feat: solution for 6a1864fd8a94f887e50d4706

solutions/6a1864fd8a94f887e50d4706/solution.py

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+from typing import TypedDict, List, Optional
+from langchain_openai import ChatOpenAI
+from pydantic import SecretStr
+from langgraph.graph import StateGraph, START, END
+from langgraph.checkpoint.memory import InMemorySaver
+
+# ---------- LLM ----------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b",
+    base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
+    api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
+    temperature=0.2,
+)
+
+# ---------- State ----------
+class PlanningState(TypedDict):
+    task: str
+    plan: List[str] | None
+    current_step: int
+    results: List[str]
+
+# ---------- Nodes ----------
+def planning(state: PlanningState) -> PlanningState:
+    prompt = (
+        f"Разбей задачу '{state['task']}' на 3–6 конкретных шагов. "
+        "Ответ в виде JSON массива строк, например:\n"
+        '["Шаг 1", "Шаг 2", ...]'
+    )
+    response = llm.invoke(prompt).content
+    try:
+        import json
+        plan = json.loads(response)
+        if not isinstance(plan, list):
+            raise ValueError
+    except Exception:
+        # fallback: simple split by newlines or numbers
+        plan = [line.strip() for line in response.splitlines() if line.strip()]
+    return {
+        **state,
+        "plan": plan,
+        "current_step": 0,
+        "results": [],
+    }
+
+def execution(state: PlanningState) -> PlanningState:
+    step_idx = state["current_step"]
+    if state["plan"] is None or step_idx >= len(state["plan"]):
+        return state
+    step_text = state["plan"][step_idx]
+    # Execute the step (here we just echo it; replace with real logic)
+    result = f"[Шаг {step_idx + 1}] Выполнено: {step_text}"
+    new_results = state["results"] + [result]
+    return {
+        **state,
+        "current_step": step_idx + 1,
+        "results": new_results,
+    }
+
+def should_continue(state: PlanningState) -> str:
+    if state["plan"] is None or state["current_step"] >= len(state["plan"]):
+        return "finish"
+    return "execute"
+
+# ---------- Graph ----------
+graph = StateGraph(PlanningState)
+graph.add_node("planning", planning)
+graph.add_node("execution", execution)
+
+# Connect planning to the first execution step
+graph.add_edge("planning", "execution")
+
+# Conditional loop: after each execution, decide whether to continue or finish
+graph.add_conditional_edges(
+    "execution",
+    should_continue,
+    {
+        "execute": "execution",
+        "finish": END,
+    },
+)
+
+graph.set_entry_point("planning")
+workflow = graph.compile(checkpointer=InMemorySaver())
+
+# ---------- Demo ----------
+if __name__ == "__main__":
+    task_text = input("Задача: ").strip()
+    if not task_text:
+        print("Нет задачи.")
+        exit(0)
+
+    initial_state: PlanningState = {
+        "task": task_text,
+        "plan": None,
+        "current_step": 0,
+        "results": [],
+    }
+
+    result = workflow.invoke(initial_state)
+    plan = result["plan"]
+    results = result["results"]
+
+    print("\nПлан:")
+    for i, step in enumerate(plan, start=1):
+        print(f"{i}. {step}")
+
+    print("\nРезультаты выполнения:")
+    for r in results:
+        print(r)
+
+    final_summary = "\n".join(results)
+    print("\nИтог:\n", final_summary)