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@@ -1,9 +1,8 @@
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"""
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-NanoBanana Tool - 图像特征提取与图像生成
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+NanoBanana Tool - 图像生成
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-该工具可以提取图片中的特征，也可以根据描述生成图片。
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-支持通过 OpenRouter 调用多模态模型，提取结构化的图像特征并保存为 JSON，
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-或基于输入图像生成新的图像。
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+通用图像生成工具，可以接受自然语言描述和/或图像输入，生成新的图像。
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+支持通过 OpenRouter 调用 Gemini 2.5 Flash Image 模型。
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"""
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import base64
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@@ -22,23 +21,10 @@ from agent.tools import tool, ToolResult
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OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
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DEFAULT_TIMEOUT = 120.0
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-DEFAULT_EXTRACTION_PROMPT = (
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- "请从这张图像中提取跨场景相对稳定、可复用的视觉不变特征。"
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- "输出严格 JSON，字段包含：identity_features、pose_features、appearance_features、"
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- "material_features、style_features、uncertainty、notes。"
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- "每个字段给出简洁要点，避免臆测。"
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-)
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-
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-DEFAULT_IMAGE_PROMPT = (
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- "基于输入图像生成一张保留主体身份与关键视觉特征的新图像。"
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- "保持人物核心特征一致，同时提升清晰度与可用性。"
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-)
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+DEFAULT_IMAGE_PROMPT = "根据输入生成图像。"
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DEFAULT_IMAGE_MODEL_CANDIDATES = [
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"google/gemini-2.5-flash-image",
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- # "google/gemini-3-pro-image-preview",
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- # "black-forest-labs/flux.2-flex",
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- # "black-forest-labs/flux.2-pro",
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]
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@@ -214,84 +200,76 @@ def _normalize_model_id(model_id: str) -> str:
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return m
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-@tool(description="可以提取图片中的特征，也可以根据描述生成图片")
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+@tool(description="通用图像生成工具，可以接受自然语言描述和/或图像输入，生成新的图像")
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async def nanobanana(
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image_path: str = "",
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image_paths: Optional[List[str]] = None,
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- output_file: Optional[str] = None,
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prompt: Optional[str] = None,
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model: Optional[str] = None,
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max_tokens: int = 1200,
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- generate_image: bool = False,
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image_output_path: Optional[str] = None,
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) -> ToolResult:
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"""
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- 可以提取图片中的特征，也可以根据描述生成图片。
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+ 通用图像生成工具，可以接受自然语言描述和/或图像输入，生成新的图像。
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Args:
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image_path: 输入图片路径（单图模式，可选）
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- image_paths: 输入图片路径列表（多图整体模式，可选）
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- output_file: 输出 JSON 文件路径（可选，用于特征提取模式）
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- prompt: 自定义提取指令或生成描述（可选）
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- model: OpenRouter 模型名（可选，默认读取 NANOBANANA_MODEL 或使用 Gemini 视觉模型）
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+ image_paths: 输入图片路径列表（多图模式，可选）
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+ prompt: 自定义生成描述（可选，默认使用通用prompt）
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+ model: OpenRouter 模型名（可选，默认使用 gemini-2.5-flash-image）
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max_tokens: 最大输出 token
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- generate_image: 是否生成图片（False=提取特征，True=生成图片）
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- image_output_path: 生成图片保存路径（generate_image=True 时可选）
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+ image_output_path: 生成图片保存路径（可选）
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Returns:
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- ToolResult: 包含结构化特征和输出文件路径，或生成的图片路径
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+ ToolResult: 包含生成的图片路径
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"""
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raw_paths: List[str] = []
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if image_paths:
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raw_paths.extend(image_paths)
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if image_path:
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raw_paths.append(image_path)
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- if not raw_paths:
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- return ToolResult(
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- title="NanoBanana 提取失败",
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- output="",
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- error="未提供输入图片，请传入 image_path 或 image_paths",
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- )
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- # 去重并检查路径
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- unique_raw: List[str] = []
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- seen = set()
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- for p in raw_paths:
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- if p and p not in seen:
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- unique_raw.append(p)
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- seen.add(p)
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-
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- input_paths: List[Path] = [Path(p) for p in unique_raw]
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- invalid = [str(p) for p in input_paths if (not p.exists() or not p.is_file())]
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- if invalid:
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- return ToolResult(
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- title="NanoBanana 提取失败",
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- output="",
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- error=f"以下图片不存在或不可读: {invalid}",
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- )
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+ # 图像输入是可选的，但如果提供了就需要验证
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+ input_paths: List[Path] = []
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+ if raw_paths:
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+ # 去重并检查路径
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+ unique_raw: List[str] = []
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+ seen = set()
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+ for p in raw_paths:
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+ if p and p not in seen:
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+ unique_raw.append(p)
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+ seen.add(p)
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+
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+ input_paths = [Path(p) for p in unique_raw]
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+ invalid = [str(p) for p in input_paths if (not p.exists() or not p.is_file())]
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+ if invalid:
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+ return ToolResult(
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+ title="NanoBanana 生成失败",
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+ output="",
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+ error=f"以下图片不存在或不可读: {invalid}",
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+ )
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api_key = _resolve_api_key()
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if not api_key:
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return ToolResult(
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- title="NanoBanana 提取失败",
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+ title="NanoBanana 生成失败",
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output="",
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error="未找到 OpenRouter API Key，请设置 OPENROUTER_API_KEY 或 OPEN_ROUTER_API_KEY",
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)
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- if generate_image:
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- user_prompt = prompt or DEFAULT_IMAGE_PROMPT
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- else:
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- chosen_model = model or os.getenv("NANOBANANA_MODEL") or "google/gemini-2.5-flash"
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- user_prompt = prompt or DEFAULT_EXTRACTION_PROMPT
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+ user_prompt = prompt or DEFAULT_IMAGE_PROMPT
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- try:
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- image_data_urls = [_image_to_data_url(p) for p in input_paths]
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- except Exception as e:
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- return ToolResult(
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- title="NanoBanana 提取失败",
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- output="",
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- error=f"图片编码失败: {e}",
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- )
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+ # 编码图像（如果有）
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+ image_data_urls = []
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+ if input_paths:
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+ try:
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+ image_data_urls = [_image_to_data_url(p) for p in input_paths]
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+ except Exception as e:
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+ return ToolResult(
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+ title="NanoBanana 生成失败",
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+ output="",
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+ error=f"图片编码失败: {e}",
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+ )
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user_content: List[Dict[str, Any]] = [{"type": "text", "text": user_prompt}]
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for u in image_data_urls:
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@@ -301,11 +279,7 @@ async def nanobanana(
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"messages": [
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{
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"role": "system",
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- "content": (
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- "你是视觉助手。"
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- "当任务为特征提取时输出 JSON 对象，不要输出 markdown。"
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- "当任务为图像生成时请返回图像。"
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- ),
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+ "content": "你是图像生成助手。请根据用户的描述和/或输入图像生成新的图像。",
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},
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{
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"role": "user",
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@@ -314,9 +288,8 @@ async def nanobanana(
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],
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"temperature": 0.2,
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"max_tokens": max_tokens,
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+ "modalities": ["image", "text"],
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}
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- if generate_image:
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- payload["modalities"] = ["image", "text"]
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headers = {
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"Authorization": f"Bearer {api_key}",
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@@ -327,33 +300,28 @@ async def nanobanana(
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endpoint = f"{OPENROUTER_BASE_URL}/chat/completions"
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- # 图像生成模式：自动尝试多个可用模型，减少 404/invalid model 影响
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- if generate_image:
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- candidates: List[str] = []
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- if model:
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- candidates.append(_normalize_model_id(model))
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- if env_model := os.getenv("NANOBANANA_IMAGE_MODEL"):
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- candidates.append(_normalize_model_id(env_model))
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- candidates.extend([_normalize_model_id(x) for x in DEFAULT_IMAGE_MODEL_CANDIDATES])
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- # 去重并保持顺序
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- dedup: List[str] = []
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- seen = set()
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- for m in candidates:
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- if m and m not in seen:
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- dedup.append(m)
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- seen.add(m)
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- candidates = dedup
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- else:
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- candidates = [chosen_model]
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+ # 自动尝试多个可用模型，减少 404/invalid model 影响
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+ candidates: List[str] = []
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+ if model:
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+ candidates.append(_normalize_model_id(model))
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+ if env_model := os.getenv("NANOBANANA_IMAGE_MODEL"):
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+ candidates.append(_normalize_model_id(env_model))
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+ candidates.extend([_normalize_model_id(x) for x in DEFAULT_IMAGE_MODEL_CANDIDATES])
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+ # 去重并保持顺序
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+ dedup: List[str] = []
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+ seen = set()
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+ for m in candidates:
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+ if m and m not in seen:
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+ dedup.append(m)
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+ seen.add(m)
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+ candidates = dedup
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data: Optional[Dict[str, Any]] = None
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used_model: Optional[str] = None
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errors: List[Dict[str, Any]] = []
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for cand in candidates:
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- modality_attempts: List[Optional[List[str]]] = [None]
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- if generate_image:
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- modality_attempts = [["image", "text"], ["image"], None]
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+ modality_attempts: List[Optional[List[str]]] = [["image", "text"], ["image"], None]
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for mods in modality_attempts:
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trial_payload = dict(payload)
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@@ -392,9 +360,8 @@ async def nanobanana(
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break
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if data is None:
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- title = "NanoBanana 生成失败" if generate_image else "NanoBanana 提取失败"
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return ToolResult(
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- title=title,
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+ title="NanoBanana 生成失败",
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output=json.dumps({"attempted_models": candidates, "errors": errors}, ensure_ascii=False, indent=2),
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long_term_memory="All candidate models failed for this request",
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metadata={"attempted_models": candidates, "errors": errors},
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@@ -405,168 +372,115 @@ async def nanobanana(
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choices = data.get("choices") or []
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message = choices[0].get("message", {}) if choices else {}
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- # 图像生成分支
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- if generate_image:
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- refs = _extract_image_refs(choices[0] if choices else {}, message)
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- if not refs:
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- content = message.get("content")
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- preview = ""
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- if isinstance(content, str):
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- preview = content[:500]
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- elif isinstance(content, list):
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- preview = json.dumps(content[:3], ensure_ascii=False)[:500]
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+ # 提取生成的图像
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+ refs = _extract_image_refs(choices[0] if choices else {}, message)
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+ if not refs:
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+ content = message.get("content")
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+ preview = ""
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+ if isinstance(content, str):
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+ preview = content[:500]
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+ elif isinstance(content, list):
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+ preview = json.dumps(content[:3], ensure_ascii=False)[:500]
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- return ToolResult(
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- title="NanoBanana 生成失败",
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- output=json.dumps(data, ensure_ascii=False, indent=2),
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- error="模型未返回可解析图片（未在 message.images/choice.images/content 中发现图片）",
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- metadata={
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- "model": chosen_model,
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- "choice_keys": list((choices[0] if choices else {}).keys()),
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- "message_keys": list(message.keys()) if isinstance(message, dict) else [],
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- "content_preview": preview,
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- },
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- )
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+ return ToolResult(
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+ title="NanoBanana 生成失败",
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+ output=json.dumps(data, ensure_ascii=False, indent=2),
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+ error="模型未返回可解析图片（未在 message.images/choice.images/content 中发现图片）",
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+ metadata={
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+ "model": chosen_model,
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+ "choice_keys": list((choices[0] if choices else {}).keys()),
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+ "message_keys": list(message.keys()) if isinstance(message, dict) else [],
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+ "content_preview": preview,
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+ },
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+ )
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- output_paths: List[str] = []
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- if image_output_path:
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- base_path = Path(image_output_path)
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+ output_paths: List[str] = []
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+ if image_output_path:
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+ base_path = Path(image_output_path)
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+ else:
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+ if len(input_paths) > 1:
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+ base_path = input_paths[0].parent / "set_generated.png"
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else:
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- if len(input_paths) > 1:
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- base_path = input_paths[0].parent / "set_generated.png"
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- else:
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- base_path = input_paths[0].parent / f"{input_paths[0].stem}_generated.png"
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- base_path.parent.mkdir(parents=True, exist_ok=True)
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-
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- for idx, ref in enumerate(refs):
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- kind = ref.get("kind", "")
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- mime_type = "image/png"
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- raw_bytes: Optional[bytes] = None
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-
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- if kind == "data_url":
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- m = re.match(r"^data:([^;]+);base64,(.+)$", ref.get("value", ""), flags=re.DOTALL)
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- if not m:
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- continue
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- mime_type = m.group(1)
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- raw_bytes = base64.b64decode(m.group(2))
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- elif kind == "base64":
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- mime_type = ref.get("mime_type", "image/png")
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- raw_bytes = base64.b64decode(ref.get("value", ""))
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- elif kind == "url":
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- url = ref.get("value", "")
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- try:
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- with httpx.Client(timeout=DEFAULT_TIMEOUT) as client:
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- r = client.get(url)
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- r.raise_for_status()
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- raw_bytes = r.content
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- mime_type = r.headers.get("content-type", "image/png").split(";")[0]
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- except Exception:
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- continue
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- else:
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- continue
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+ base_path = input_paths[0].parent / f"{input_paths[0].stem}_generated.png"
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+ base_path.parent.mkdir(parents=True, exist_ok=True)
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- if not raw_bytes:
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- continue
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+ for idx, ref in enumerate(refs):
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+ kind = ref.get("kind", "")
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+ mime_type = "image/png"
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+ raw_bytes: Optional[bytes] = None
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- ext = _mime_to_ext(mime_type)
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- if len(refs) == 1:
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- target = base_path
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- if target.suffix.lower() not in [".png", ".jpg", ".jpeg", ".webp"]:
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- target = target.with_suffix(ext)
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- else:
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- stem = base_path.stem
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- target = base_path.with_name(f"{stem}_{idx+1}{ext}")
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+ if kind == "data_url":
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+ m = re.match(r"^data:([^;]+);base64,(.+)$", ref.get("value", ""), flags=re.DOTALL)
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+ if not m:
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+ continue
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+ mime_type = m.group(1)
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+ raw_bytes = base64.b64decode(m.group(2))
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+ elif kind == "base64":
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+ mime_type = ref.get("mime_type", "image/png")
|
|
|
+ raw_bytes = base64.b64decode(ref.get("value", ""))
|
|
|
+ elif kind == "url":
|
|
|
+ url = ref.get("value", "")
|
|
|
try:
|
|
|
- target.write_bytes(raw_bytes)
|
|
|
- output_paths.append(str(target))
|
|
|
- except Exception as e:
|
|
|
- return ToolResult(
|
|
|
- title="NanoBanana 生成失败",
|
|
|
- output="",
|
|
|
- error=f"写入生成图片失败: {e}",
|
|
|
- metadata={"model": chosen_model},
|
|
|
- )
|
|
|
-
|
|
|
- if not output_paths:
|
|
|
+ with httpx.Client(timeout=DEFAULT_TIMEOUT) as client:
|
|
|
+ r = client.get(url)
|
|
|
+ r.raise_for_status()
|
|
|
+ raw_bytes = r.content
|
|
|
+ mime_type = r.headers.get("content-type", "image/png").split(";")[0]
|
|
|
+ except Exception:
|
|
|
+ continue
|
|
|
+ else:
|
|
|
+ continue
|
|
|
+
|
|
|
+ if not raw_bytes:
|
|
|
+ continue
|
|
|
+
|
|
|
+ ext = _mime_to_ext(mime_type)
|
|
|
+ if len(refs) == 1:
|
|
|
+ target = base_path
|
|
|
+ if target.suffix.lower() not in [".png", ".jpg", ".jpeg", ".webp"]:
|
|
|
+ target = target.with_suffix(ext)
|
|
|
+ else:
|
|
|
+ stem = base_path.stem
|
|
|
+ target = base_path.with_name(f"{stem}_{idx+1}{ext}")
|
|
|
+ try:
|
|
|
+ target.write_bytes(raw_bytes)
|
|
|
+ output_paths.append(str(target))
|
|
|
+ except Exception as e:
|
|
|
return ToolResult(
|
|
|
title="NanoBanana 生成失败",
|
|
|
- output=json.dumps(data, ensure_ascii=False, indent=2),
|
|
|
- error="检测到图片引用但写入失败（可能是无效 base64 或 URL 不可访问）",
|
|
|
- metadata={"model": chosen_model, "ref_count": len(refs)},
|
|
|
+ output="",
|
|
|
+ error=f"写入生成图片失败: {e}",
|
|
|
+ metadata={"model": chosen_model},
|
|
|
)
|
|
|
|
|
|
- usage = data.get("usage", {})
|
|
|
- prompt_tokens = usage.get("prompt_tokens") or usage.get("input_tokens", 0)
|
|
|
- completion_tokens = usage.get("completion_tokens") or usage.get("output_tokens", 0)
|
|
|
- summary = {
|
|
|
- "model": chosen_model,
|
|
|
- "input_images": [str(p) for p in input_paths],
|
|
|
- "input_count": len(input_paths),
|
|
|
- "generated_images": output_paths,
|
|
|
- "prompt_tokens": prompt_tokens,
|
|
|
- "completion_tokens": completion_tokens,
|
|
|
- }
|
|
|
- return ToolResult(
|
|
|
- title="NanoBanana 图片生成完成",
|
|
|
- output=json.dumps({"summary": summary}, ensure_ascii=False, indent=2),
|
|
|
- long_term_memory=f"Generated {len(output_paths)} image(s) from {len(input_paths)} input image(s) using {chosen_model}",
|
|
|
- attachments=output_paths,
|
|
|
- metadata=summary,
|
|
|
- )
|
|
|
-
|
|
|
- content = message.get("content") or ""
|
|
|
- if not content:
|
|
|
+ if not output_paths:
|
|
|
return ToolResult(
|
|
|
- title="NanoBanana 提取失败",
|
|
|
+ title="NanoBanana 生成失败",
|
|
|
output=json.dumps(data, ensure_ascii=False, indent=2),
|
|
|
- error="模型未返回内容",
|
|
|
+ error="检测到图片引用但写入失败（可能是无效 base64 或 URL 不可访问）",
|
|
|
+ metadata={"model": chosen_model, "ref_count": len(refs)},
|
|
|
)
|
|
|
|
|
|
- try:
|
|
|
- parsed = _safe_json_parse(content)
|
|
|
- except Exception as e:
|
|
|
- return ToolResult(
|
|
|
- title="NanoBanana 提取失败",
|
|
|
- output=content,
|
|
|
- error=f"模型返回非 JSON 内容，解析失败: {e}",
|
|
|
- metadata={"model": chosen_model},
|
|
|
- )
|
|
|
-
|
|
|
- if output_file:
|
|
|
- out_path = Path(output_file)
|
|
|
- else:
|
|
|
- if len(input_paths) > 1:
|
|
|
- out_path = input_paths[0].parent / "set_invariant_features.json"
|
|
|
- else:
|
|
|
- out_path = input_paths[0].parent / f"{input_paths[0].stem}_invariant_features.json"
|
|
|
-
|
|
|
- out_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
- out_path.write_text(json.dumps(parsed, ensure_ascii=False, indent=2), encoding="utf-8")
|
|
|
-
|
|
|
usage = data.get("usage", {})
|
|
|
prompt_tokens = usage.get("prompt_tokens") or usage.get("input_tokens", 0)
|
|
|
completion_tokens = usage.get("completion_tokens") or usage.get("output_tokens", 0)
|
|
|
-
|
|
|
summary = {
|
|
|
"model": chosen_model,
|
|
|
"input_images": [str(p) for p in input_paths],
|
|
|
"input_count": len(input_paths),
|
|
|
- "output_file": str(out_path),
|
|
|
+ "generated_images": output_paths,
|
|
|
"prompt_tokens": prompt_tokens,
|
|
|
"completion_tokens": completion_tokens,
|
|
|
}
|
|
|
-
|
|
|
return ToolResult(
|
|
|
- title="NanoBanana 不变特征提取完成",
|
|
|
- output=json.dumps(
|
|
|
- {
|
|
|
- "summary": summary,
|
|
|
- "features": parsed,
|
|
|
- },
|
|
|
- ensure_ascii=False,
|
|
|
- indent=2,
|
|
|
- ),
|
|
|
- long_term_memory=f"Extracted invariant features from {len(input_paths)} input image(s) using {chosen_model}",
|
|
|
- attachments=[str(out_path)],
|
|
|
+ title="NanoBanana 图片生成完成",
|
|
|
+ output=json.dumps({"summary": summary}, ensure_ascii=False, indent=2),
|
|
|
+ long_term_memory=f"Generated {len(output_paths)} image(s) from {len(input_paths)} input image(s) using {chosen_model}",
|
|
|
+ attachments=output_paths,
|
|
|
metadata=summary,
|
|
|
+ tool_usage={
|
|
|
+ "model": chosen_model,
|
|
|
+ "prompt_tokens": prompt_tokens,
|
|
|
+ "completion_tokens": completion_tokens,
|
|
|
+ }
|
|
|
)
|
elksmmx