Update app.py

app.py

@@ -1,41 +1,22 @@
 import sys
-#import subprocess
-#from torch.utils.checkpoint import checkpoint
-# implement pip as a subprocess:
-#subprocess.check_call([sys.executable, '-m', 'pip', 'install','--quiet','sentencepiece==0.1.95'])
-
 import gradio as gr
-#from transformers import pipeline
 from transformers import AutoTokenizer
 import torch
 
 tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
 model = torch.load("helsinki_fineTuned.pt", map_location=torch.device('cpu'))
 model.eval()
-#translation_pipeline = pipeline(model)
 
 
 def translate_gradio(input):
-    '''
-    with tokenizer.as_target_tokenizer():
-        input_ids = tokenizer(input, return_tensors='pt')
-    encode = model.generate(**input_ids)
-#    encode = model.generate(**tokenizer.prepare_seq2seq_batch(input,return_tensors='pt'))
-    text_ar = tokenizer.batch_decode(encode,skip_special_tokens=True)[0]'''
-    
+
     tokenized_text = tokenizer.prepare_seq2seq_batch([input], return_tensors='pt')
-    
-    # Perform translation and decode the output
     encode = model.generate(**tokenized_text)
     text_ar = tokenizer.batch_decode(encode,skip_special_tokens=True)[0]
     return text_ar
 
-
-
-
-
-#description = 'Translating "English Data Science" content into Arabic'
 translate_interface = gr.Interface(fn = translate_gradio,
+                                   allow_flagging = True,
                                    title = 'Translating "English Data Science" content into Arabic',
                                    inputs=gr.inputs.Textbox(lines = 7, label = 'english content'),
                                    outputs="text",