mirror of https://github.com/THUDM/ChatGLM-6B
Add vision demo
duzx16
parent
a4119fd3ad
commit
b7d5bfe291
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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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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() == "stop":
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break
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if query.strip() == "clear":
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history = []
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os.system(clear_command)
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print(prefix)
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continue
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if __name__ == "__main__":
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main()
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from transformers import AutoModel, AutoTokenizer
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import gradio as gr
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import mdtex2html
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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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"""Override Chatbot.postprocess"""
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def postprocess(self, y):
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if y is None:
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return []
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for i, (message, response) in enumerate(y):
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y[i] = (
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None if message is None else mdtex2html.convert((message)),
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None if response is None else mdtex2html.convert(response),
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)
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return y
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gr.Chatbot.postprocess = postprocess
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def parse_text(text):
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"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
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lines = text.split("\n")
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lines = [line for line in lines if line != ""]
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count = 0
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for i, line in enumerate(lines):
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if "```" in line:
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count += 1
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items = line.split('`')
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if count % 2 == 1:
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lines[i] = f'<pre><code class="language-{items[-1]}">'
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else:
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lines[i] = f'<br></code></pre>'
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else:
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if i > 0:
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if count % 2 == 1:
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line = line.replace("`", "\`")
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line = line.replace("<", "<")
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line = line.replace(">", ">")
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line = line.replace(" ", " ")
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line = line.replace("*", "*")
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line = line.replace("_", "_")
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line = line.replace("-", "-")
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line = line.replace(".", ".")
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line = line.replace("!", "!")
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line = line.replace("(", "(")
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line = line.replace(")", ")")
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line = line.replace("$", "$")
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lines[i] = "<br>"+line
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text = "".join(lines)
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return text
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def predict(input, image_path, chatbot, max_length, top_p, temperature, history):
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if image_path is None:
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return [(input, "图片为空！请重新上传图片并重试。")]
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chatbot.append((parse_text(input), ""))
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for response, history in model.stream_chat(tokenizer, image_path, input, history, max_length=max_length, top_p=top_p,
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temperature=temperature):
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chatbot[-1] = (parse_text(input), parse_text(response))
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yield chatbot, history
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def predict_new_image(image_path, chatbot, max_length, top_p, temperature):
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input, history = "描述这张图片。", []
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chatbot.append((parse_text(input), ""))
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for response, history in model.stream_chat(tokenizer, image_path, input, history, max_length=max_length,
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top_p=top_p,
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temperature=temperature):
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chatbot[-1] = (parse_text(input), parse_text(response))
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yield chatbot, history
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def reset_user_input():
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return gr.update(value='')
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def reset_state():
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return None, [], []
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with gr.Blocks() as demo:
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gr.HTML("""<h1 align="center">VisualGLM</h1>""")
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image_path = gr.Image(type="filepath", label="Image Prompt", value=None)
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chatbot = gr.Chatbot()
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with gr.Row():
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with gr.Column(scale=4):
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with gr.Column(scale=12):
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user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
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container=False)
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with gr.Column(min_width=32, scale=1):
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submitBtn = gr.Button("Submit", variant="primary")
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with gr.Column(scale=1):
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emptyBtn = gr.Button("Clear History")
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max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
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top_p = gr.Slider(0, 1, value=0.4, step=0.01, label="Top P", interactive=True)
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temperature = gr.Slider(0, 1, value=0.8, step=0.01, label="Temperature", interactive=True)
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history = gr.State([])
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submitBtn.click(predict, [user_input, image_path, chatbot, max_length, top_p, temperature, history], [chatbot, history],
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show_progress=True)
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image_path.upload(predict_new_image, [image_path, chatbot, max_length, top_p, temperature], [chatbot, history],
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show_progress=True)
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image_path.clear(reset_state, outputs=[image_path, chatbot, history], show_progress=True)
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submitBtn.click(reset_user_input, [], [user_input])
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emptyBtn.click(reset_state, outputs=[image_path, chatbot, history], show_progress=True)
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demo.queue().launch(share=False, inbrowser=True)
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