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import torch |
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from transformers import (PegasusForConditionalGeneration, PegasusTokenizer) |
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import gradio as gr |
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best_model_path = "springml111/Pegasus_Paraphrase_model" |
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model = PegasusForConditionalGeneration.from_pretrained(best_model_path) |
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tokenizer = PegasusTokenizer.from_pretrained("springml111/Pegasus_Paraphrase_model") |
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def tokenize_data(text): |
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input_ = str(text) + ' </s>' |
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max_len = 64 |
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tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt') |
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inputs={"input_ids": tokenized_inputs['input_ids'], |
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"attention_mask": tokenized_inputs['attention_mask']} |
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return inputs |
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def generate_answers(text): |
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inputs = tokenize_data(text) |
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results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True, |
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max_length=64, |
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top_k=120, |
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top_p=0.98, |
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early_stopping=True, |
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num_return_sequences=1) |
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answer = tokenizer.decode(results[0], skip_special_tokens=True) |
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return answer |
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iface = gr.Interface(fn=generate_answers, inputs=[gr.inputs.Textbox(lines=5)],outputs=["text"]) |
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iface.launch() |
