Update app.py
Browse files
app.py
CHANGED
@@ -1,8 +1,10 @@
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import openai
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import os
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import gradio as gr
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class Conversation:
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def __init__(self, prompt, round):
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def ask(self, question):
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try:
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self.messages.append({"role": "user", "content": question})
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max_tokens=2048,
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top_p=1,
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)
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except Exception as e:
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print(e)
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return e
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message = response["choices"][0]["message"]["content"]
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self.messages.append({"role": "assistant", "content": message})
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if len(self.messages) > self.round * 2 + 1:
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text = self._build_message(self.messages)
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#print (text)
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#print ("=====summarize=====")
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summarize = self.summarize(text)
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#print (summarize)
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#print ("=====summarize=====")
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self.messages = []
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self.messages.append({"role": "system", "content":
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return message
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def summarize(self, text, max_tokens=200):
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response = openai.Completion.create(
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model = "text-davinci-003",
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prompt = text + "\n\n请总结一下上面User和Assistant聊了些什么,限制100字以内:\n",
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max_tokens = max_tokens,
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)
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return response["choices"][0]["text"]
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def _build_message(self, messages):
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text = ""
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for message in messages:
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@@ -56,7 +41,6 @@ class Conversation:
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text += "Assistant : " + message["content"] + "\n\n"
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return text
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prompt = """你是一个大数据和AI领域的专家,用中文回答大数据和AI的相关问题。你的回答需要满足以下要求:
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1. 你的回答必须是中文
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2. 回答限制在200个字以内
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@@ -64,7 +48,6 @@ prompt = """你是一个大数据和AI领域的专家,用中文回答大数据
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conv = Conversation(prompt, 3)
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def answer(question, history=[]):
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history.append(question)
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message = conv.ask(question)
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import os
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "huggingface_model_name" # 将此处替换为你想使用的模型的名称
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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class Conversation:
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def __init__(self, prompt, round):
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def ask(self, question):
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try:
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self.messages.append({"role": "user", "content": question})
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input_text = self._build_message(self.messages)
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encoded_input = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors='pt')
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output = model.generate(encoded_input, max_length=200, temperature=0.5)
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message = tokenizer.decode(output[:, encoded_input.shape[-1]:][0], skip_special_tokens=True)
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except Exception as e:
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print(e)
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return e
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self.messages.append({"role": "assistant", "content": message})
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if len(self.messages) > self.round * 2 + 1:
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text = self._build_message(self.messages)
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self.messages = []
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self.messages.append({"role": "system", "content": text})
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return message
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def _build_message(self, messages):
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text = ""
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for message in messages:
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text += "Assistant : " + message["content"] + "\n\n"
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return text
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prompt = """你是一个大数据和AI领域的专家,用中文回答大数据和AI的相关问题。你的回答需要满足以下要求:
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1. 你的回答必须是中文
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2. 回答限制在200个字以内
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conv = Conversation(prompt, 3)
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def answer(question, history=[]):
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history.append(question)
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message = conv.ask(question)
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