from langgraph.graph import StateGraph, START, END
from agent_state import AgentState
from tools.transcriber import transcribe_fast
from tools.news_reporter import create_news_report
from tools.saver import save_summary

# In your workflow.py

# ... (imports and node definitions for transcribe, create_news_report, save_summary)

def build_graph():
    workflow = StateGraph(AgentState)

    workflow.add_node("transcriber", transcribe_fast)
    workflow.add_node("news_reporter", create_news_report) # Renamed for clarity
    workflow.add_node("saver", save_summary)

    # This is the conditional logic based on human approval
    def check_approval(state: AgentState):
        return "saver" if state.approved else "news_reporter"

    # Define the graph's structure
    workflow.add_edge(START, "transcriber")
    workflow.add_edge("transcriber", "news_reporter")

    # The conditional edge for the loop/save decision
    workflow.add_conditional_edges(
        "news_reporter",
        check_approval,
        {
            "saver": "saver",
            "news_reporter": "news_reporter" # This allows looping back if feedback is given
        }
    )
    workflow.add_edge("saver", END)

    return workflow.compile()