Create app.py

app.py

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