From 8856813907d9d62ee2140a9b7271f2039746ce17 Mon Sep 17 00:00:00 2001 From: VSP Date: Sun, 19 May 2024 16:50:31 +0800 Subject: [PATCH] add gradio version --- testGenius.py | 152 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 testGenius.py diff --git a/testGenius.py b/testGenius.py new file mode 100644 index 0000000..33b14eb --- /dev/null +++ b/testGenius.py @@ -0,0 +1,152 @@ +import random +from http import HTTPStatus +from dashscope import Generation +import dashscope +import gradio as gr + +# dashscope.api_key = 'sk-73e9b0452a7e40048495d8ac8ab1afe4' # Vincent's API key +dashscope.api_key = 'sk-83b8ed0ead0849ae9e63a2ae5bdbde0d' # Rayman's API key + + +def respond(prompt, chat_history, instruction, model, if_stream): + """ + 与AI助手进行对话，并返回对话历史。 + + 参数: + - prompt: 用户输入的文本。 + - chat_history: 之前的聊天历史，列表，每个元素是二元组，包括用户输入和AI响应。 + - instruction: 系统指令，作为对话的起始信息。 + + 返回值: + - 生成器，每次产生一个二元组，包括空字符串和更新后的聊天历史。 + """ + # 构建对话消息结构 + messages = [{'role': 'system', + 'content': instruction}, + {'role': 'user', + 'content': prompt} + ] + full_response = "" # 初始化空字符串以聚合响应 + # 调用AI模型生成响应 + # -------非流式输出------- + if if_stream == 'Non-Stream': + response = Generation.call(model=model, + messages=messages, + # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 + seed=1234, + # 将输出设置为"message"格式 + result_format='message', + # 设置输出方式为非流式输出 + stream=False, + # 设置输出方式为非增量式输出 + incremental_output=False) + if not chat_history or chat_history[-1][0] != prompt: + chat_history.append((prompt, "")) + if response.status_code == HTTPStatus.OK: + # 获取响应中的消息内容 + message = response.output.choices[0]['message']['content'] + # 将消息内容添加到聊天历史中 + chat_history.append((prompt, message)) + # 返回更新后的聊天历史 + return "", chat_history + elif if_stream == 'Stream': + # -------流式输出------- + responses = Generation.call(model=model, + messages=messages, + # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 + seed=1234, + # 将输出设置为"message"格式 + result_format='message', + stream=True, # 设置输出方式为流式输出 + incremental_output=True, # 增量式流式输出 + temperature=1.8, + top_p=0.9, + top_k=999) + # 确保聊天历史至少有当前会话的开始 + if not chat_history or chat_history[-1][0] != prompt: + chat_history.append((prompt, "")) + # 循环处理每个流式响应 + for response in responses: + if response.status_code == HTTPStatus.OK: + # 累加每次流式响应的内容 + text = response.output.choices[0]['message']['content'] + full_response += text + # 更新聊天历史的最后一项 + last_turn = list(chat_history[-1]) + last_turn[1] = full_response + chat_history[-1] = tuple(last_turn) + yield "", chat_history # 实时输出当前的聊天历史 + else: + # 如果出错，构建错误信息并更新最后一项 + full_response = 'Request id: {}, Status code: {}, error code: {}, error message: {}'.format( + response.request_id, response.status_code, + response.code, response.message + ) + last_turn = list(chat_history[-1]) + last_turn[1] = full_response + chat_history[-1] = tuple(last_turn) + yield "", chat_history + break # 出现错误时终止循环 + + +def respond_nonStream(prompt, chat_history, instruction, model): + # 构建对话消息结构 + messages = [{'role': 'system', + 'content': instruction}, + {'role': 'user', + 'content': prompt} + ] + full_response = "" # 初始化空字符串以聚合响应 + # 调用AI模型生成响应 + responses = Generation.call(model=model, + messages=messages, + # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 + seed=1234, + # 将输出设置为"message"格式 + result_format='message', + stream=True, # 设置输出方式为流式输出 + incremental_output=True, # 增量式流式输出 + temperature=1.8, + top_p=0.9, + top_k=999) + + + +llm_model_list = ['qwen-turbo','qwen-plus', 'qwen-max'] +init_llm = llm_model_list[0] + +# 创建 Gradio 界面 +with gr.Blocks() as demo: + gr.Markdown( + """ + # AI TestGenius + A simple LLM app for generating test cases from function design. + """) + chatbot = gr.Chatbot() + msg = gr.Textbox(label="Prompt") + with gr.Accordion(label="Advanced options", open=False): + system = gr.Textbox(label="System prompts", lines=2, + value="A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.") + llm = gr.Dropdown( + llm_model_list, + label='Choose LLM Model', + value=init_llm, + interactive=True + ) + if_stream = gr.Dropdown( + ["Stream", "Non-Stream"], + label='Choose Streaming', + value="Stream", + interactive=True + ) + + btn = gr.Button("Submit") + clear = gr.ClearButton(components=[msg, chatbot], value="Clear console") + btn.click(respond, inputs=[msg, chatbot, system, llm, if_stream], outputs=[msg, chatbot]) # click to submit + msg.submit(respond, inputs=[msg, chatbot, system, llm, if_stream], outputs=[msg, chatbot]) # Press enter to submit + + +# 运行界面 +if __name__ == "__main__": + gr.close_all() + demo.launch()