File size: 2,788 Bytes
8ae18de a55dbd1 8ae18de a55dbd1 8ae18de | 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 | import streamlit as st
import pandas as pd
import numpy as np
from unidecode import unidecode
import tensorflow as tf
import cloudpickle
from transformers import DistilBertTokenizerFast
import os
from matplotlib import pyplot as plt
from PIL import Image
with open(os.path.join("models", "toxic_comment_preprocessor_classnames.bin"), "rb") as model_file_obj:
text_preprocessor, class_names = cloudpickle.load(model_file_obj)
interpreter = tf.lite.Interpreter(model_path=os.path.join("models", "toxic_comment_classifier_hf_distilbert.tflite"))
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def inference(text):
text = text_preprocessor.preprocess(pd.Series(text))[0]
model_checkpoint = "distilbert-base-uncased"
tokenizer = DistilBertTokenizerFast.from_pretrained(model_checkpoint)
tokens = tokenizer(text, max_length=512, padding="max_length", truncation=True, return_tensors="tf")
# tflite model inference
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()[0]
attention_mask, input_ids = tokens['attention_mask'], tokens['input_ids']
interpreter.set_tensor(input_details[0]["index"], attention_mask)
interpreter.set_tensor(input_details[1]["index"], input_ids)
interpreter.invoke()
tflite_logits = interpreter.get_tensor(output_details["index"])[0]
tflite_pred = sigmoid(tflite_logits)
result_df = pd.DataFrame({'class': class_names, 'prob': tflite_pred})
result_df.sort_values(by='prob', ascending=True, inplace=True)
return result_df
def display_image(df):
fig, ax = plt.subplots(figsize=(2, 1.8))
df.plot(x='class', y='prob', kind='barh', ax=ax, color='black', ylabel='')
ax.tick_params(axis='both', which='major', labelsize=8.5)
ax.get_legend().remove()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_xaxis().set_ticks([])
plt.rcParams["figure.autolayout"] = True
plt.xlim(0, 1)
for n, i in enumerate([*df['prob']]):
plt.text(i+0.015, n-0.15, f'{str(np.round(i, 3))} ', fontsize=7.5)
fig.savefig("prediction.png", bbox_inches='tight', dpi=100)
image = Image.open('prediction.png')
st.write('')
st.image(image, output_format="PNG", caption="Prediction")
############## ENTRY POINT START #######################
def main():
st.title("Toxic Comment Classifier")
comment_txt = st.text_area("Enter a comment:", "", height=100)
if st.button("Submit"):
df = inference(comment_txt)
display_image(df)
############## ENTRY POINT END #######################
if __name__ == "__main__":
main() |