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import os |
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import platform |
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import signal |
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import sys |
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from transformers import AutoTokenizer, AutoModel |
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import readline |
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tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True) |
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model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda() |
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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, prefix): |
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prompt = prefix |
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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\nChatGLM-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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global stop_stream |
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while True: |
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history = [] |
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prefix = "欢迎使用 VisualGLM-6B 模型,输入图片路径和内容即可进行对话,clear 清空对话历史,stop 终止程序" |
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print(prefix) |
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image_path = input("\n请输入图片路径:") |
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if image_path == "stop": |
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break |
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prefix = prefix + "\n" + image_path |
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query = "描述这张图片。" |
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while True: |
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count = 0 |
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for response, history in model.stream_chat(tokenizer, image_path, query, history=history): |
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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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count += 1 |
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if count % 8 == 0: |
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os.system(clear_command) |
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print(build_prompt(history, prefix), flush=True) |
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signal.signal(signal.SIGINT, signal_handler) |
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os.system(clear_command) |
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print(build_prompt(history, prefix), flush=True) |
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query = input("\n用户:") |
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if query.strip() == "clear": |
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break |
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if query.strip() == "stop": |
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sys.exit(0) |
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if __name__ == "__main__": |
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main() |
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