Create app.py

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import re
+import nltk
+import string
+from nltk.corpus import stopwords
+from nltk.stem import SnowballStemmer
+from transformers import pipeline
+
+# Download NLTK resources
+nltk.download('stopwords')
+stopword = set(stopwords.words('english'))
+stemmer = SnowballStemmer("english")
+
+# Load the dataset
+data = pd.read_csv("commentdataset.csv")
+
+# Labelling the data set with classifier classes according to which classifications has to perform
+data["labels"] = data["class"].map({0: "Offensive Language", 1: "Abusive comments", 2: "No Abusive and Offensive"})
+data = data[["comments", "labels"]]
+
+# Clean data function
+def clean(text):
+    text = str(text).lower()
+    text = re.sub(r"she's", "she is", text)
+    text = re.sub(r"it's", "it is", text)
+    text = re.sub(r"that's", "that is", text)
+    text = re.sub(r"what's", "that is", text)
+    text = re.sub(r"where's", "where is", text)
+    text = re.sub(r"how's", "how is", text)
+    text = re.sub(r"'ll", " will", text)
+    text = re.sub(r"'ve", " have", text)
+    text = re.sub(r"'re", " are", text)
+    text = re.sub(r"i'm", "i am", text)
+    text = re.sub(r"r", "", text)
+    text = re.sub(r"he's", "he is", text)
+    text = re.sub(r"'d", " would", text)
+    text = re.sub(r"won't", "will not", text)
+    text = re.sub(r"can't", "cannot", text)
+    text = re.sub(r"n't", " not", text)
+    text = re.sub(r"n'", "ng", text)
+    text = re.sub(r"'bout", "about", text)
+    text = re.sub(r"'til", "until", text)
+    text = re.sub('\[.*?\]', '', text)
+    text = re.sub('https?://\S+|www\.\S+', '', text)
+    text = re.sub('<.*?>+', '', text)
+    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
+    text = re.sub('\n', '', text)
+    text = re.sub('\w*\d\w*', '', text)
+    text = [word for word in text.split(' ') if word not in stopword]
+    text = " ".join(text)
+    text = [stemmer.stem(word) for word in text.split(' ')]
+    text = " ".join(text)
+    return text
+
+data["comments"] = data["comments"].apply(clean)
+
+# Using a pre-trained transformer model for sentiment analysis
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+# Function to classify comments
+def classify_comment(comment):
+    cleaned_comment = clean(comment)
+    prediction = sentiment_pipeline(cleaned_comment)
+    label = prediction[0]['label']
+    return label
+
+comment_input = gr.Textbox(label="Enter a comment")
+classification_output = gr.Label()
+
+# Create the Gradio interface
+interface = gr.Interface(fn=classify_comment, inputs=comment_input, outputs=classification_output, title="Comment Classifier")
+interface.launch()