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Create app.py
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app.py
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import os
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# install torch and tf
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os.system('pip install transformers SentencePiece')
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os.system('pip install torch')
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# pip install streamlit-chat
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os.system('pip install streamlit-chat')
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from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer
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import torch
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import streamlit as st
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from streamlit_chat import message
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# 下载模型
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tokenizer = T5Tokenizer.from_pretrained("ClueAI/ChatYuan-large-v1")
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model = T5ForConditionalGeneration.from_pretrained("ClueAI/ChatYuan-large-v1")
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# 修改colab笔记本设置为gpu,推理更快
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device = torch.device('cpu')
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model.to(device)
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print('Model Load done!')
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def preprocess(text):
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text = text.replace("\n", "\\n").replace("\t", "\\t")
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return text
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def postprocess(text):
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return text.replace("\\n", "\n").replace("\\t", "\t")
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def answer(history, sample=True, top_p=1, temperature=0.7):
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'''sample:是否抽样。生成任务,可以设置为True;
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top_p:0-1之间,生成的内容越多样
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max_new_tokens=512 lost...'''
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preprocess_history = []
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for i in range(len(history)):
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preprocess_history[i] = preprocess(text)
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#text = preprocess(text)
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#print('用户: '+text)
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encoding = tokenizer(text=preprocess_history, truncation=True, padding=True, max_length=768, return_tensors="pt").to(device)
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if not sample:
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out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, num_beams=1, length_penalty=0.6)
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else:
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out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, do_sample=True, top_p=top_p, temperature=temperature, no_repeat_ngram_size=3)
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out_text = tokenizer.batch_decode(out["sequences"], skip_special_tokens=True)
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print('小元: '+postprocess(out_text[0]))
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return postprocess(out_text[0])
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st.set_page_config(
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page_title="Chinese ChatBot - Demo",
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page_icon=":robot:"
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)
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st.header("Chinese ChatBot - Demo")
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st.markdown("[Github](https://github.com/scutcyr)")
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if 'generated' not in st.session_state:
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st.session_state['generated'] = []
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if 'past' not in st.session_state:
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st.session_state['past'] = []
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def query(history):
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inputs = tokenizer.dialogue_encode(
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history, add_start_token_as_response=True, return_tensors=True, is_split_into_words=False
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)
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inputs["input_ids"] = inputs["input_ids"].astype("int64")
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ids, scores = model.generate(
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input_ids=inputs["input_ids"],
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token_type_ids=inputs["token_type_ids"],
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position_ids=inputs["position_ids"],
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attention_mask=inputs["attention_mask"],
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max_length=64,
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min_length=1,
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decode_strategy="sampling",
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temperature=1.0,
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top_k=5,
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top_p=1.0,
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num_beams=0,
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length_penalty=1.0,
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early_stopping=False,
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num_return_sequences=20,
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)
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max_dec_len = 64
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num_return_sequences = 20
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bot_response = select_response(
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ids, scores, tokenizer, max_dec_len, num_return_sequences, keep_space=False
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)[0]
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return bot_response
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def get_text():
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input_text = st.text_input("用户: ","你好!", key="input")
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return input_text
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history = []
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user_input = get_text()
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history.append(user_input)
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if user_input:
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output = answer(history)
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st.session_state.past.append(user_input)
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st.session_state.generated.append(output)
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history.append(output)
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if st.session_state['generated']:
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for i in range(len(st.session_state['generated'])-1, -1, -1):
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message(st.session_state["generated"][i], key=str(i))
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message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
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