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| SUPPORTED_MODELS = ["meta-llama/Llama-3.3-70B-Instruct", "openai/gpt-oss-120b:groq"] | |
| def generate(system_prompt: str, user_message: str, api_key: str, | |
| model: str = None, temperature: float = 0.0, | |
| max_tokens: int = 8192) -> str: | |
| from openai import OpenAI | |
| model = model or SUPPORTED_MODELS[0] | |
| client = OpenAI(base_url="https://router.huggingface.co/v1", | |
| api_key=api_key, | |
| default_headers={"X-HF-Bill-To": "cgiar"}) | |
| try: | |
| messages = [ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_message}, | |
| ] | |
| response = client.chat.completions.create( | |
| model=model, | |
| messages=messages, | |
| temperature=temperature, | |
| max_completion_tokens=max_tokens, | |
| ) | |
| # Check for blocked responses | |
| if not response.choices: | |
| return "" | |
| return response.choices[0].message.content or "" | |
| except ValueError as e: | |
| # Safety filter block — return empty so verifier treats it as generation failure | |
| return "" | |
| except Exception as e: | |
| error_type = type(e).__name__ | |
| error_msg = str(e) | |
| if "auth" in error_type.lower() or "authentication" in error_type.lower() or "api_key" in error_msg.lower(): | |
| raise RuntimeError(f"Hugging Face authentication failed. Check your API key.") from e | |
| elif "rate" in error_type.lower() or "429" in error_msg: | |
| raise RuntimeError(f"Hugging Face rate limit exceeded. Wait a moment and try again.") from e | |
| elif "model" in error_msg.lower() or "not found" in error_msg.lower(): | |
| raise RuntimeError(f"Hugging Face model error: {error_msg}") from e | |
| else: | |
| raise RuntimeError(f"Hugging Face API error ({error_type}): {error_msg}") from e | |
| def list_models(api_key: str) -> list[str]: | |
| return list(SUPPORTED_MODELS) |