import gradio as gr #from huggingface_hub import InferenceClient """ For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference """ #from new_chat import Conversation, ChatgptAPI from pathlib import Path from openai import OpenAI class ChatgptAPI: def __init__(self, ): self.client = OpenAI( api_key = "sk-u8YI0ArRHFRRdMEdboouRAXVc3PpR6EhZOfxO4tST5Ua9147", base_url = "https://api.moonshot.cn/v1", ) def get_single_round_completion(self, file_path, prompt, conversation): file_object = client.files.create(file=Path(file_path), purpose="file-extract") file_content = client.files.content(file_id=file_object.id).text messages = [ { "role": "system", "content": "你是 Kimi，由 Moonshot AI 提供的人工智能助手，你更擅长中文和英文的对话。你会为用户提供安全，有帮助，准确的回答。同时，你会拒绝一切涉及恐怖主义，种族歧视，黄色暴力等问题的回答。Moonshot AI 为专有名词，不可翻译成其他语言。", }, { "role": "system", "content": file_content, }, {"role": "user", "content": prompt}, ] completion = self.client.chat.completions.create( model="moonshot-v1-32k", messages=messages, temperature=0.3, ) return completion.choices[0].message def get_multi_round_completion(self, prompt, conversation, model='gpt-3.5-turbo'): conversation.append_question(prompt) prompts = conversation.get_prompts() response = openai.ChatCompletion.create( model=model, messages=prompts, temperature=0, max_tokens=2048, top_p=1, ) message = response.choices[0].message['content'] conversation.append_answer(message) return message, conversation class Conversation: def __init__(self, system_prompt='', num_of_round = 5): self.num_of_round = num_of_round self.history = [] self.initialized = False self.history.append({"role": "system", "content": system_prompt}) if len(system_prompt) > 0: logger.info(f'Conversation initialized with system prompt: {system_prompt}') self.initialized = True def is_initialized(self): return self.initialized def append_question(self, question): self.history.append({"role": "user", "content": question}) def append_answer(self, answer): self.history.append({"role": "assistant", "content": answer}) if len(self.history) > self.num_of_round * 2: del self.history[1:3] def clear(self): self.history.clear() self.initialized = False def get_prompts(self): return self.history def round_size(self): return 0 if len(self.history) < 2 else len(self.hitory) - 1 def get_history_messages(self): return [(u['content'], b['content']) for u,b in zip(self.history[1::2], self.history[2::2])] chat_api = ChatgptAPI() def predict(system_input, password_input, user_in_file, user_input, conversation): if password_input != '112233': return [(None, "Wrong password!")], conversation, user_input if conversation.is_initialized() == False: conversation = Conversation(system_input, 5) conversation = chat_api.get_single_round_completion(user_in_file, user_input, conversation) return conversation, conversation, None #_, conversation = chat_api.get_multi_round_completion(user_input, conversation) #return conversation.get_history_messages(), conversation, None def clear_history(conversation): conversation.clear() return None, conversation with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:600px}") as demo: chatbot = gr.Chatbot(elem_id="chatbot") conversation = gr.State(value=Conversation()) with gr.Row(): system_in_txt = gr.Textbox(lines=1, label="System role content:", placeholder="Enter system role content") password_in_txt = gr.Textbox(lines=1, label="Password:", placeholder="Enter password") with gr.Row(): user_in_file = gr.File(label="Upload File") user_in_txt = gr.Textbox(lines=3, label="User role content:", placeholder="Enter text...").style(container=False) with gr.Row(): submit_button = gr.Button("Submit") reset_button = gr.Button("Reset") submit_button.click(predict, [system_in_txt, password_in_txt, user_in_file, user_in_txt, conversation], [chatbot, conversation, user_in_txt]) reset_button.click(clear_history, [conversation], [chatbot, conversation], queue=False) ''' client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": message}) response = "" for message in client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): token = message.choices[0].delta.content response += token yield response """ For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ demo = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox(value="You are a friendly Chatbot.", label="System message"), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)", ), ], ) ''' if __name__ == "__main__": demo.launch()