app.py

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("data/train.csv",tokenizer,attributes)

    toxicity_data_module=Toxcity_Data_Module("data/train.csv","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("data/train.csv","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("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()