import random from http import HTTPStatus from dashscope import Generation import dashscope import gradio as gr # dashscope.api_key = # Vincent's API key dashscope.api_key = # Rayman's API key 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}] print(f"Messages: {messages}") # 初始化空字符串以聚合响应 full_response = "" # 调用AI模型生成响应 try: response = Generation.call(model=model, messages=messages, seed=1234, result_format='message', stream=False, incremental_output=False) print(f"Response: {response}") if not chat_history or chat_history[-1][0] != prompt: chat_history.append([prompt, ""]) print(f"old chat history: {chat_history}") if response.status_code == HTTPStatus.OK: # 获取响应中的消息内容 message = response.output.choices[0]['message']['content'] print(f"Generated message: {message}") # 更新聊天历史记录中的最后一条记录 chat_history[-1] = [prompt, message] print(f"Updated chat_history: {chat_history}") return "", chat_history else: print(f"Error: Received response status {response.status_code}") return "Error: Could not generate response", chat_history except Exception as e: print(f"Exception occurred: {e}") return f"Exception occurred: {e}", chat_history def respond(prompt, chat_history, instruction, model, if_stream='Stream'): if if_stream == 'Stream': messages = [{'role': 'system', 'content': instruction}, {'role': 'user', 'content': prompt} ] full_response = "" # 初始化空字符串以聚合响应 # -------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 elif if_stream == 'Non-Stream': # 构建对话消息结构 messages = [{'role': 'system', 'content': instruction}, {'role': 'user', 'content': prompt}] print(f"Messages: {messages}") # 调用AI模型生成响应 try: response = Generation.call(model=model, messages=messages, seed=1234, result_format='message', stream=False, incremental_output=False) print(f"Response: {response}") if not chat_history or chat_history[-1][0] != prompt: chat_history.append([prompt, ""]) print(f"old chat history: {chat_history}") if response.status_code == HTTPStatus.OK: # 获取响应中的消息内容 message = response.output.choices[0]['message']['content'] print(f"Generated message: {message}") # 更新聊天历史记录中的最后一条记录 chat_history[-1] = [prompt, message] print(f"Updated chat_history: {chat_history}") return "", chat_history else: print(f"Error: Received response status {response.status_code}") return "Error: Could not generate response", chat_history except Exception as e: print(f"Exception occurred: {e}") return f"Exception occurred: {e}", chat_history 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. """) history = [["Hello", "Hello, how can I help you?"]] chatbot = gr.Chatbot(history) 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.") # 选择模型 llm = gr.Dropdown( llm_model_list, label='Choose LLM Model', value=init_llm, interactive=True ) # 选择是否流式输出 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()