Add main.py
This commit is contained in:
@@ -1,136 +1,84 @@
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import os
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import asyncio
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from typing import TypedDict, Annotated, Dict
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from langgraph.graph import StateGraph, START, END
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from langgraph.graph.message import add_messages
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, AIMessage
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from langchain_core.output_parsers import PydanticOutputParser
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from langchain_core.messages import HumanMessage, SystemMessage
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from langgraph.graph import StateGraph, START, END
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from typing import TypedDict, Annotated
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from langgraph.graph.message import add_messages
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from pydantic import BaseModel, Field
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from deepagents.tools import tool
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from langchain_core.output_parsers import PydanticOutputParser
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# ---------- LLM ----------
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# LLM setup
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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model="gpt-4o-mini",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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# ---------- State ----------
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# State definition
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class CodeReviewState(TypedDict):
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code: str
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draft_review: str
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criteria_scores: Dict[str, int]
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criteria_scores: dict[str, int]
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weakest_criterion: str
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verdict: str
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round: int
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max_rounds: int
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# ---------- Structured output for reflect ----------
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# Node: draft_review
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async def draft_review(state: CodeReviewState):
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prompt = f"Write a concise code review (3-6 points) for the following Python function:\n\n{state['code']}"
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msg = await llm.ainvoke([HumanMessage(content=prompt)])
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state['draft_review'] = msg.content
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return state
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# Node: reflect
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class ReflectOutput(BaseModel):
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pep8: int = Field(..., ge=0, le=10)
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type_hints: int = Field(..., ge=0, le=10)
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edge_cases: int = Field(..., ge=0, le=10)
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naming: int = Field(..., ge=0, le=10)
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weakest_criterion: str = Field(...)
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verdict: str = Field(..., regex="^(ok|needs_revision)$")
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scores: dict[str, int]
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weakest: str
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verdict: str
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reflect_parser = PydanticOutputParser(pydantic_object=ReflectOutput)
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parser = PydanticOutputParser(pydantic_object=ReflectOutput)
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# ---------- Nodes ----------
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@tool
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def draft_review(state: CodeReviewState) -> CodeReviewState:
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"""Generate an initial code review."""
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prompt = (
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"You are a senior Python reviewer.\n"
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"Given the following function, write a concise code review (3–6 points).\n"
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"Focus on style, correctness, and potential improvements.\n"
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"Return only the review text.\n\n"
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f"Function:\n{state['code']}"
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)
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review = llm.invoke([HumanMessage(content=prompt)]).content
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state['draft_review'] = review
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async def reflect(state: CodeReviewState):
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prompt = f"Evaluate the draft review and assign scores 0-10 for PEP8, type_hints, edge_cases, naming. Return JSON with keys scores, weakest, verdict (ok or needs_revision).\n\nDraft review:\n{state['draft_review']}"
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msg = await llm.ainvoke([HumanMessage(content=prompt)])
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out = parser.parse(msg.content)
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state['criteria_scores'] = out.scores
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state['weakest_criterion'] = out.weakest
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state['verdict'] = out.verdict
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return state
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@tool
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def reflect(state: CodeReviewState) -> CodeReviewState:
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"""Critique the draft review and score four criteria."""
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prompt = (
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"You are an automated code review critic.\n"
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"Given the original code and the draft review, assign a score 0–10 for each of the following criteria:\n"
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"- pep8: adherence to PEP8 style guide\n"
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"- type_hints: use of type hints\n"
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"- edge_cases: handling of edge cases\n"
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"- naming: clarity of identifiers\n"
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"Also identify the weakest criterion and decide if the review is "ok" or "needs_revision".\n"
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"Return a JSON object with keys: pep8, type_hints, edge_cases, naming, weakest_criterion, verdict.\n"
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"Do not include any other text.\n\n"
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f"Code:\n{state['code']}\n\n"
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f"Draft Review:\n{state['draft_review']}"
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)
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raw = llm.invoke([HumanMessage(content=prompt)]).content
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try:
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parsed = reflect_parser.parse(raw)
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except Exception as e:
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# Fallback: simple extraction
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parsed = ReflectOutput(pep8=5, type_hints=5, edge_cases=5, naming=5, weakest_criterion="pep8", verdict="needs_revision")
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state['criteria_scores'] = {
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"pep8": parsed.pep8,
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"type_hints": parsed.type_hints,
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"edge_cases": parsed.edge_cases,
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"naming": parsed.naming,
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}
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state['weakest_criterion'] = parsed.weakest_criterion
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state['verdict'] = parsed.verdict
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return state
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@tool
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def rewrite(state: CodeReviewState) -> CodeReviewState:
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"""Rewrite the section of the draft review that addresses the weakest criterion."""
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prompt = (
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"You are a senior Python reviewer.\n"
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"The draft review below has been critiqued. The weakest criterion is {criterion}.\n"
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"Rewrite only the part of the review that addresses this criterion, improving it.\n"
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"Keep the rest of the review unchanged.\n"
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"Return the full updated review.\n\n"
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f"Weakest criterion: {state['weakest_criterion']}\n\n"
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f"Draft Review:\n{state['draft_review']}"
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).format(criterion=state['weakest_criterion'])
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updated = llm.invoke([HumanMessage(content=prompt)]).content
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state['draft_review'] = updated
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# Node: rewrite
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async def rewrite(state: CodeReviewState):
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prompt = f"Rewrite the part of the draft review that addresses the weakest criterion '{state['weakest_criterion']}'. Keep other points unchanged.\n\nOriginal draft:\n{state['draft_review']}"
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msg = await llm.ainvoke([HumanMessage(content=prompt)])
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state['draft_review'] = msg.content
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state['round'] += 1
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return state
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# ---------- Graph ----------
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builder = StateGraph(CodeReviewState)
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builder.add_node("draft_review", draft_review)
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builder.add_node("reflect", reflect)
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builder.add_node("rewrite", rewrite)
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# Graph
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graph = StateGraph(CodeReviewState)
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graph.add_node("draft_review", draft_review)
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graph.add_node("reflect", reflect)
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graph.add_node("rewrite", rewrite)
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builder.set_entry_point("draft_review")
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builder.add_edge("draft_review", "reflect")
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builder.add_conditional_edges(
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graph.set_entry_point("draft_review")
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graph.add_edge("draft_review", "reflect")
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graph.add_conditional_edges(
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"reflect",
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lambda state: "rewrite" if state["verdict"] == "needs_revision" and state["round"] < state["max_rounds"] else "END",
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lambda s: "rewrite" if s['verdict']=='needs_revision' and s['round']<s['max_rounds'] else END,
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)
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builder.add_edge("rewrite", "reflect")
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builder.add_edge("END", END)
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graph.add_edge("rewrite", "reflect")
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graph = builder.compile()
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app = graph.compile()
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# ---------- DeepAgent wrapper ----------
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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@tool
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def run_review(code: str) -> str:
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"""Run the LangGraph code review pipeline on the provided code."""
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initial_state: CodeReviewState = {
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# Demo function
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async def demo():
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code = """def sort_numbers(arr):
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return sorted(arr)"""
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state: CodeReviewState = {
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"code": code,
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"draft_review": "",
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"criteria_scores": {},
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@@ -139,31 +87,10 @@ def run_review(code: str) -> str:
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"round": 0,
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"max_rounds": 2,
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}
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final_state = graph.invoke(initial_state)
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return (
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f"Initial Draft Review:\n{final_state['draft_review']}\n\n"
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f"Scores: {final_state['criteria_scores']}\n"
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f"Verdict: {final_state['verdict']}\n"
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f"Rounds: {final_state['round']}\n"
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)
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agent = create_deep_agent(
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model=llm,
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tools=[run_review],
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backend=backend,
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system_prompt="You are a code review assistant.",
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)
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async def main():
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code_example = """
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def sort_numbers(arr):
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return sorted(arr)
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"""
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=f"Please review this code:\n{code_example}")]},
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{"configurable": {"thread_id": "session-1"}},
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)
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print(result["messages"][-1].content)
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final = await app.ainvoke(state)
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print("Final draft review:\n", final['draft_review'])
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print("Scores:", final['criteria_scores'])
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print("Verdict:", final['verdict'])
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if __name__ == "__main__":
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asyncio.run(main())
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asyncio.run(demo())
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