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app.py

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+import gradio as gr
+import tensorflow as tf
+from transformers import BertTokenizer, TFBertForSequenceClassification
+import re
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from nltk.stem import PorterStemmer
+from tqdm import tqdm
+import nltk
+nltk.download('wordnet')
+nltk.download('stopwords')
+def clean_text(raw_text):
+    # Remove unnecessary symbols and numbers
+    cleaned_text = re.sub('[^a-zA-Z]', ' ', raw_text)
+    
+    # Convert to lowercase
+    cleaned_text = cleaned_text.lower()
+    
+    # Tokenize the text
+    words = cleaned_text.split()
+    
+    # Remove stop words
+    stop_words = set(stopwords.words('english'))
+    words = [word for word in words if word not in stop_words]
+    
+    # Perform stemming
+    stemmer = PorterStemmer()
+    words = [stemmer.stem(word) for word in words]
+    
+    # Perform lemmatization
+    lemmatizer = WordNetLemmatizer()
+    words = [lemmatizer.lemmatize(word) for word in words]
+    
+    # Join the cleaned words back into a single string
+    cleaned_text = ' '.join(words)
+    
+    return cleaned_text
+# Load the pre-trained BERT model and tokenizer
+model_name = 'bert-base-uncased'
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = TFBertForSequenceClassification.from_pretrained("troll_model")
+
+def predict_text(input_text):
+    # Tokenize and encode the input text
+    input_ids = tokenizer.encode_plus(
+        clean_text(input_text),
+        add_special_tokens=True,
+        max_length=128,
+        padding='longest',
+        truncation=True,
+        return_tensors='tf'
+    )['input_ids']
+
+    # Make prediction
+    predictions = model.predict(input_ids)[0]
+
+    # Get predicted label and confidence scores
+    predicted_label = tf.argmax(predictions, axis=1).numpy()[0]
+    confidence_scores = tf.nn.softmax(predictions, axis=1).numpy()[0]
+
+    # Interpret the predicted label
+    if predicted_label == 0:
+        output_text = f"Not troll, Troll level: {confidence_scores[1]}"
+    else:
+        output_text = f"Troll, Troll level: {confidence_scores[1]}"
+
+    return output_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_text,
+    inputs="text",
+    outputs="text",
+    title="Text Classification",
+    description="Enter a text and the model will predict its class.",
+    theme="default"
+)
+
+# Launch the interface
+iface.launch()