| import torch |
| from transformers import DistilBertTokenizer, DistilBertForSequenceClassification |
| import gradio as gr |
| import re |
| import nltk |
| from nltk.tokenize import word_tokenize |
| from nltk.corpus import stopwords |
| from nltk.stem import WordNetLemmatizer |
|
|
| |
| nltk.download('punkt_tab') |
| nltk.download('stopwords') |
| nltk.download('wordnet') |
|
|
| |
| def preprocess(text): |
| text = re.sub(r'[^a-zA-Z\s]', '', text).lower() |
| tokens = word_tokenize(text) |
| stop_words = set(stopwords.words('english')) |
| tokens = [word for word in tokens if word not in stop_words] |
| lemmatizer = WordNetLemmatizer() |
| tokens = [lemmatizer.lemmatize(word) for word in tokens] |
| return ' '.join(tokens) |
|
|
| |
| tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') |
| model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2) |
| model.load_state_dict(torch.load('best_model (3).pth', map_location=torch.device('cpu'))) |
| model.eval() |
|
|
| |
| def classify_essay(text): |
| cleaned_text = preprocess(text) |
| inputs = tokenizer(cleaned_text, return_tensors='pt', truncation=True, padding=True, max_length=100) |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| probs = torch.nn.functional.softmax(outputs.logits, dim=1) |
| predicted_class = torch.argmax(probs, dim=1).item() |
| labels = ["Human-Written", "AI-Generated"] |
| return {labels[0]: float(probs[0][0]), labels[1]: float(probs[0][1])} |
|
|
| |
| iface = gr.Interface( |
| fn=classify_essay, |
| inputs=gr.Textbox(lines=10, placeholder="Paste your essay here..."), |
| outputs=gr.Label(num_top_classes=2), |
| title="Essay Authorship Classifier", |
| description="Detect whether an essay is AI-generated or human-written using a fine-tuned DistilBERT model." |
| ) |
|
|
| |
| if __name__ == "__main__": |
| iface.launch() |
|
|
