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import gradio as gr |
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""" |
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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 |
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""" |
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from pathlib import Path |
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from openai import OpenAI |
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class ChatgptAPI: |
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def __init__(self, ): |
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self.client = OpenAI( |
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api_key = "sk-u8YI0ArRHFRRdMEdboouRAXVc3PpR6EhZOfxO4tST5Ua9147", |
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base_url = "https://api.moonshot.cn/v1", |
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) |
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def get_single_round_completion(self, file_path, prompt, conversation): |
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file_object = client.files.create(file=Path(file_path), purpose="file-extract") |
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file_content = client.files.content(file_id=file_object.id).text |
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messages = [ |
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{ |
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"role": "system", |
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"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。", |
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}, |
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{ |
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"role": "system", |
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"content": file_content, |
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}, |
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{"role": "user", "content": prompt}, |
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] |
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completion = self.client.chat.completions.create( |
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model="moonshot-v1-32k", |
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messages=messages, |
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temperature=0.3, |
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) |
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return completion.choices[0].message |
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def get_multi_round_completion(self, prompt, conversation, model='gpt-3.5-turbo'): |
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conversation.append_question(prompt) |
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prompts = conversation.get_prompts() |
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response = openai.ChatCompletion.create( |
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model=model, |
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messages=prompts, |
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temperature=0, |
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max_tokens=2048, |
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top_p=1, |
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) |
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message = response.choices[0].message['content'] |
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conversation.append_answer(message) |
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return message, conversation |
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class Conversation: |
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def __init__(self, system_prompt='', num_of_round = 5): |
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self.num_of_round = num_of_round |
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self.history = [] |
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self.initialized = False |
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self.history.append({"role": "system", "content": system_prompt}) |
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if len(system_prompt) > 0: |
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logger.info(f'Conversation initialized with system prompt: {system_prompt}') |
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self.initialized = True |
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def is_initialized(self): |
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return self.initialized |
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def append_question(self, question): |
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self.history.append({"role": "user", "content": question}) |
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def append_answer(self, answer): |
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self.history.append({"role": "assistant", "content": answer}) |
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if len(self.history) > self.num_of_round * 2: |
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del self.history[1:3] |
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def clear(self): |
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self.history.clear() |
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self.initialized = False |
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def get_prompts(self): |
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return self.history |
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def round_size(self): |
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return 0 if len(self.history) < 2 else len(self.hitory) - 1 |
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def get_history_messages(self): |
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return [(u['content'], b['content']) for u,b in zip(self.history[1::2], self.history[2::2])] |
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chat_api = ChatgptAPI() |
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def predict(system_input, password_input, user_in_file, user_input, conversation): |
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if password_input != '112233': |
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return [(None, "Wrong password!")], conversation, user_input |
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if conversation.is_initialized() == False: |
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conversation = Conversation(system_input, 5) |
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conversation = chat_api.get_single_round_completion(user_in_file, user_input, conversation) |
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return conversation, conversation, None |
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def clear_history(conversation): |
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conversation.clear() |
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return None, conversation |
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with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:600px}") as demo: |
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chatbot = gr.Chatbot(elem_id="chatbot") |
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conversation = gr.State(value=Conversation()) |
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with gr.Row(): |
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system_in_txt = gr.Textbox(lines=1, label="System role content:", placeholder="Enter system role content") |
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password_in_txt = gr.Textbox(lines=1, label="Password:", placeholder="Enter password") |
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with gr.Row(): |
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user_in_file = gr.File(label="Upload File") |
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user_in_txt = gr.Textbox(lines=3, label="User role content:", placeholder="Enter text...").style(container=False) |
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with gr.Row(): |
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submit_button = gr.Button("Submit") |
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reset_button = gr.Button("Reset") |
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submit_button.click(predict, [system_in_txt, password_in_txt, user_in_file, user_in_txt, conversation], [chatbot, conversation, user_in_txt]) |
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reset_button.click(clear_history, [conversation], [chatbot, conversation], queue=False) |
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''' |
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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for val in history: |
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if val[0]: |
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messages.append({"role": "user", "content": val[0]}) |
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if val[1]: |
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messages.append({"role": "assistant", "content": val[1]}) |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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for message in client.chat_completion( |
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messages, |
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max_tokens=max_tokens, |
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stream=True, |
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temperature=temperature, |
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top_p=top_p, |
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): |
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token = message.choices[0].delta.content |
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response += token |
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yield response |
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""" |
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface |
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""" |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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) |
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''' |
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if __name__ == "__main__": |
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demo.launch() |
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