app/agent.py

from langgraph.graph import StateGraph, END
from langgraph.graph.state import CompiledStateGraph
from typing import Dict, Any
from app.rag_chain import get_qa_chain
from app.models.model import LLM as llm
from app.summary_chain import get_summary_chain
from app.translate_chain import get_translate_chain
from app.rewrite_chain import get_rewrite_chain

# --- OUTPUT COLLECTOR ---
output_collector = {
    "answer": None,
    "summary": None,
    "translation": None,
    "formal_email": None
}

# --- TOOLS ---

def answer_with_rag_tool(input_text: str, input_lang: str, **kwargs) -> str:
    chain = get_qa_chain(language=input_lang)
    result = chain.invoke({"input": input_text})
    output_collector["answer"] = result
    return result

def summarize_tool(input_text: str, input_lang: str, **kwargs) -> str:
    llm, prompt = get_summary_chain(input_lang)
    result = llm.invoke(prompt.format_messages(input=input_text))
    output_collector["summary"] = result.content
    return result

def translate_tool(input_text: str, input_lang: str, target_lang: str) -> str:
    if not target_lang:
        target_lang = input_lang  # Default to input language if no target language provided
    llm, prompt = get_translate_chain(input_lang)
    result = llm.invoke(prompt.format_messages(input=input_text, target_lang=target_lang))
    output_collector["translation"] = result.content
    return result

def rewrite_email_tool(input_text: str, input_lang: str, target_lang: str) -> str:
    llm, prompt = get_rewrite_chain(input_lang)
    result = llm.invoke(prompt.format_messages(input=input_text, target_lang=target_lang))
    output_collector["formal_email"] = result.content
    return result

# --- LANGGRAPH STATE & NODES ---

class AgentState(Dict[str, Any]):
    user_input: str
    input_lang: str
    target_lang: str
    answer: str = None
    summary: str = None
    translation: str = None
    formal_email: str = None

def node_answer_by_RAG(state: AgentState) -> AgentState:
    answer = answer_with_rag_tool(state["user_input"], state["input_lang"])
    state["answer"] = answer
    return state

def node_summarize(state: AgentState) -> AgentState:
    summary = summarize_tool(state["answer"], state["input_lang"])
    state["summary"] = summary
    return state

def node_translate(state: AgentState) -> AgentState:
    # Use summary if exists, else answer
    text_to_translate = state.get("summary") or state["answer"]
    translation = translate_tool(text_to_translate, state["input_lang"], state["target_lang"])
    state["translation"] = translation
    return state

def node_rewrite(state: AgentState) -> AgentState:
    # Use translation if exists, else summary, else answer
    text_to_rewrite = state.get("translation") or state["answer"]
    formal_email = rewrite_email_tool(text_to_rewrite, state["input_lang"], state["target_lang"])
    state["formal_email"] = formal_email
    return state

# --- LANGGRAPH GRAPH DEFINITION ---

graph = StateGraph(AgentState)
graph.add_node("answer_by_RAG", node_answer_by_RAG)
graph.add_node("summarize", node_summarize)
graph.add_node("translate", node_translate)
graph.add_node("rewrite", node_rewrite)

graph.set_entry_point("answer_by_RAG")
# Remove all conditional edges and decision functions, just allow direct edges for sequential execution if needed
# (But for the new execution plan, we do not need to set edges between nodes, as we step through them manually)
# Only keep END edge for completeness

# Add END edges for all possible last nodes
for node in ["answer_by_RAG", "summarize", "translate", "rewrite"]:
    graph.add_edge(node, END)

compiled_graph = graph.compile()

# --- MAIN API CALL ---

def run_agent(user_input: str, input_lang: str = "Deutsch", target_lang: str = None, do_summarize: bool = False, do_translate: bool = False, do_email: bool = False):
    print(user_input, input_lang, target_lang, do_summarize, do_translate, do_email)
    for k in output_collector:
        output_collector[k] = None

    state = {
        "user_input": user_input,
        "input_lang": input_lang,
        "target_lang": target_lang
    }
    execution_plan = ["answer_by_RAG"]
    if do_summarize:
        execution_plan.append("summarize")
    if do_translate:
        execution_plan.append("translate")
    if do_email:
        execution_plan.append("rewrite")

    for node in execution_plan:
        state = globals()[f"node_{node}"](state)
        # Yield the current outputs after each node
        yield {k: v for k, v in output_collector.items() if v is not None}