import gradio as gr from openai import OpenAI import os import json from datetime import datetime from zoneinfo import ZoneInfo import uuid from pathlib import Path from huggingface_hub import CommitScheduler openai_api_key = os.getenv('api_key') openai_api_base = os.getenv('url') model_name = "weblab-GENIAC/Tanuki-8x8B-dpo-v1.0" """ 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 """ # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) # Define the file where to save the data. Use UUID to make sure not to overwrite existing data from a previous run. feedback_file = Path("user_feedback/") / f"data_{uuid.uuid4()}.json" feedback_folder = feedback_file.parent # Schedule regular uploads. Remote repo and local folder are created if they don't already exist. scheduler = CommitScheduler( repo_id="team-hatakeyama-phase2/8x8b-server-original-data4", # Replace with your actual repo ID repo_type="dataset", folder_path=feedback_folder, path_in_repo="data", every=60, # Upload every 1 minutes ) def save_or_update_conversation(conversation_id, history, message, response, message_index, liked=None): """ Save or update conversation data in a JSON Lines file. If the entry already exists (same id and message_index), update the 'label' field. Otherwise, append a new entry. """ with scheduler.lock: # Read existing data data = [] if feedback_file.exists(): with feedback_file.open("r") as f: data = [json.loads(line) for line in f if line.strip()] # Find if an entry with the same id and message_index exists #entry_index = next((i for i, entry in enumerate(data) if entry['id'] == conversation_id and entry['message_index'] == message_index), None) #if entry_index is not None: ## # Update existing entry # data[entry_index]['label'] = liked #else: #always append if True: # Append new entry data.append({ #"id": conversation_id, "timestamp": datetime.now(ZoneInfo("Asia/Tokyo")).isoformat(), "history":json.dumps(history,ensure_ascii=False), "prompt": str(message), "completion": str(response), #"message_index": message_index, "label": liked }) # Write updated data back to file with feedback_file.open("w") as f: for entry in data: f.write(json.dumps(entry,ensure_ascii=False) + "\n") def respond( message, history: list[tuple[str, str]], conversation_id, 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 chunk in client.chat.completions.create( model=model_name, messages=messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, stop="### 指示:", ): token=chunk.choices[0].delta.content if token is not None: if response.find("### 指示:")>=0 or token.find("### 指示:")>=0: response=response.split("### 指示:")[0] token=token.split("### 指示:")[0] response=response.replace("### 指示:","") token=token.replace("### 指示:","") break response += token #response=response.replace("### 指示:","") yield response # Save conversation after the full response is generated message_index = len(history) save_or_update_conversation(conversation_id,messages, message, response, message_index) def vote(data: gr.LikeData, history, conversation_id): """ Update user feedback (like/dislike) in the local file. """ message_index = data.index[0] liked = data.liked save_or_update_conversation(conversation_id, history,None, None, message_index, liked) def create_conversation_id(): return str(uuid.uuid4()) """ For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ description = """ ### [Tanuki-8x8B-dpo-v1.0](https://huggingface.co/weblab-GENIAC/Tanuki-8x8B-dpo-v1.0)との会話(期間限定での公開) - 9/16停止中 - 人工知能開発のため､原則として**このChatBotの入出力データは全て著作権フリー(CC0)で公開する**ため､ご注意ください｡著作物､個人情報､機密情報､誹謗中傷などのデータを入力しないでください｡ - **上記の条件に同意する場合のみ**､以下のChatbotを利用してください｡ """ HEADER = description FOOTER = """### 注意 - コンテクスト長が4096までなので､あまり会話が長くなると､エラーで停止します｡ページを再読み込みしてください｡ - v1.10""" def run(): conversation_id = gr.State(create_conversation_id) chatbot = gr.Chatbot( elem_id="chatbot", scale=1, show_copy_button=True, height="70%", layout="panel", ) with gr.Blocks(fill_height=True) as demo: gr.Markdown(HEADER) chat_interface = gr.ChatInterface( fn=respond, stop_btn="Stop Generation", cache_examples=False, multimodal=False, chatbot=chatbot, additional_inputs_accordion=gr.Accordion( label="Parameters", open=False, render=False ), additional_inputs=[ conversation_id, gr.Textbox(value="以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。", label="System message(試験用: 変えると性能が低下する可能性があります。)", render=False,), gr.Slider( minimum=1, maximum=4096, step=1, value=1024, label="Max tokens", visible=True, render=False, ), gr.Slider( minimum=0, maximum=1, step=0.1, value=0.3, label="Temperature", visible=True, render=False, ), gr.Slider( minimum=0, maximum=1, step=0.1, value=1.0, label="Top-p", visible=True, render=False, ), ], analytics_enabled=False, ) chatbot.like(vote, [chatbot, conversation_id], None) gr.Markdown(FOOTER) demo.queue(max_size=256, api_open=True) demo.launch(share=True, quiet=True) if __name__ == "__main__": run()