Update app.py

app.py

@@ -24,11 +24,11 @@ word_vectors = api.load("glove-wiki-gigaword-50")
 # Check for GPU and set the device accordingly
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Load AI Detector model and tokenizer from Hugging Face (roberta-base-openai-detector)
-tokenizer_ai = AutoTokenizer.from_pretrained("roberta-base-openai-detector")
-model_ai = AutoModelForSequenceClassification.from_pretrained("roberta-base-openai-detector").to(device)
+# Load AI Detector model and tokenizer from Hugging Face (e.g., GPT-Neo)
+tokenizer_ai = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
+model_ai = AutoModelForSequenceClassification.from_pretrained("EleutherAI/gpt-neo-2.7B").to(device)
 
-# AI detection function using RoBERTa-based model
+# AI detection function using GPT-Neo-based model
 def detect_ai_generated(text):
     inputs = tokenizer_ai(text, return_tensors="pt", truncation=True, max_length=512).to(device)
     with torch.no_grad():
@@ -63,13 +63,13 @@ def capitalize_sentences_and_nouns(text):
 
     return ' '.join(corrected_text)
 
-# Paraphrasing function using spaCy and NLTK
+# Paraphrasing function using SpaCy and NLTK
 def paraphrase_with_spacy_nltk(text):
     doc = nlp(text)
     paraphrased_words = []
     
     for token in doc:
-        # Map spaCy POS tags to WordNet POS tags
+        # Map SpaCy POS tags to WordNet POS tags
         pos = None
         if token.pos_ in {"NOUN"}:
             pos = wordnet.NOUN