Update app.py

app.py

@@ -1,12 +1,11 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
 import torch
 import spacy
 import subprocess
 import nltk
 from nltk.corpus import wordnet
 from gensim import downloader as api
-import language_tool_python  # LanguageTool for grammar checking
 
 # Ensure necessary NLTK data is downloaded
 nltk.download('wordnet')
@@ -29,8 +28,9 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 tokenizer_ai = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
 model_ai = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english").to(device)
 
-# Initialize LanguageTool for grammar correction (English)
-tool = language_tool_python.LanguageTool('en-US')
+# Load grammar correction model and tokenizer from Hugging Face
+tokenizer_gc = AutoTokenizer.from_pretrained("prithivida/grammar_error_correcter_v1")
+model_gc = AutoModelForSeq2SeqLM.from_pretrained("prithivida/grammar_error_correcter_v1").to(device)
 
 # AI detection function using DistilBERT
 def detect_ai_generated(text):
@@ -123,10 +123,11 @@ def paraphrase_with_spacy_nltk(text):
     
     return corrected_text
 
-# Function to correct grammar using LanguageTool
+# Function to correct grammar using Hugging Face model
 def correct_grammar(text):
-    # Apply grammar and spelling suggestions
-    corrected_text = tool.correct(text)
+    inputs = tokenizer_gc.encode("gec: " + text, return_tensors="pt", truncation=True).to(device)
+    outputs = model_gc.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
+    corrected_text = tokenizer_gc.decode(outputs[0], skip_special_tokens=True)
     return corrected_text
 
 # Combined function: Paraphrase -> Tense Check -> Capitalization -> Grammar Correction
@@ -140,7 +141,7 @@ def paraphrase_and_correct(text):
     # Step 3: Capitalize sentences and proper nouns
     capitalized_text = capitalize_sentences_and_nouns(tense_checked_text)
     
-    # Step 4: Correct grammar
+    # Step 4: Correct grammar using Hugging Face model
     final_text = correct_grammar(capitalized_text)
     
     return final_text