Delete app.py

app.py

@@ -1,136 +0,0 @@
-# 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')
-