Update app.py

app.py

@@ -123,20 +123,41 @@
 # out=grad.Textbox(lines=1, label="Probablity of label being true is")
 # grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()
 
+# from transformers import GPT2LMHeadModel,GPT2Tokenizer
+# 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
+# txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
+# out=grad.Textbox(lines=1, label="Generated Tensors")
+# grad.Interface(generate, inputs=txt, outputs=out).launch()
 from transformers import GPT2LMHeadModel,GPT2Tokenizer
 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
+    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
+
 txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
 out=grad.Textbox(lines=1, label="Generated Tensors")
 grad.Interface(generate, inputs=txt, outputs=out).launch()
 
 
 
+
+