Update app.py

app.py

@@ -24,16 +24,19 @@
 
 
 import os
+import nltk
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 import gradio as gr
 from nltk.tokenize import sent_tokenize
 from difflib import SequenceMatcher
 
 # Ensure the necessary NLTK data is downloaded
-os.system('python download.py')
+nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
+nltk.download('wordnet')
 
 # Load a pre-trained T5 model specifically fine-tuned for grammar correction
-tokenizer = T5Tokenizer.from_pretrained("prithivida/grammar_error_correcter_v1")
+tokenizer = T5Tokenizer.from_pretrained("prithivida/grammar_error_correcter_v1", legacy=False)
 model = T5ForConditionalGeneration.from_pretrained("prithivida/grammar_error_correcter_v1")
 
 # Function to perform grammar correction
@@ -80,7 +83,7 @@ interface = gr.Interface(
         "In the past, did you have a similar experience where you found something valuable or interesting? Tell the story. Describe what you found, what you did with it, and how you felt about your decision.\n\n"
         "Remember to use past tense in your writing.\n\n"
         "<b>A student's sample answer:</b>\n"
-        "<blockquote>When I was 10, I find an old coin in my backyard. I kept it for a while and shows it to my friends. They was impressed and say it might be valuable. Later, I take it to a local antique shop, and the owner told me it was very old. I decided to give it to the museum in my town. The museum was happy and put it on display. I feel proud of my decision.<br><br><i>Copy and paste to try.</i></blockquote>"
+        "<blockquote>When I was 10, I found an old coin in my backyard. I kept it for a while and showed it to my friends. They were impressed and said it might be valuable. Later, I took it to a local antique shop, and the owner told me it was very old. I decided to give it to the museum in my town. The museum was happy and put it on display. I felt proud of my decision.<br><br><i>Copy and paste to try.</i></blockquote>"
     )
 )