add: main.py

main.py

@@ -0,0 +1,145 @@
+import os
+import asyncio
+from typing import TypedDict, Annotated, Dict
+
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from langchain_core.output_parsers import PydanticOutputParser
+
+# ---------- LLM ----------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# ---------- State ----------
+class CodeReviewState(TypedDict):
+    code: str
+    draft_review: str
+    criteria_scores: Dict[str, int]
+    weakest_criterion: str
+    verdict: str
+    round: int
+    max_rounds: int
+
+# ---------- Pydantic for reflect output ----------
+class ReflectOutput(BaseModel):
+    pep8: int = Field(..., description="Score 0-10 for PEP8 compliance")
+    type_hints: int = Field(..., description="Score 0-10 for type hints usage")
+    edge_cases: int = Field(..., description="Score 0-10 for edge case handling")
+    naming: int = Field(..., description="Score 0-10 for naming conventions")
+    weakest_criterion: str = Field(..., description="Name of the weakest criterion")
+    verdict: str = Field(..., description="'ok' or 'needs_revision'")
+
+reflect_parser = PydanticOutputParser(pydantic_object=ReflectOutput)
+
+# ---------- Nodes ----------
+async def draft_review(state: CodeReviewState) -> CodeReviewState:
+    prompt = f"""Please write a concise code review (3-6 bullet points) for the following Python function. Focus on style, type hints, edge cases, and naming.
+
+```python
+{state['code']}
+```
+
+Return only the review text."""
+    review = await llm.ainvoke([HumanMessage(content=prompt)])
+    state['draft_review'] = review.content.strip()
+    return state
+
+async def reflect(state: CodeReviewState) -> CodeReviewState:
+    prompt = f"""You are a code review critic. Evaluate the following review text and assign scores 0-10 for each of the four criteria: pep8, type_hints, edge_cases, naming. Also identify the weakest criterion and decide if the review is "ok" or "needs_revision".
+
+Review text:
+{state['draft_review']}
+
+Provide the output in the following JSON-like format:
+{{"pep8": int, "type_hints": int, "edge_cases": int, "naming": int, "weakest_criterion": str, "verdict": str}}
+"""
+    raw = await llm.ainvoke([HumanMessage(content=prompt)])
+    parsed = reflect_parser.parse(raw.content)
+    state['criteria_scores'] = {
+        "pep8": parsed.pep8,
+        "type_hints": parsed.type_hints,
+        "edge_cases": parsed.edge_cases,
+        "naming": parsed.naming,
+    }
+    state['weakest_criterion'] = parsed.weakest_criterion
+    state['verdict'] = parsed.verdict
+    return state
+
+async def rewrite(state: CodeReviewState) -> CodeReviewState:
+    # Simple rewrite: add a sentence addressing the weakest criterion
+    additional = f"Additionally, the review should pay more attention to {state['weakest_criterion']}.")
+    state['draft_review'] = state['draft_review'] + "\n" + additional
+    state['round'] += 1
+    return state
+
+# ---------- Graph ----------
+def build_graph() -> StateGraph[CodeReviewState]:
+    graph = StateGraph(CodeReviewState)
+    graph.add_node("draft_review", draft_review)
+    graph.add_node("reflect", reflect)
+    graph.add_node("rewrite", rewrite)
+
+    graph.set_entry_point("draft_review")
+    graph.add_edge("draft_review", "reflect")
+    graph.add_conditional_edges(
+        "reflect",
+        lambda x: "END" if x['verdict'] == "ok" or x['round'] >= x['max_rounds'] else "rewrite",
+    )
+    graph.add_edge("rewrite", "reflect")
+
+    return graph.compile()
+
+# ---------- Tool ----------
+@tool
+def code_review_tool(code: str) -> str:
+    """Perform a structured code review with possible rewrites."""
+    graph = build_graph()
+    initial_state: CodeReviewState = {
+        "code": code,
+        "draft_review": "",
+        "criteria_scores": {},
+        "weakest_criterion": "",
+        "verdict": "",
+        "round": 0,
+        "max_rounds": 2,
+    }
+    final_state = graph.invoke(initial_state)
+    return f"Final Review:\n{final_state['draft_review']}\n\nScores: {final_state['criteria_scores']}"
+
+# ---------- DeepAgent ----------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+agent = create_deep_agent(
+    model=llm,
+    tools=[code_review_tool],
+    backend=backend,
+    system_prompt="You are a helpful code review assistant.",
+)
+
+async def main():
+    sample_code = """
+    def sort_numbers(arr):
+        return sorted(arr)
+    """
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=f"Please review this function:\n{sample_code}")]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    print(result["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())