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import os
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import platform
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import signal
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from transformers import AutoTokenizer, AutoModel
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import readline
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from utils import load_model_on_gpus
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
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# 多显卡支持，使用下面三行代替上面两行，将num_gpus改为你实际的显卡数量
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# model_path = "THUDM/chatglm2-6b"
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# tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# model = load_model_on_gpus(model_path, num_gpus=2)
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model = model.eval()
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os_name = platform.system()
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clear_command = 'cls' if os_name == 'Windows' else 'clear'
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stop_stream = False
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def build_prompt(history):
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prompt = "欢迎使用 ChatGLM2-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序"
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for query, response in history:
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prompt += f"\n\n用户：{query}"
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prompt += f"\n\nChatGLM2-6B：{response}"
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return prompt
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def signal_handler(signal, frame):
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global stop_stream
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stop_stream = True
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def main():
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past_key_values, history = None, []
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global stop_stream
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print("欢迎使用 ChatGLM2-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
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while True:
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query = input("\n用户：")
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if query.strip() == "stop":
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break
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if query.strip() == "clear":
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past_key_values, history = None, []
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os.system(clear_command)
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print("欢迎使用 ChatGLM2-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
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continue
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print("\nChatGLM：", end="")
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current_length = 0
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for response, history, past_key_values in model.stream_chat(tokenizer, query, history=history,
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past_key_values=past_key_values,
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return_past_key_values=True):
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if stop_stream:
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stop_stream = False
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break
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else:
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print(response[current_length:], end="", flush=True)
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current_length = len(response)
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print("")
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if __name__ == "__main__":
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main()
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