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| import os | |
| import gradio as gr | |
| import spaces | |
| import fitz | |
| import docx | |
| from huggingface_hub import InferenceClient | |
| # 1. 引擎配置：Llama-3-70B-Instruct (全球开源标杆) | |
| model_id = "meta-llama/Meta-Llama-3-70B-Instruct" | |
| hf_token = os.getenv("HF_TOKEN") | |
| client = InferenceClient( | |
| model=model_id, | |
| token=hf_token, | |
| headers={"Authorization": f"Bearer {hf_token}"} if hf_token else None | |
| ) | |
| def extract_text(file_path): | |
| if not file_path: return "" | |
| try: | |
| ext = str(file_path).lower() | |
| if ext.endswith(".pdf"): | |
| doc = fitz.open(file_path) | |
| text = "".join([page.get_text() for page in doc]) | |
| doc.close() | |
| return text[:15000] | |
| elif ext.endswith(".docx"): | |
| doc = docx.Document(file_path) | |
| return "\n".join([p.text for p in doc.paragraphs])[:15000] | |
| elif ext.endswith(".txt"): | |
| with open(file_path, 'r', encoding='utf-8', errors='ignore') as f: | |
| return f.read()[:15000] | |
| return "" | |
| except: | |
| return "" | |
| def chat_fn(message, history): | |
| clean_messages = [] | |
| # 系统提示词：设定为国际化法律与技术专家 | |
| clean_messages.append({"role": "system", "content": "You are a professional global intellectual property and technical expert. Answer accurately and professionally."}) | |
| for turn in history: | |
| clean_messages.append({"role": turn["role"], "content": str(turn["content"])}) | |
| prompt_text = message.get("text", "") | |
| files = message.get("files", []) | |
| if files: | |
| f = files[0] | |
| file_path = f.get("path") if isinstance(f, dict) else f | |
| context = extract_text(file_path) | |
| if context: | |
| prompt_text = f"【Reference Document】:\n{context}\n\n【Instruction】: {prompt_text}" | |
| clean_messages.append({"role": "user", "content": prompt_text}) | |
| response = "" | |
| try: | |
| stream = client.chat_completion(clean_messages, max_tokens=4096, stream=True, temperature=0.5) | |
| for msg in stream: | |
| if hasattr(msg, 'choices') and len(msg.choices) > 0: | |
| delta_content = msg.choices[0].delta.content | |
| if delta_content: | |
| # Llama 的 API 响应通常比较标准，但我们依然保留清洗逻辑 | |
| response += str(delta_content) | |
| yield response | |
| if response: | |
| yield response + "\n\n---\n> **OpenIPOS MegaNode | Meta-Llama-3 全球通用节点**" | |
| except Exception as e: | |
| yield f"⚠️ 算力调度提示: {str(e)}。由于 Llama-3 节点访问量巨大，请刷新重试。" | |
| # 3. 统一品牌装修 | |
| with gr.Blocks(fill_height=True) as demo: | |
| gr.HTML(""" | |
| <div style="display: flex; align-items: center; gap: 20px; padding: 10px;"> | |
| <img src="https://s3.bmp.ovh/2026/03/30/rIAGKg0O.png" style="height: 60px; width: auto;"> | |
| <div> | |
| <h1 style="margin: 0; font-size: 24px;">OpenIPOS MegaNode</h1> | |
| <p style="margin: 0; color: #666;">全球通用人工智能算力出口 (Meta-Llama-3-70B)</p> | |
| </div> | |
| </div> | |
| """) | |
| gr.ChatInterface(chat_fn, multimodal=True) | |
| gr.Markdown("© 2026 OpenIPOS Global Limited. 算力驱动：Together AI / Novita Relay") | |
| demo.launch(theme=gr.themes.Soft()) |