38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
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import gradio as gr
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from transformers import T5Tokenizer, T5Model
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def inference(text):
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model_path = "t5-large"
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tokenizer = T5Tokenizer.from_pretrained(model_path)
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model = T5Model.from_pretrained(model_path)
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input_ids = tokenizer(
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text, return_tensors="pt"
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).input_ids # Batch size 1
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decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
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# forward pass
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outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
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print(outputs[0])
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last_hidden_states = outputs.last_hidden_state
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return outputs[0]
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examples=[["Studies have been shown that owning a dog is good for you"]]
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# Translation:t5-large
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Gradio Demo for t5-large. To use it, simply type in text, or click one of the examples to load them.
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""")
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with gr.Row():
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text_input = gr.Textbox()
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text_output = gr.Textbox()
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image_button = gr.Button("上传")
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image_button.click(inference, inputs=text_input, outputs=text_output)
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gr.Examples(examples,inputs=text_input)
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demo.launch()
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