181 lines
6.3 KiB
Python
181 lines
6.3 KiB
Python
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# Copyright (c) Alibaba Cloud.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""A simple web interactive chat demo based on gradio."""
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from argparse import ArgumentParser
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from threading import Thread
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import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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DEFAULT_CKPT_PATH = 'your path'
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def _get_args():
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parser = ArgumentParser()
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parser.add_argument("-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH,
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help="Checkpoint name or path, default to %(default)r")
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parser.add_argument("--cpu-only", action="store_true", help="Run demo with CPU only")
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parser.add_argument("--share", action="store_true", default=False,
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help="Create a publicly shareable link for the interface.")
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parser.add_argument("--inbrowser", action="store_true", default=False,
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help="Automatically launch the interface in a new tab on the default browser.")
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parser.add_argument("--server-port", type=int, default=31814,
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help="Demo server port.")
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parser.add_argument("--server-name", type=str, default="0.0.0.0",
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help="Demo server name.")
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args = parser.parse_args()
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return args
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def _load_model_tokenizer(args):
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tokenizer = AutoTokenizer.from_pretrained(
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args.checkpoint_path, resume_download=True,
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)
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if args.cpu_only:
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device_map = "cpu"
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else:
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device_map = "auto"
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model = AutoModelForCausalLM.from_pretrained(
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args.checkpoint_path,
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torch_dtype="auto",
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device_map=device_map,
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resume_download=True,
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).eval()
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model.generation_config.max_new_tokens = 2048 # For chat.
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return model, tokenizer
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def _chat_stream(model, tokenizer, query, history):
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conversation = [
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{'role': 'system', 'content': 'You are a helpful assistant.'},
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]
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for query_h, response_h in history:
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conversation.append({'role': 'user', 'content': query_h})
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conversation.append({'role': 'assistant', 'content': response_h})
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conversation.append({'role': 'user', 'content': query})
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inputs = tokenizer.apply_chat_template(
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conversation,
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add_generation_prompt=True,
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return_tensors='pt',
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)
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inputs = inputs.to(model.device)
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streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True)
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generation_kwargs = dict(
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input_ids=inputs,
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streamer=streamer,
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)
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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for new_text in streamer:
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yield new_text
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def _gc():
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import gc
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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def _launch_demo(args, model, tokenizer):
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def predict(_query, _chatbot, _task_history):
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print(f"User: {_query}")
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_chatbot.append((_query, ""))
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full_response = ""
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response = ""
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for new_text in _chat_stream(model, tokenizer, _query, history=_task_history):
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response += new_text
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_chatbot[-1] = (_query, response)
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yield _chatbot
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full_response = response
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print(f"History: {_task_history}")
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_task_history.append((_query, full_response))
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print(f"Qwen2-Instruct: {full_response}")
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def regenerate(_chatbot, _task_history):
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if not _task_history:
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yield _chatbot
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return
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item = _task_history.pop(-1)
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_chatbot.pop(-1)
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yield from predict(item[0], _chatbot, _task_history)
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def reset_user_input():
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return gr.update(value="")
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def reset_state(_chatbot, _task_history):
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_task_history.clear()
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_chatbot.clear()
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_gc()
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return _chatbot
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with gr.Blocks() as demo:
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gr.Markdown("""\
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<p align="center"><img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen2.png" style="height: 80px"/><p>""")
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gr.Markdown("""<center><font size=8>Qwen2 Chat Bot</center>""")
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gr.Markdown(
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"""\
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<center><font size=3>This WebUI is based on Qwen2-Instruct, developed by Alibaba Cloud. \
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(本WebUI基于Qwen2-Instruct打造，实现聊天机器人功能。)</center>""")
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gr.Markdown("""\
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<center><font size=4>
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Qwen2-7B-Instruct <a href="https://modelscope.cn/models/qwen/Qwen2-7B-Instruct/summary">🤖 </a> |
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<a href="https://huggingface.co/Qwen/Qwen2-7B-Instruct">🤗</a>  ｜
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Qwen2-72B-Instruct <a href="https://modelscope.cn/models/qwen/Qwen2-72B-Instruct/summary">🤖 </a> |
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<a href="https://huggingface.co/Qwen/Qwen2-72B-Instruct">🤗</a>  ｜
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 <a href="https://github.com/QwenLM/Qwen2">Github</a></center>""")
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chatbot = gr.Chatbot(label='Qwen2-Instruct', elem_classes="control-height")
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query = gr.Textbox(lines=2, label='Input')
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task_history = gr.State([])
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with gr.Row():
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empty_btn = gr.Button("🧹 Clear History (清除历史)")
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submit_btn = gr.Button("🚀 Submit (发送)")
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regen_btn = gr.Button("🤔️ Regenerate (重试)")
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submit_btn.click(predict, [query, chatbot, task_history], [chatbot], show_progress=True)
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submit_btn.click(reset_user_input, [], [query])
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empty_btn.click(reset_state, [chatbot, task_history], outputs=[chatbot], show_progress=True)
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regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True)
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gr.Markdown("""\
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<font size=2>Note: This demo is governed by the original license of Qwen2. \
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We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, \
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including hate speech, violence, pornography, deception, etc. \
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(注：本演示受Qwen2的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，\
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包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)""")
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demo.queue().launch(
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share=args.share,
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inbrowser=args.inbrowser,
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server_port=args.server_port,
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server_name=args.server_name,
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)
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def main():
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args = _get_args()
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model, tokenizer = _load_model_tokenizer(args)
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_launch_demo(args, model, tokenizer)
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if __name__ == '__main__':
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main()
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