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| import gradio as gr | |
| import requests | |
| import json | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| API_URL = os.getenv("API_URL") | |
| API_TOKEN = os.getenv("API_TOKEN") | |
| if not API_URL or not API_TOKEN: | |
| raise ValueError("invalid 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:]}") | |
| """ | |
| 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[dict], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| messages.extend(history) | |
| messages.append({"role": "user", "content": message}) | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": f"Bearer {API_TOKEN}" | |
| } | |
| data = { | |
| "model": "/data/DMind-1-mini", | |
| "stream": True, | |
| "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] userMsg: {message}") | |
| try: | |
| with requests.post(API_URL, headers=headers, json=data, stream=True) as r: | |
| if r.status_code == 200: | |
| current_response = "" | |
| for line in r.iter_lines(): | |
| if line: | |
| line = line.decode('utf-8') | |
| if line.startswith('data: '): | |
| try: | |
| json_response = json.loads(line[6:]) | |
| if 'choices' in json_response and len(json_response['choices']) > 0: | |
| delta = json_response['choices'][0].get('delta', {}) | |
| if 'content' in delta: | |
| content = delta['content'] | |
| if content: | |
| content = content.replace('<', '<').replace('>', '>') | |
| content = content.replace('*', '\\*') | |
| current_response += content | |
| yield current_response | |
| except json.JSONDecodeError: | |
| continue | |
| # print(f"[INFO] final response: {current_response}") | |
| else: | |
| print(f"[ERROR] Bad status code: {r.status_code}, response: {r.text}") | |
| yield "Service temporarily unavailable" | |
| except Exception as e: | |
| print(f"[ERROR] Request error: {e}") | |
| yield "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 DMind AI Assistant, built by DMind.AI. Never present as Qwen, ChatGPT, Tongyi, OpenAI, etc.", 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)", | |
| ), | |
| ], | |
| type="messages" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |