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import pandas as pd
import numpy as np
from torch.utils.data import Dataset
import torch
from transformers import AutoTokenizer
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from transformers import AutoModel, AdamW, get_cosine_schedule_with_warmup
import torch.nn as nn
import math
from torchmetrics.functional.classification import auroc
import torch.nn.functional as F
import streamlit as st
from transformers import pipeline
class toxicity_dataset(Dataset):
def __init__(self,data_path,tokenizer,attributes,max_token_len= 128,sample = 1000):
self.data_path=data_path
self.tokenizer=tokenizer
self.attributes=attributes
self.max_token_len=max_token_len
self.sample=sample
self._prepare_data()
def _prepare_data(self):
data=pd.read_csv(self.data_path)
if self.sample is not None:
self.data=data.sample(self.sample,random_state=7)
else:
self.data=data
def __len__(self):
return(len(self.data))
def __getitem__(self,index):
item = self.data.iloc[index]
comment = str(item.comment_text)
attributes = torch.FloatTensor(item[self.attributes])
tokens = self.tokenizer.encode_plus(comment,add_special_tokens=True,return_tensors="pt",truncation=True,max_length=self.max_token_len,padding="max_length",return_attention_mask=True)
return{'input_ids':tokens.input_ids.flatten(),"attention_mask":tokens.attention_mask.flatten(),"labels":attributes}
class Toxcity_Data_Module(pl.LightningDataModule):
def __init__(self,train_path,test_path,attributes,batch_size = 16, max_token_len = 128, model_name="roberta-base"):
super().__init__()
self.train_path=train_path
self.test_path=test_path
self.attributes=attributes
self.batch_size=batch_size
self.max_token_len=max_token_len
self.model_name=model_name
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
def setup(self, stage = None):
if stage in (None, "fit"):
self.train_dataset=toxicity_dataset(self.train_path,self.tokenizer,self.attributes)
self.test_dataset=toxicity_dataset(self.test_path,self.tokenizer,self.attributes, sample=None)
if stage == "predict":
self.val_dataset=toxicity_dataset(self.test_path,self.tokenizer,self.attributes)
def train_dataloader(self):
return DataLoader(self.train_dataset,batch_size=self.batch_size,shuffle=True)
def val_dataloader(self):
return DataLoader(self.train_dataset,batch_size=self.batch_size,shuffle=False)
def predict_dataloader(self):
return DataLoader(self.test_dataset,batch_size=self.batch_size,shuffle=False)
class Toxic_Comment_Classifier(pl.LightningModule):
def __init__(self, config: dict):
super().__init__()
self.config = config
self.pretrained_model = AutoModel.from_pretrained(config['model_name'], return_dict = True)
self.hidden = torch.nn.Linear(self.pretrained_model.config.hidden_size, self.pretrained_model.config.hidden_size)
self.classifier = torch.nn.Linear(self.pretrained_model.config.hidden_size, self.config['n_labels'])
torch.nn.init.xavier_uniform_(self.classifier.weight)
self.loss_func = nn.BCEWithLogitsLoss(reduction='mean')
self.dropout = nn.Dropout()
def forward(self, input_ids, attention_mask=None, labels=None):
# roberta layer
output = self.pretrained_model(input_ids=input_ids, attention_mask=attention_mask)
pooled_output = torch.mean(output.last_hidden_state, 1)
# final logits
pooled_output = self.dropout(pooled_output)
pooled_output = self.hidden(pooled_output)
pooled_output = F.relu(pooled_output)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
# calculate loss
loss = 0
if labels is not None:
loss = self.loss_func(logits.view(-1, self.config['n_labels']), labels.view(-1, self.config['n_labels']))
return loss, logits
def training_step(self, batch, batch_index):
loss, outputs = self(**batch)
self.log("train loss ", loss, prog_bar = True, logger=True)
return {"loss":loss, "predictions":outputs, "labels": batch["labels"]}
def validation_step(self, batch, batch_index):
loss, outputs = self(**batch)
self.log("validation loss ", loss, prog_bar = True, logger=True)
return {"val_loss": loss, "predictions":outputs, "labels": batch["labels"]}
def predict_step(self, batch, batch_index):
loss, outputs = self(**batch)
return outputs
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=self.config['lr'], weight_decay=self.config['w_decay'])
total_steps = self.config['train_size']/self.config['bs']
warmup_steps = math.floor(total_steps * self.config['warmup'])
warmup_steps = math.floor(total_steps * self.config['warmup'])
scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)
return [optimizer],[scheduler]
def predict_raw_comments(model, dm, trainer):
#print("debug1")
predictions = trainer.predict(model,dm)
#print("debug2")
flattened_predictions = np.stack([torch.sigmoid(torch.Tensor(p)) for batch in predictions for p in batch])
#print("debug3")
return flattened_predictions
def main():
# -- Creates Variables for Use of Model --
attributes=["toxic","severe_toxic","obscene","threat","insult","identity_hate"]
tokenizer=AutoTokenizer.from_pretrained("roberta-base")
toxic_comments_dataset=toxicity_dataset("AppDirectory/data/train.csv",tokenizer,attributes)
toxicity_data_module=Toxcity_Data_Module("AppDirectory/data/train.csv","AppDirectory/data/test.csv",attributes)
toxicity_data_module.setup()
dataloader=toxicity_data_module.train_dataloader()
config = {
'model_name':"distilroberta-base",
'n_labels':len(attributes),
'bs':128,
'lr':1.5e-6,
'warmup':0.2,
"train_size":len(toxicity_data_module.train_dataloader()),
'w_decay':0.001,
'n_epochs':1
}
toxicity_data_module=Toxcity_Data_Module("AppDirectory/data/train.csv","AppDirectory/data/reduced_test.csv",attributes,batch_size=config['bs'])
toxicity_data_module.setup()
trainer = pl.Trainer(max_epochs=config['n_epochs'],num_sanity_val_steps=50)
## -- Creates Streamlit App --
st.title("Tweet Toxicity Classifier ")
st.header("Fine tuned model from roberta-base using PyTorch")
st.header("Jozef Janosko - CS 482, Milestone 3")
model_name = st.selectbox("Select Model...", ["Toxicity Classification Model"])
if st.button("Click to Load Data"):
if model_name=="Toxicity Classification Model":
model = torch.load("ToxicityClassificationModel.pt")
with st.spinner('Analyzing Text...'):
logits = predict_raw_comments(model,toxicity_data_module,trainer=trainer)
torch_logits = torch.from_numpy(logits)
probabilities = F.softmax(torch_logits, dim = -1).numpy()
inputs=pd.read_csv("AppDirectory/data/reduced_test.csv")
data=[]
#print(inputs["comment_text"][0]," ",probabilities)
for i in range(len(probabilities)):
max_prob = 0
max_cat = 6
prob=0
for j in range(6):
prob=probabilities[i][j]
if(prob >= max_prob):
max_prob = prob
max_cat = j
#print(inputs["comment_text"][i]," ",attributes[max_cat]," ",max_prob," ",probabilities[i])
data.append([inputs["comment_text"][i][0:16]+"...",attributes[max_cat],max_prob])
results_df=pd.DataFrame(data,columns=["Comment Text","Most Likely Classification","Classification Probability"])
st.table(data=results_df)
if __name__ == '__main__' :
main()
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