Update app.py

app.py

@@ -4,28 +4,10 @@ import gradio as grad
 mdl = GPT2LMHeadModel.from_pretrained('gpt2')
 gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
 
-# def generate(starting_text):
-#     tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
-#     gpt2_tensors = mdl.generate(tkn_ids)
-#     response = gpt2_tensors
-#     return response
-
-# def generate(starting_text):
-#     tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
-#     gpt2_tensors = mdl.generate(tkn_ids)
-#     response=""
-#     #response = gpt2_tensors
-#     for i, x in enumerate(gpt2_tensors):
-#         response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
-#     return response
-
 def generate(starting_text):
     tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
-    gpt2_tensors = mdl.generate(tkn_ids,max_length=100)
-    response=""
-    #response = gpt2_tensors
-    for i, x in enumerate(gpt2_tensors):
-        response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
+    gpt2_tensors = mdl.generate(tkn_ids)
+    response = gpt2_tensors
     return response
 
 txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")