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app.py

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+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,max_length=100,no_repeat_ngram_size=True)
+    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()
+
+

requirements.txt

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+gradio
+transformers
+torch
+