Solution published successfully: update main.py

This commit is contained in:
2026-06-18 12:29:49 +00:00
parent 0416a805d3
commit ac83be424f
+33 -17
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@@ -2,8 +2,10 @@
Implementation follows assignment: Implementation follows assignment:
- State: CodeReviewState with 4 criteria. - State: CodeReviewState with 4 criteria.
- Nodes: review_and_critique, rewrite. - Nodes: draft_review, reflect, rewrite.
- Graph: START -> review_and_critique -> (ok -> END) or (needs_revision & round<max_rounds -> rewrite -> review_and_critique). - Graph: START -> draft_review -> reflect
- ok -> END
- needs_revision & round < max_rounds -> rewrite -> reflect
- Uses LangGraph and LangChain OpenAI for LLM calls. - Uses LangGraph and LangChain OpenAI for LLM calls.
- Structured output for critique via Pydantic model. - Structured output for critique via Pydantic model.
- Demo function sort_numbers. - Demo function sort_numbers.
@@ -11,7 +13,6 @@ Implementation follows assignment:
from __future__ import annotations from __future__ import annotations
import os
from typing import TypedDict, Dict from typing import TypedDict, Dict
from langgraph.graph import StateGraph, END from langgraph.graph import StateGraph, END
@@ -30,18 +31,19 @@ class CodeReviewState(TypedDict):
max_rounds: int max_rounds: int
# ---------- LLM ---------- # ---------- LLM ----------
# Use OpenAI only
llm = ChatOpenAI(temperature=0) llm = ChatOpenAI(temperature=0)
# ---------- Nodes ---------- # ---------- Nodes ----------
class CritiqueOutput(BaseModel): class DraftReviewOutput(BaseModel):
review: str = Field(..., description="Draft review text") review: str = Field(..., description="Draft review text")
class ReflectOutput(BaseModel):
scores: Dict[str, int] = Field(..., description="Scores 0-10 for each criterion") scores: Dict[str, int] = Field(..., description="Scores 0-10 for each criterion")
weakest_criterion: str = Field(..., description="Criterion with lowest score") weakest_criterion: str = Field(..., description="Criterion with lowest score")
verdict: str = Field(..., description="'ok' or 'needs_revision'") verdict: str = Field(..., description="'ok' or 'needs_revision'")
def review_and_critique(state: CodeReviewState) -> CodeReviewState: def draft_review(state: CodeReviewState) -> CodeReviewState:
code = state["code"] code = state["code"]
prompt = f""" prompt = f"""
Write a concise code review (3-6 bullet points) for the following Python function. Focus on style, correctness, and potential improvements. Write a concise code review (3-6 bullet points) for the following Python function. Focus on style, correctness, and potential improvements.
@@ -49,13 +51,23 @@ def review_and_critique(state: CodeReviewState) -> CodeReviewState:
```python ```python
{code} {code}
``` ```
Then evaluate the review on four criteria (PEP8, type_hints, edge_cases, naming) on a scale 0-10.
Return a JSON object with keys: review (string), scores (dict), weakest_criterion (string), verdict ('ok' if all scores >=7 else 'needs_revision').
""" """
response = llm.invoke([HumanMessage(content=prompt)]) response = llm.invoke([HumanMessage(content=prompt)])
data = CritiqueOutput.model_validate_json(response.content) data = DraftReviewOutput.model_validate_json(response.content)
state["draft_review"] = data.review state["draft_review"] = data.review
return state
def reflect(state: CodeReviewState) -> CodeReviewState:
review = state["draft_review"]
prompt = f"""
Evaluate the following review on four criteria (PEP8, type_hints, edge_cases, naming) on a scale 0-10. Return a JSON object with keys: scores (dict), weakest_criterion (string), verdict ('ok' if all scores >=7 else 'needs_revision').
Review:
{review}
"""
response = llm.invoke([HumanMessage(content=prompt)])
data = ReflectOutput.model_validate_json(response.content)
state["criteria_scores"] = data.scores state["criteria_scores"] = data.scores
state["weakest_criterion"] = data.weakest_criterion state["weakest_criterion"] = data.weakest_criterion
state["verdict"] = data.verdict state["verdict"] = data.verdict
@@ -77,22 +89,26 @@ def rewrite(state: CodeReviewState) -> CodeReviewState:
# ---------- Graph ---------- # ---------- Graph ----------
builder = StateGraph(CodeReviewState) builder = StateGraph(CodeReviewState)
builder.add_node("review_and_critique", review_and_critique) builder.add_node("draft_review", draft_review)
builder.add_node("reflect", reflect)
builder.add_node("rewrite", rewrite) builder.add_node("rewrite", rewrite)
builder.set_entry_point("review_and_critique") builder.set_entry_point("draft_review")
# After initial review_and_critique, decide to end if verdict ok # After draft_review, go to reflect
builder.add_edge("draft_review", "reflect")
# After reflect, decide
builder.add_conditional_edges( builder.add_conditional_edges(
"review_and_critique", "reflect",
lambda state: state["verdict"] == "ok", lambda state: state["verdict"] == "ok",
{"ok": END, "needs_revision": "rewrite"}, {"ok": END, "needs_revision": "rewrite"},
) )
# After rewrite, go back to review_and_critique if rounds remain # After rewrite, go back to reflect if rounds remain
builder.add_conditional_edges( builder.add_conditional_edges(
"rewrite", "rewrite",
lambda state: "review_and_critique" if state["round"] < state["max_rounds"] else END, lambda state: state["round"] < state["max_rounds"],
{"review_and_critique": "review_and_critique", END: END} {"continue": "reflect", "end": END},
) )
# Compile graph
graph = builder.compile() graph = builder.compile()