# app.py import os import io import base64 from typing import List, Dict, Any, Optional import httpx import gradio as gr from PIL import Image # ====== 配置(可用环境变量覆写)====== STEPFUN_ENDPOINT = os.getenv("STEPFUN_ENDPOINT", "https://api.stepfun.com/v1") MODEL_NAME = os.getenv("STEPFUN_MODEL", "step-3") # 也可填 step-r1-v-mini REQUEST_TIMEOUT = float(os.getenv("REQUEST_TIMEOUT", "60")) # =================================== def _get_api_key() -> Optional[str]: """ 优先读 OPENAI_API_KEY(与 OpenAI 兼容),否则读 STEPFUN_KEY。 在 HF Space: Settings → Variables and secrets 添加其中一个即可。 """ return os.getenv("OPENAI_API_KEY") or os.getenv("STEPFUN_KEY") def _pil_to_data_url(img: Image.Image, fmt: str = "PNG") -> str: """ PIL -> data:image/...;base64,... 字符串(适配 OpenAI 兼容的 image_url 输入) """ buf = io.BytesIO() img.save(buf, format=fmt) b64 = base64.b64encode(buf.getvalue()).decode("utf-8") mime = "image/png" if fmt.upper() == "PNG" else "image/jpeg" return f"data:{mime};base64,{b64}" def _post_chat(messages: List[Dict[str, Any]], temperature: float = 0.7, max_tokens: Optional[int] = None) -> str: """ 直接请求 StepFun 的 /v1/chat/completions(OpenAI 兼容)。 返回纯字符串,避免 Gradio schema 问题。 """ api_key = _get_api_key() if not api_key: raise RuntimeError( "未检测到 API Key。请到 Space 的 Settings → Variables and secrets 添加:\n" " OPENAI_API_KEY=你的 StepFun API Key (或使用 STEPFUN_KEY)" ) url = f"{STEPFUN_ENDPOINT.rstrip('/')}/chat/completions" headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", } payload: Dict[str, Any] = { "model": MODEL_NAME, "messages": messages, "temperature": temperature, # StepFun 多数情况下无需强制 max_tokens;需要时再放开 } if max_tokens is not None: payload["max_tokens"] = max_tokens with httpx.Client(timeout=REQUEST_TIMEOUT) as client: resp = client.post(url, headers=headers, json=payload) # 让 httpx 抛出更清晰的错误 resp.raise_for_status() data = resp.json() # 标准 OpenAI 兼容返回 try: return str(data["choices"][0]["message"]["content"]) except Exception: # 返回原始数据便于诊断 return f"[WARN] 无法解析返回格式:{data}" def chat_with_step3(image: Optional[Image.Image], question: str, temperature: float) -> str: """ Gradio 的回调函数:接收 PIL 图片与文本,返回字符串。 """ if image is None and not question.strip(): return "请上传一张图片,或至少输入一个问题。" # 构造 messages(支持纯文本、纯图像,或图文混合) content: List[Dict[str, Any]] = [] if image is not None: data_url = _pil_to_data_url(image, fmt="PNG") content.append({"type": "image_url", "image_url": {"url": data_url}}) if question.strip(): content.append({"type": "text", "text": question.strip()}) else: content.append({"type": "text", "text": "请描述这张图片。"}) # 默认问题 messages = [{"role": "user", "content": content}] try: return _post_chat(messages, temperature=temperature) except httpx.HTTPStatusError as e: # 返回服务端 HTTP 错误 + 文本体,便于排查 try: detail = e.response.text except Exception: detail = repr(e) return f"[HTTP {e.response.status_code}] 接口错误:{detail}" except Exception as e: return f"调用失败:{e!r}" # ================ Gradio UI ================ with gr.Blocks(title="Step3 (StepFun API Demo)") as demo: gr.Markdown( """ # Step3 · 图文对话演示(StepFun OpenAI 兼容接口) - 在 **Settings → Variables and secrets** 添加 `OPENAI_API_KEY`(或 `STEPFUN_KEY`)后即可使用 - 后端直连 `https://api.stepfun.com/v1/chat/completions`,不依赖 `openai` SDK """ ) with gr.Row(): image = gr.Image(type="pil", label="上传图片(可选)") question = gr.Textbox(label="问题", placeholder="例如:帮我看看这是什么菜,怎么做?") temperature = gr.Slider(0.0, 1.5, value=0.7, step=0.1, label="Temperature") submit = gr.Button("提交", variant="primary") output = gr.Textbox(label="模型回答", lines=8) submit.click(fn=chat_with_step3, inputs=[image, question, temperature], outputs=[output]) gr.Markdown( """ **提示:** - 如果看到 `调用失败:RuntimeError('未检测到 API Key')`,请检查 Space 的 Secrets - 如需改模型:设置环境变量 `STEPFUN_MODEL`,或在代码顶部修改默认值 """ ) if __name__ == "__main__": # HF Space 环境会自动执行;本地运行也 OK demo.launch()