my_inference_function,
inputs="text",
outputs="text",
# outputs=gradio.outputs.Label(num_top_classes=5),
title='Emotion Classification',
description=f"Let's know how you feel!!!",
allow_flagging="never"
)
gradio_interface.launch(inbrowser=True)
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