TradeMate Dev
23a31f7c00
- Add silent WeChat login for MP/browser environments - Fix Python 3.6 compatibility (remove typing.Annotated usage) - Marketing page: tab-based content generation with category support - Translate page: add auto-detect language default - Homepage: add TTS playback, announcement ticker, remove redundant quick-actions - Fix FAB button overlap with custom tabbar on customers/quotation pages - Make openai/anthropic imports lazy for Python 3.6 compat
91 lines
4.2 KiB
Python
91 lines
4.2 KiB
Python
from typing import Dict, Any, Optional
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import json
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from app.ai.base import AIProvider
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SYSTEM_PROMPTS = {
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"translate": "You are a professional translator specialized in foreign trade. "
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"Translate business terms accurately. Return ONLY the translated text.",
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"reply": "You are an experienced foreign trade sales expert. Write professional, "
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"clear business replies. Return ONLY the reply text.",
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"marketing": "You are a creative copywriter for international trade. "
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"Return ONLY the marketing copy, no explanations.",
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"extract": "Extract structured data from text. Return ONLY valid JSON.",
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}
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class SparkProvider(AIProvider):
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def __init__(self, api_key: str, model: str = "astron-code-latest", base_url: str = None):
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from app.config import settings
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try:
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from openai import AsyncOpenAI
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except ImportError:
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raise ImportError("openai>=1.0 is required for SparkProvider")
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self.client = AsyncOpenAI(
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api_key=api_key,
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base_url=base_url or settings.IFLYTEK_API_BASE,
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)
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self.model = model
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self._name = f"spark-{model}"
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async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["translate"]
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if context:
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system += f"\nContext: {context}"
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prompt = f"Translate {f'from {source_lang} ' if source_lang and source_lang != 'auto' else ''}to {target_lang}:\n\n{text}"
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content = await self._call(system, prompt)
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return {"translated_text": content, "provider": self.name}
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async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["reply"] + f"\nTone: {tone}"
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if preference_context:
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system += f"\nUser preference: {preference_context}"
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ctx = ""
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if context:
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ctx = "\n".join(f"{k}: {v}" for k, v in context.items() if v)
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prompt = f"{ctx}\nCustomer inquiry:\n{inquiry}\n\nWrite a reply:"
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content = await self._call(system, prompt)
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return {"reply": content, "provider": self.name}
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async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["marketing"] + f"\nStyle: {style}\nAudience: {target}\nLanguage: {language}"
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if preference_context:
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system += f"\nUser preference: {preference_context}"
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info = json.dumps(product_info, ensure_ascii=False)
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prompt = f"Product:\n{info}\n\nGenerate marketing copy:"
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content = await self._call(system, prompt, max_tokens=1500)
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return {"content": content, "provider": self.name}
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async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["extract"]
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prompt = f"Schema:\n{json.dumps(schema, indent=2)}\n\nText:\n{text}\n\nJSON:"
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content = await self._call(system, prompt, response_format={"type": "json_object"})
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try:
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data = json.loads(content)
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return {"data": data, "confidence": 0.9, "provider": self.name}
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except json.JSONDecodeError:
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return {"data": {}, "confidence": 0.0, "provider": self.name}
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async def _call(self, system: str, prompt: str, max_tokens: int = 1000, response_format: Optional[Dict] = None) -> str:
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kwargs = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": prompt},
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],
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"max_tokens": max_tokens,
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"temperature": 0.7,
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}
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if response_format:
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kwargs["response_format"] = response_format
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resp = await self.client.chat.completions.create(**kwargs)
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return resp.choices[0].message.content
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@property
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def name(self) -> str:
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return self._name
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@property
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def cost_per_1k_tokens(self) -> float:
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return 0.0
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