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| import gradio as gr | |
| import requests | |
| import json | |
| import os | |
| from dotenv import load_dotenv | |
| # 加载.env文件中的环境变量 | |
| load_dotenv() | |
| # 从环境变量中读取配置 | |
| API_URL = os.getenv("API_URL") | |
| API_TOKEN = os.getenv("API_TOKEN") | |
| # 验证必要的环境变量 | |
| if not API_URL or not API_TOKEN: | |
| raise ValueError("make sure API_URL & API_TOKEN") | |
| print(f"[INFO] starting:") | |
| print(f"[INFO] API_URL: {API_URL[:6]}...{API_URL[-12:]}") | |
| print(f"[INFO] API_TOKEN: {API_TOKEN[:10]}...{API_TOKEN[-10:]}") # 只显示token的前10位和后10位 | |
| """ | |
| 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 | |
| """ | |
| 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}) | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": f"Bearer {API_TOKEN}" | |
| } | |
| data = { | |
| "model": "/data/DMind-1-mini", | |
| "stream": False, # 改为非流式模式 | |
| "messages": messages, | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "top_k": 20, | |
| "min_p": 0.1, | |
| "max_tokens": 16384 | |
| } | |
| print(f"[INFO] process user msg...") | |
| print(f"[INFO] sysMsg: {system_message}") | |
| print(f"[INFO] userMsg: {message}") | |
| print(f"[INFO] modelParam: temperature={temperature}, top_p={top_p}") | |
| print(f"[INFO] reqData: {data}") | |
| try: | |
| with requests.post(API_URL, headers=headers, json=data) as r: | |
| if r.status_code == 200: | |
| json_response = r.json() | |
| if 'choices' in json_response and len(json_response['choices']) > 0: | |
| content = json_response['choices'][0].get('message', {}).get('content', '') | |
| if content: | |
| # if '<think>' in content and '</think>' in content: | |
| # content = content.split('</think>')[-1].strip() | |
| print(f"[INFO] response: {content}") | |
| return content | |
| return "Service temporarily unavailable" | |
| except Exception as e: | |
| print(f"[ERROR] Request error: {e}") | |
| return "Service error occurred" | |
| """ | |
| 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=32768, value=16384, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.6, 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(share=True) | |