40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
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import gradio as gr
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from transformers import pipeline, set_seed
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def inference(text):
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model_path = "distilgpt2"
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generator = pipeline('text-generation', model=model_path)
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set_seed(42)
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output=[]
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lst=generator(text, max_length=20, num_return_sequences=5)
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for dic in lst:
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output.append(dic['generated_text'])
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return output
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# tokenizer = GPT2Tokenizer.from_pretrained(model_path)
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# model = GPT2Model.from_pretrained(model_path)
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# encoded_input = tokenizer(text, return_tensors='pt')
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# output = model(**encoded_input)
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# print(output)
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# return output
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examples=[["Hello, I’m a language model."]]
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# Text generation:distilgpt2
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Gradio Demo for distilgpt2. 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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