from typing import Dict, Any
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from scripts.rag_chat import build_general_qa_chain

def build_router_chain(model_name=None):
    general_qa = build_general_qa_chain(model_name=model_name)
    llm = ChatOpenAI(model_name=model_name or "gpt-4o-mini", temperature=0.0)

    # This prompt asks the LLM to choose which "mode" to use
    router_prompt = ChatPromptTemplate.from_template("""
You are a routing assistant for a chatbot. 
Classify the following user request into one of these categories:
- "code" for programming or debugging
- "summarize" for summary requests
- "calculate" for math or numeric calculations
- "general" for general Q&A using course files

Return ONLY the category word.

User request: {input}
""")

    router_chain = router_prompt | llm | StrOutputParser()

    class Router:
        def invoke(self, input_dict: Dict[str, Any]):
            category = router_chain.invoke({"input": input_dict["input"]}).strip().lower()

            print(f"[ROUTER] User query routed to category: {category}")

            if category == "code":
                prompt = ChatPromptTemplate.from_template(
                    "As a coding assistant, help with this Python question.\nQuestion: {input}\nAnswer:"
                )
                chain = prompt | llm | StrOutputParser()
                return {"result": chain.invoke({"input": input_dict["input"]})}

            # elif category == "summarize":
            #     prompt = ChatPromptTemplate.from_template(
            #         "Provide a concise summary about: {input}\nSummary:"
            #     )
            #     chain = prompt | llm | StrOutputParser()
            #     return {"result": chain.invoke({"input": input_dict["input"]})}

            #elif category == "summarize":
            #    # 1. Use RAG to retrieve relevant docs
            #    rag_result = general_qa({"query": input_dict["input"]})

            #    # 2. Extract docs and prepare text
            #    source_docs = rag_result.get("source_documents", [])
            #    combined_text = "\n\n".join([doc.page_content for doc in source_docs])

            #    # 3. Run the summarizer chain on the retrieved text
            #    from scripts.summarizer import get_summarizer
            #    summarizer_chain = get_summarizer()
            #    summary = summarizer_chain.run(combined_text)

            #    # 4. Add sources if any
            #    sources = list({str(doc.metadata.get("source", "unknown")) for doc in source_docs})
            #    if sources:
            #        summary += f"\n\n📚 Sources: {', '.join(sources)}"

            #    return {"result": summary}
            

            elif category == "summarize":
                # 1) Retrieve relevant documents via your existing RAG chain
                rag_result = general_qa({"query": input_dict["input"]})

                # 2) Get the retrieved docs (already LangChain Document objects)
                source_docs = rag_result.get("source_documents", []) or []

                # 3) Build the summarizer and prepare the docs list
                from langchain.docstore.document import Document
                from scripts.summarizer import get_summarizer

                summarizer_chain = get_summarizer()

                # If retrieval returned nothing, fall back to summarizing the user’s text
                docs = source_docs if source_docs else [Document(page_content=input_dict["input"])]

                # 4) Summarize — load_summarize_chain returns {"output_text": "..."}
                out = summarizer_chain.invoke(docs)
                summary = out["output_text"] if isinstance(out, dict) and "output_text" in out else str(out)

                # 5) Append sources (only if we actually had retrieved docs)
                if source_docs:
                    sources = sorted({str(d.metadata.get("source", "unknown")) for d in source_docs})
                    if sources:
                        summary += f"\n\n📚 Sources: {', '.join(sources)}"

                return {"result": summary}


            elif category == "calculate":
                prompt = ChatPromptTemplate.from_template(
                    "Solve the following calculation step-by-step:\n{input}"
                )
                chain = prompt | llm | StrOutputParser()
                return {"result": chain.invoke({"input": input_dict["input"]})}

            else:  # "general"
                return general_qa({"query": input_dict["input"]})

    return Router()