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import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, BertForSequenceClassification
from sklearn import metrics
import streamlit as st
# Define models to be used
bert_path = "bert-base-uncased"
bert_tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = BertForSequenceClassification.from_pretrained(bert_path, num_labels=6)
tuned_model = model = torch.load("pytorch_bert_toxic.bin")
# Read and format data.
tweets_raw = pd.read_csv("test.csv", nrows=20)
labels_raw = pd.read_csv("test_labels.csv", nrows=20)
label_set = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
label_vector = labels_raw[label_set].values.tolist()
tweet_df = tweets_raw[["comment_text"]]
tweet_df["labels"] = label_vector
# User selects model for front-end.
option = st.selectbox("Select a text analysis model:", ("BERT", "Fine-tuned BERT"))
if option == "BERT":
tokenizer = bert_tokenizer
model = bert_model
else:
tokenizer = bert_tokenizer
model = tuned_model
# Dataset for loading tables into DataLoader
class ToxicityDataset(Dataset):
def __init__(self, dataframe, tokenizer, max_len):
self.tokenizer = tokenizer
self.data = dataframe
self.text = self.data.comment_text
self.targets = self.data.labels
self.max_len = max_len
def __len__(self):
return len(self.text)
def __getitem__(self, index):
text = str(self.text[index])
text = " ".join(text.split())
inputs = self.tokenizer.encode_plus(
text,
None,
add_special_tokens=True,
max_length=self.max_len,
padding="max_length",
truncation=True,
return_token_type_ids=True,
)
ids = inputs["input_ids"]
mask = inputs["attention_mask"]
token_type_ids = inputs["token_type_ids"]
return {
"ids": torch.tensor(ids, dtype=torch.long),
"mask": torch.tensor(mask, dtype=torch.long),
"token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
"targets": torch.tensor(self.targets[index], dtype=torch.float),
}
# Based on user model selection, prepare Dataset and DataLoader
MAX_LENGTH = 100
TEST_BATCH_SIZE = 128
infer_dataset = ToxicityDataset(tweet_df, tokenizer, MAX_LENGTH)
infer_params = {"batch_size": TEST_BATCH_SIZE, "shuffle": True, "num_workers": 0}
infer_loader = DataLoader(test_dataset, **test_params)
# Freeze model and input tokens
def inference():
model.eval()
final_targets = []
final_outputs = []
with torch.no_grad():
for _, data in enumerate(infer_loader, 0):
ids = data["ids"].to(device, dtype=torch.long)
mask = data["mask"].to(device, dtype=torch.long)
token_type_ids = data["token_type_ids"].to(device, dtype=torch.long)
targets = data["targets"].to(device, dtype=torch.float)
outputs = model(ids, mask, token_type_ids)
final_targets.extend(targets.cpu().detach().numpy().tolist())
final_outputs.extend(torch.sigmoid(outputs).cpu().detach().numpy().tolist())
return final_outputs, final_targets
prediction, targets = inference()
prediction = np.array(prediction) >= 0.5
targets = np.argmax(targets, axis=1)
prediction = np.argmax(prediction, axis=1)
accuracy = metrics.accuracy_score(targets, prediction)
f1_score_micro = metrics.f1_score(targets, prediction, average="micro")
f1_score_macro = metrics.f1_score(targets, prediction, average="macro")
st.write(prediction)
st.write(f"Accuracy Score = {accuracy}")
st.write(f"F1 Score (Micro) = {f1_score_micro}")
st.write(f"F1 Score (Macro) = {f1_score_macro}")
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