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| import torch | |
| from transformers import (T5ForConditionalGeneration,T5Tokenizer) | |
| import gradio as gr | |
| best_model_path = "swcrazyfan/Dekingify-T5-Large" | |
| model = T5ForConditionalGeneration.from_pretrained(best_model_path) | |
| tokenizer = T5Tokenizer.from_pretrained("swcrazyfan/Dekingify-T5-Large") | |
| def tokenize_data(text): | |
| # Tokenize the review body | |
| # input_ = "paraphrase: "+ str(text) + ' >' | |
| input_ = "dekingify: " + str(text) + ' </s>' | |
| max_len = 512 | |
| # tokenize inputs | |
| tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt') | |
| inputs={"input_ids": tokenized_inputs['input_ids'], | |
| "attention_mask": tokenized_inputs['attention_mask']} | |
| return inputs | |
| #def generate_answers(text, max_length, min_length, num_beams): | |
| def generate_answers(text, max_length, num_beams): | |
| inputs = tokenize_data(text) | |
| results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True, | |
| num_beams=num_beams, | |
| max_length=max_length, | |
| # min_length=min_length, | |
| early_stopping=True, | |
| num_return_sequences=1) | |
| answer = tokenizer.decode(results[0], skip_special_tokens=True) | |
| return answer | |
| #iface = gr.Interface(title="DeKingify", description="Write anything below. Then, click submit to 'DeKingify' it.", fn=generate_answers, inputs=[gr.inputs.Textbox(label="Original Text",lines=10), gr.inputs.Slider(label="Maximum Length", minimum=1, maximum=512, default=512, step=1), gr.inputs.Slider(label="Minimum Length", minimum=1, maximum=512, default=1, step=1), gr.inputs.Slider(label="Number of Beams", minimum=1, maximum=50, default=5, step=1)], outputs=["text"]) | |
| iface = gr.Interface(title="DeKingify", description="Write anything below. Then, click submit to 'DeKingify' it.", fn=generate_answers, inputs=[gr.inputs.Textbox(label="Original Text",lines=10), gr.inputs.Slider(label="Maximum Length", minimum=1, maximum=512, default=512, step=1), gr.inputs.Slider(label="Number of Beams", minimum=1, maximum=50, default=5, step=1)], outputs=["text"]) | |
| iface.launch(inline=False) |