Update app.py
Browse files
app.py
CHANGED
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@@ -1,10 +1,8 @@
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from transformers import GPT2LMHeadModel,GPT2Tokenizer
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import gradio as grad
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mdl = GPT2LMHeadModel.from_pretrained('gpt2')
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gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
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def generate(starting_text):
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tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
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gpt2_tensors = mdl.generate(tkn_ids,max_length=100,no_repeat_ngram_size=True,num_beams=3,do_sample=True,temperatue=1.5)
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@@ -14,11 +12,6 @@ def generate(starting_text):
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response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
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return response
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txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
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out=grad.Textbox(lines=1, label="Generated Tensors")
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grad.Interface(generate, inputs=txt, outputs=out).launch()
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from transformers import GPT2LMHeadModel,GPT2Tokenizer
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import gradio as grad
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mdl = GPT2LMHeadModel.from_pretrained('gpt2')
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gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
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def generate(starting_text):
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tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
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gpt2_tensors = mdl.generate(tkn_ids,max_length=100,no_repeat_ngram_size=True,num_beams=3,do_sample=True,temperatue=1.5)
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response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
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return response
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txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
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out=grad.Textbox(lines=1, label="Generated Tensors")
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grad.Interface(generate, inputs=txt, outputs=out).launch()
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