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cli_demo_hf.py
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cli_demo_hf.py
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import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import torch
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
os_name = platform.system()
clear_command = 'cls' if os_name == 'Windows' else 'clear'
stop_stream = False
def build_prompt(history, prefix):
prompt = prefix
for query, response in history:
prompt += f"\n\n用户:{query}"
prompt += f"\n\nVisualGLM-6B:{response}"
return prompt
def signal_handler(signal, frame):
global stop_stream
stop_stream = True
def main():
global stop_stream
while True:
history = []
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"
print(prefix)
image_path = input("\n请输入图片路径:")
if image_path == "stop":
break
prefix = prefix + "\n" + image_path
query = "描述这张图片。"
while True:
count = 0
with torch.no_grad():
for response, history in model.stream_chat(tokenizer, image_path, query, history=history):
if stop_stream:
stop_stream = False
break
else:
count += 1
if count % 8 == 0:
os.system(clear_command)
print(build_prompt(history, prefix), flush=True)
signal.signal(signal.SIGINT, signal_handler)
os.system(clear_command)
print(build_prompt(history, prefix), flush=True)
query = input("\n用户:")
if query.strip() == "clear":
break
if query.strip() == "stop":
stop_stream = True
exit(0)
# if query.strip() == "clear":
# history = []
# os.system(clear_command)
# print(prefix)
# continue
if __name__ == "__main__":
main()