Create inference-app.py

inference-app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+from transformers import AutoTokenizer,AutoModel
+import random
+from bs4 import BeautifulSoup
+import re
+
+
+from transformers import AutoModelForSequenceClassification
+import pytorch_lightning as pl
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+train_path = "train.csv"
+test_path = "test.csv"
+test_labels_paths = "test_labels.csv"
+test_df = pd.read_csv(test_path)
+test_labels_df = pd.read_csv(test_labels_paths)
+test_df = pd.concat([test_df.iloc[:, 1], test_labels_df.iloc[:, 1:]], axis = 1)
+test_df.to_csv("test-dataset.csv")
+test_dataset_path = "test-dataset.csv"
+
+#Lets make a new column labeled "healthy"
+
+def healthy_filter(df):
+  if (df["toxic"]==0) and (df["severe_toxic"]==0) and (df["obscene"]==0) and (df["threat"]==0) and (df["insult"]==0) and (df["identity_hate"]==0):
+    return 1
+  else:
+    return 0
+
+attributes = ['toxic', 'severe_toxic', 'obscene', 'threat',
+       'insult', 'identity_hate', 'healthy']
+
+class Comments_Dataset(Dataset):
+  def __init__(self, data_path, tokenizer, attributes, max_token_len = 128, sample=5000):
+    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)
+    data["healthy"] = data.apply(healthy_filter,axis=1)
+    data["unhealthy"] = np.where(data['healthy']==1, 0, 1)
+    if self.sample is not None:
+      unhealthy = data.loc[data["healthy"] == 0]
+      healthy = data.loc[data["healthy"] ==1]
+      self.data = pd.concat([unhealthy, healthy.sample(self.sample, random_state=42)])
+    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,
+                                      padding='max_length',
+                                      max_length=self.max_token_len,
+                                      return_attention_mask = True)
+    return {'input_ids': tokens.input_ids.flatten(), 'attention_mask': tokens.attention_mask.flatten(), 'labels': attributes}
+
+
+class Comments_Data_Module(pl.LightningDataModule):
+
+  def __init__(self, train_path, val_path, attributes, batch_size: int = 16, max_token_length: int = 128,  model_name='roberta-base'):
+    super().__init__()
+    self.train_path = train_path
+    self.val_path = val_path
+    self.attributes = attributes
+    self.batch_size = batch_size
+    self.max_token_length = max_token_length
+    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 = Comments_Dataset(self.train_path, attributes=self.attributes, tokenizer=self.tokenizer)
+      self.val_dataset = Comments_Dataset(self.val_path, attributes=self.attributes, tokenizer=self.tokenizer, sample=None)
+    if stage == 'predict':
+      self.val_dataset = Comments_Dataset(self.val_path, attributes=self.attributes, tokenizer=self.tokenizer, sample=None)
+
+  def train_dataloader(self):
+    return DataLoader(self.train_dataset, batch_size = self.batch_size, num_workers=4, shuffle=True)
+
+  def val_dataloader(self):
+    return DataLoader(self.val_dataset, batch_size = self.batch_size, num_workers=4, shuffle=False)
+
+  def predict_dataloader(self):
+    return DataLoader(self.val_dataset, batch_size = self.batch_size, num_workers=4, shuffle=False)
+
+comments_data_module = Comments_Data_Module(train_path, test_dataset_path, attributes=attributes)
+comments_data_module.setup()
+comments_data_module.train_dataloader()
+
+class Comment_Classifier(pl.LightningModule):
+#the config dict has the hugginface parameters in it
+  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.CrossEntropyLoss()
+    self.dropout = nn.Dropout()
+    
+  def forward(self, input_ids, attention_mask, 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 / classification layers
+    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['weight_decay'])
+    total_steps = self.config['train_size']/self.config['batch_size']
+    warmup_steps = math.floor(total_steps * self.config['warmup'])
+    scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)
+    return [optimizer],[scheduler]
+
+    
+config = {
+    'model_name': 'distilroberta-base',
+    'n_labels': len(attributes),
+    'batch_size': 128,
+    'lr': 1.5e-6,
+    'warmup': 0.2, 
+    'train_size': len(comments_data_module.train_dataloader()),
+    'weight_decay': 0.001,
+    'n_epochs': 100
+}
+
+model_name = 'distilroberta-base'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = Comment_Classifier(config=config)
+model.load_state_dict(torch.load("model_state_dict.pt"))
+model.eval()
+
+
+
+def prepare_tokenized_review(raw_review):
+  # Remove HTML tags with BS
+  review_text = BeautifulSoup(raw_review).get_text()
+  # Removing non-letters using a regular expression
+  review_text = re.sub("[^a-zA-Z!?]"," ", review_text)
+  # Convert words to lower case and split them
+  words = review_text.lower().split()
+
+  return " ".join(words)
+
+def get_encodings(text):
+    MAX_LEN=256
+    encodings = tokenizer.encode_plus(
+        text,
+        None,
+        add_special_tokens=True,
+        max_length=MAX_LEN,
+        padding='max_length',
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors='pt')
+    return encodings
+
+def run_inference(encoding):
+  with torch.no_grad():
+    input_ids = encoding['input_ids'].to(device, dtype=torch.long)
+    attention_mask = encoding['attention_mask'].to(device, dtype=torch.long)
+    output = model(input_ids, attention_mask)
+    final_output = torch.softmax(output[1][0],dim=0).cpu()
+    print(final_output.numpy().tolist())
+    return final_output.numpy().tolist()
+
+
+
+test_tweets = test_df["comment_text"].values
+#streamlit section
+models = ["distilroberta-base"]
+model_pointers = ["default: distilroberta-base"]
+
+# current_random_tweet = test_tweets[random.randint(0,len(test_tweets))]
+# current_random_tweet = prepare_tokenized_review(current_random_tweet)
+st.write("1. Hit the button to view and see the analyis of a random tweet")
+
+with st.form(key="init_form"):
+    current_random_tweet = test_tweets[random.randint(0,len(test_tweets))]
+    current_random_tweet = prepare_tokenized_review(current_random_tweet)
+ 
+
+
+    choice = st.selectbox("Choose Model", model_pointers)
+
+    user_picked_model = models[model_pointers.index(choice)]
+    with st.spinner("Analyzing..."):
+        text_encoding = get_encodings(current_random_tweet)
+        result = run_inference(text_encoding)
+        df = pd.DataFrame({"Tweet":current_random_tweet}, index=[0])
+        df["Highest Toxicity Class"] = attributes[result.index(max(result))]
+        df["Sentiment Score"] = max(result)
+        st.table(df)
+
+    next_tweet = st.form_submit_button("Next Tweet")
+
+if next_tweet:
+    with st.spinner("Analyzing..."):
+        st.write("")