chore: import upstream snapshot with attribution

examples/flex-flows/eval-code-quality/code_quality.py

@@ -0,0 +1,69 @@
+import json
+
+from typing import TypedDict
+from pathlib import Path
+
+from jinja2 import Template
+
+from promptflow.tracing import trace
+from promptflow.core import AzureOpenAIModelConfiguration
+from promptflow.core._flow import Prompty
+
+BASE_DIR = Path(__file__).absolute().parent
+
+
+@trace
+def load_prompt(jinja2_template: str, code: str, examples: list) -> str:
+    """Load prompt function."""
+    with open(BASE_DIR / jinja2_template, "r", encoding="utf-8") as f:
+        tmpl = Template(f.read(), trim_blocks=True, keep_trailing_newline=True)
+        prompt = tmpl.render(code=code, examples=examples)
+        return prompt
+
+
+class Result(TypedDict):
+    correctness: float
+    readability: float
+    explanation: str
+
+
+class CodeEvaluator:
+    def __init__(self, model_config: AzureOpenAIModelConfiguration):
+        self.model_config = model_config
+
+    def __call__(self, code: str) -> Result:
+        """Evaluate the code based on correctness, readability."""
+        prompty = Prompty.load(
+            source=BASE_DIR / "eval_code_quality.prompty",
+            model={"configuration": self.model_config},
+        )
+        output = prompty(code=code)
+        output = json.loads(output)
+        output = Result(**output)
+        return output
+
+    def __aggregate__(self, line_results: list) -> dict:
+        """Aggregate the results."""
+        total = len(line_results)
+        avg_correctness = sum(int(r["correctness"]) for r in line_results) / total
+        avg_readability = sum(int(r["readability"]) for r in line_results) / total
+        return {
+            "average_correctness": avg_correctness,
+            "average_readability": avg_readability,
+            "total": total,
+        }
+
+
+if __name__ == "__main__":
+    from promptflow.tracing import start_trace
+
+    start_trace()
+    model_config = AzureOpenAIModelConfiguration(
+        connection="open_ai_connection",
+        azure_deployment="gpt-4o",
+    )
+    evaluator = CodeEvaluator(model_config)
+    result = evaluator('print("Hello, world!")')
+    print(result)
+    aggregate_result = evaluator.__aggregate__([result])
+    print(aggregate_result)