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Update app.py
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app.py
CHANGED
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import gradio as gr
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from transformers import pipeline, set_seed
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import random
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def
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top_k=50,
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top_p=0.9
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def generate(text):
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set_seed(55)
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result = generator(text,
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max_length=500,
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num_return_sequences=1,
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do_sample=True,
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temperature=0.75,
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top_k=50,
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top_p=0.9)
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return result[0]["generated_text"]
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examples = [
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["闕很大明知金融帳戶之存摺、提款卡及密碼係供自己使用之重要理財工具,"],
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["梅友乾明知其無資力支付酒店消費,亦無付款意願,竟意圖為自己不法之所有,"],
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["王大明意圖為自己不法所有,基於竊盜之犯意,"]
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["陳小智知悉吳良醫院可配合假病患製作不實之診斷證明書、病歷資料,以供渠等向保險公司詐領住院保險給付,即意圖為自己不法之所有,"]
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]
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with gr.Blocks() as demo:
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""")
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with gr.Row():
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with gr.Column():
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with gr.Column():
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result = gr.components.Textbox(lines=15, label="Generative")
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btn.click(rnd_generate, inputs=[prompt], outputs=[result])
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btn2.click(generate, inputs=[prompt], outputs=[result])
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from transformers import AutoModelForCausalLM
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from transformers import BloomTokenizerFast
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from transformers import pipeline, set_seed
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model_name = "bloom-560m"
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model = AutoModelForCausalLM.from_pretrained(f"jslin09/{model_name}-finetuned-fraud")
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tokenizer = BloomTokenizerFast.from_pretrained(f'bigscience/{model_name}', bos_token = '<s>', eos_token = '</s>')
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def generate(prompt):
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result_length = len(prompt) + 4
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inputs = tokenizer(prompt, return_tensors="pt") # 回傳的張量使用 Pytorch的格式。如果是 Tensorflow 格式的話,則指定為 "tf"。
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results = model.generate(inputs["input_ids"],
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num_return_sequences=5, # 產生五個句子回來。
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max_length=result_length,
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early_stopping=True,
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do_sample=True,
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top_k=50,
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top_p=0.9
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)
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return tokenizer.decode(results[0])
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examples = [
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["闕很大明知金融帳戶之存摺、提款卡及密碼係供自己使用之重要理財工具,"],
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["梅友乾明知其無資力支付酒店消費,亦無付款意願,竟意圖為自己不法之所有,"],
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["王大明意圖為自己不法所有,基於竊盜之犯意,"]
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]
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with gr.Blocks() as demo:
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""")
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with gr.Row():
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with gr.Column():
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result = gr.components.Textbox(lines=7, label="生成的草稿", show_label=True)
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prompt = gr.components.Textbox(lines=2, label="輸入提示文字", placeholder=examples[0],visible=False)
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gr.Examples(examples, label='例句', inputs=[prompt])
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prompt.change(generate, inputs=[prompt], outputs=[result])
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btn = gr.Button("下一句")
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btn.click(generate, inputs=[result], outputs=[result])
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if __name__ == "__main__":
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demo.launch()
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