add: main.py

main.py

@@ -0,0 +1,183 @@
+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,
+)
+
+# ---------- Backend ----------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# ---------- 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 model for reflect output ----------
+class ReflectOutput(BaseModel):
+    pep8: int = Field(description="Score for PEP8 compliance (0-10)")
+    type_hints: int = Field(description="Score for type hints (0-10)")
+    edge_cases: int = Field(description="Score for edge case handling (0-10)")
+    naming: int = Field(description="Score for naming conventions (0-10)")
+    weakest_criterion: str = Field(description="The criterion with the lowest score")
+    verdict: str = Field(description="'ok' or 'needs_revision'")
+
+parser = PydanticOutputParser(pydantic_object=ReflectOutput)
+
+# ---------- Nodes ----------
+async def draft_review(state: CodeReviewState) -> CodeReviewState:
+    prompt = f"""
+    Write a concise code review for the following Python function. Provide 3-6 bullet points.
+
+    Function:
+    ```python
+    {state['code']}
+    ```
+    """
+    response = await llm.ainvoke(HumanMessage(content=prompt))
+    state['draft_review'] = response.content.strip()
+    return state
+
+async def reflect(state: CodeReviewState) -> CodeReviewState:
+    prompt = f"""
+    Evaluate the following code and draft review. Score each of the four criteria on a scale 0-10:
+    - PEP8 compliance
+    - Type hints
+    - Edge case handling
+    - Naming conventions
+
+    Provide the scores, identify the weakest criterion, and give a verdict ('ok' or 'needs_revision').
+
+    Code:
+    ```python
+    {state['code']}
+    ```
+
+    Draft Review:
+    ```text
+    {state['draft_review']}
+    ```
+
+    Return a JSON object with keys: pep8, type_hints, edge_cases, naming, weakest_criterion, verdict.
+    """
+    response = await llm.ainvoke(HumanMessage(content=prompt))
+    try:
+        parsed = parser.parse(response.content)
+    except Exception as e:
+        # Fallback: set all scores to 0 and verdict to needs_revision
+        parsed = ReflectOutput(pep8=0, type_hints=0, edge_cases=0, naming=0, weakest_criterion="unknown", verdict="needs_revision")
+    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:
+    prompt = f"""
+    Rewrite the section of the draft review that addresses the weakest criterion: {state['weakest_criterion']}.
+    Keep all other parts of the review unchanged.
+
+    Current Draft Review:
+    ```text
+    {state['draft_review']}
+    ```
+    """
+    response = await llm.ainvoke(HumanMessage(content=prompt))
+    state['draft_review'] = response.content.strip()
+    state['round'] += 1
+    return state
+
+# ---------- Graph ----------
+review_graph = StateGraph(CodeReviewState)
+review_graph.add_node("draft_review", draft_review)
+review_graph.add_node("reflect", reflect)
+review_graph.add_node("rewrite", rewrite)
+
+review_graph.set_entry_point("draft_review")
+review_graph.add_edge("draft_review", "reflect")
+
+# Conditional edges after reflect
+review_graph.add_conditional_edges(
+    "reflect",
+    lambda state: "END" if state["verdict"] == "ok" else "rewrite" if state["round"] < state["max_rounds"] else "END",
+)
+review_graph.add_edge("rewrite", "reflect")
+
+compiled_graph = review_graph.compile()
+
+# ---------- Tool ----------
+@tool
+async def run_code_review(code: str) -> Dict:
+    """Run a code review on the provided Python function."""
+    initial_state: CodeReviewState = {
+        "code": code,
+        "draft_review": "",
+        "criteria_scores": {},
+        "weakest_criterion": "",
+        "verdict": "",
+        "round": 0,
+        "max_rounds": 2,
+    }
+    final_state = compiled_graph.invoke(initial_state)
+    return {
+        "final_review": final_state["draft_review"],
+        "scores": final_state["criteria_scores"],
+        "weakest_criterion": final_state["weakest_criterion"],
+        "verdict": final_state["verdict"],
+        "rounds": final_state["round"],
+    }
+
+# ---------- Agent ----------
+agent = create_deep_agent(
+    model=llm,
+    tools=[run_code_review],
+    backend=backend,
+    system_prompt="You are a helpful code review agent.",
+)
+
+# ---------- Demo ----------
+async def main():
+    # Sample function to review
+    code = """
+    def sort_numbers(arr):
+        return sorted(arr)
+    """
+    result = await run_code_review(code)
+    print("\n=== Final Review ===\n")
+    print(result["final_review"])
+    print("\n=== Scores ===\n")
+    for k, v in result["scores"].items():
+        print(f"{k}: {v}")
+    print(f"\nVerdict: {result['verdict']} (Rounds: {result['rounds']})")
+
+if __name__ == "__main__":
+    asyncio.run(main())