仪评指标联分析模块

bbit_ai/test/vision/Vision.py

@@ -0,0 +1,56 @@
+import os
+import base64
+from langchain.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain.output_parsers import JsonOutputParser
+from langchain.schema import HumanMessage
+from pydantic import BaseModel, Field
+
+# 定义你想要的结构化输出
+class ImageDescription(BaseModel):
+    objects: list[str] = Field(description="图片里出现的主要物体")
+    scene: str = Field(description="场景描述")
+    mood: str = Field(description="整体氛围")
+
+parser = JsonOutputParser(pydantic_object=ImageDescription)
+
+prompt = PromptTemplate(
+    template="""你是一个图像分析助手。请根据输入的图片内容和文字说明，
+输出符合下列 JSON schema 的结果：
+{
+		"moisture_content": 12.5,
+		"cocoon_weight": 15.2,
+		"defective_pupa_count": 3,
+		"fresh_shell_weight": 8.7,
+		"sample_count": 50,
+		"net_weight_total": 760,
+		"evaluator": "张三",
+		"reviewer": "李四",
+		"barcode": "123456789012"
+}
+
+输入内容:
+{query}
+""",
+    input_variables=["query"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+def get_response(base64_image: str, system_text: str = "请分析这张图片并输出JSON结果"):
+    messages = [
+        HumanMessage(content=[
+            {"type": "text", "text": system_text},
+            # {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}  # 本地图片转成base64
+            {"type": "image_url", "image_url": {"url": {base64_image}}}  # 本地图片转成base64
+        ])
+    ]
+
+    # 串起来：prompt -> llm -> parser
+    chain = prompt | llm | parser
+    response = chain.invoke({"query": messages})
+    print(response)
+
+if __name__ == "__main__":
+    with open("test.jpg", "rb") as f:
+        base64_image = base64.b64encode(f.read()).decode("utf-8")
+    get_response(base64_image)