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app.py

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+# Importing necessary libraries
+import streamlit as st
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import re
+
+st.title('Toxic Comment Classification')
+comment = st.text_area("Enter Your Text", "Type Here")
+
+comment_input = []
+comment_input.append(comment)
+test_df = pd.DataFrame()
+test_df['comment_text'] = comment_input
+cols = {'toxic':[0], 'severe_toxic':[0], 'obscene':[0], 'threat':[0], 'insult':[0], 'identity_hate':[0]}
+for key in cols.keys():
+    test_df[key] = cols[key]
+test_df = test_df.reset_index()
+test_df.drop(columns=["index"], inplace=True)
+
+# Data Cleaning and Preprocessing
+# creating copy of data for data cleaning and preprocessing
+cleaned_data = test_df.copy()
+
+# Removing Hyperlinks from text
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"https?://\S+|www\.\S+","",x) )
+
+# Removing emojis from text
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub("["
+                                                                                   u"\U0001F600-\U0001F64F"
+                                                                                   u"\U0001F300-\U0001F5FF"  
+                                                                                   u"\U0001F680-\U0001F6FF" 
+                                                                                   u"\U0001F1E0-\U0001F1FF"  
+                                                                                   u"\U00002702-\U000027B0"
+                                                                                   u"\U000024C2-\U0001F251"
+                                                                                   "]+","", x, flags=re.UNICODE))
+
+# Removing IP addresses from text 
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}","",x))
+
+# Removing html tags from text 
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"<.*?>","",x))
+
+# There are some comments which contain double quoted words like --> ""words""  we will convert these to --> "words" 
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\"\"", "\"",x))   # replacing "" with "
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"^\"", "",x))      # removing quotation from start and the end of the string
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\"$", "",x))
+
+# Removing Punctuation / Special characters (;:'".?@!%&*+) which appears more than twice in the text 
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"[^a-zA-Z0-9\s][^a-zA-Z0-9\s]+", " ",x))
+
+# Removing Special characters 
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"[^a-zA-Z0-9\s\"\',:;?!.()]", " ",x))
+
+# Removing extra spaces in text
+cleaned_data["comment_text"] = cleaned_data["comment_text"].map(lambda x: re.sub(r"\s\s+", " ",x))
+
+Final_data = cleaned_data.copy()
+
+# Model Building
+from transformers import DistilBertTokenizer
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Dataset
+
+# Using Pretrained DistilBertTokenizer
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+
+# Creating Dataset class for Toxic comments and Labels 
+class Toxic_Dataset(Dataset):
+    def __init__(self, Comments_, Labels_):
+        self.comments = Comments_.copy()
+        self.labels = Labels_.copy()
+        
+        self.comments["comment_text"] = self.comments["comment_text"].map(lambda x: tokenizer(x, padding="max_length", truncation=True, return_tensors="pt"))
+    
+    def __len__(self):
+        return len(self.labels)
+    
+    def __getitem__(self, idx):
+        comment = self.comments.loc[idx,"comment_text"]
+        label = np.array(self.labels.loc[idx,:])
+        
+        return comment, label
+
+X_test = pd.DataFrame(test_df.iloc[:, 0])
+Y_test = test_df.iloc[:, 1:]
+Test_data = Toxic_Dataset(X_test, Y_test)
+Test_Loader = DataLoader(Test_data, shuffle=False)
+
+# Loading pre-trained weights of DistilBert model for sequence classification
+# and changing classifiers output to 6 because we have 6 labels to classify.
+# DistilBERT
+
+from transformers import DistilBertForSequenceClassification
+
+Distil_bert = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
+
+Distil_bert.classifier = nn.Sequential(
+                    nn.Linear(768,6),
+                    nn.Sigmoid()
+                  )
+# print(Distil_bert)
+
+# Instantiating the model and loading the weights
+model = Distil_bert
+model.to('cpu')
+model = torch.load('dsbert_toxic.pt', map_location=torch.device('cpu'))
+
+# Making Predictions
+for comments, labels in Test_Loader:
+    labels = labels.to('cpu')
+    labels = labels.float()
+    masks = comments['attention_mask'].squeeze(1).to('cpu')
+    input_ids = comments['input_ids'].squeeze(1).to('cpu')
+    
+    output = model(input_ids, masks)
+    op = output.logits
+    
+    res = []
+    for i in range(6):
+        res.append(op[0, i])
+    # print(res)
+
+preds = []
+
+for i in range(len(res)):
+    preds.append(res[i].tolist())
+
+
+if st.button('Classify'):
+    for i in range(len(res)):
+        st.write(f"{Y_test.columns[i]} : {preds[i]}\n")
+    st.success('These are the outputs')
+    

requirements.txt

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+matplotlib==3.5.1
+numpy==1.21.5
+pandas==1.4.3
+streamlit==1.12.0
+torch==1.12.1+cpu
+transformers==4.21.1