from typing import TypedDict , Annotated , List , Optional
from langgraph.graph.message import add_messages 
from langchain_core.messages import SystemMessage , HumanMessage
from langchain_openai import ChatOpenAI
from src.retrieval import retrieve
import os
from dotenv import load_dotenv
from langgraph.graph import StateGraph, START ,END
from pydantic import BaseModel , Field
import datetime
from langchain_community.utilities import SQLDatabase

load_dotenv()

class State(TypedDict) :
    connection_url : str 
    user_id : str
    messages : Annotated[List , add_messages]
    scheme : str
    sql_query : str
    query_result : str
    error : Optional[str]
    retry : int
    final_result : str


llm = ChatOpenAI(
    model="openai/gpt-4o-mini",
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1",
    temperature=0
)

class sql_query(BaseModel) :
    generated_sql_query : str = Field(...,description="The raw, valid executable SQL query text. Contain absolutely NO markdown wrapping, code blocks, or conversational formatting.")

def retrieve_node(state : State) :
    messages = state.get("messages")
    db_url = state.get("connection_url")
    user_id = state.get("user_id")

    query = messages[-1].content

    scheme = retrieve(user_id , query , db_url)

    return {'scheme' : scheme}

def generate_node(state : State) :
    messages = state.get("messages")
    scheme = state.get("scheme")
    error = state.get("error")
    wrong_query = state.get('sql_query')

    llm_with_structured_output = llm.with_structured_output(sql_query)

    history_messages = messages[:-1]
    current_query_string = messages[-1].content

    current_date = datetime.datetime.now().strftime("%Y-%m-%d")

    if history_messages:
        history_text = "\n".join([
            f"{msg.type.capitalize()}: {msg.content}" 
            for msg in history_messages
        ])
    else:
        history_text = "This is the first user request. No history exists."

    if error and wrong_query :
        error_context = f"""
=== 🚨 ERROR CORRECTION MODE 🚨 ===
Your previous attempt to answer this request failed.
[PREVIOUS BROKEN QUERY]: 
{wrong_query}

[DATABASE ERROR MESSAGE]: 
{error}

INSTRUCTION: Analyze the error message and the schema carefully. Fix the syntax, column names, or logic, and generate a CORRECTED query.
"""
    else :
        error_context = ""

    system_prompt = SystemMessage(content=f"""
You are an expert Data Analyst and SQL Engineer.

Your task is to generate ONE valid SELECT query for the latest user request.

=== DATABASE SCHEMA & DIALECT ===
{scheme}

=== CONVERSATION HISTORY ===
{history_text}

=== ERROR CORRECTION MODE ===
{error_context}

=== CRITICAL RULES ===
1. Use ONLY tables and columns that exist in the schema.
2. Never hallucinate columns, joins, or tables.
3. Generate only SELECT queries. No INSERT, UPDATE, DELETE, DROP, TRUNCATE, ALTER.
4. Use the exact SQL dialect implied by the schema metadata.
5. For any output columns, ALWAYS use clear aliases.
   Example:
   - customer_id AS customer_id
   - customer_name AS customer_name
   - SUM(amount) AS total_amount
6. When the user asks for a person/customer/company/product/entity, return BOTH:
   - the readable name field if it exists
   - the matching ID field
7. If a name exists in another table, join to fetch it.
8. If no readable name exists, return the best human-readable identifier available, and the ID.
9. For aggregate queries, include a label column when possible so the answer layer can explain the result.
10. If fixing an error, preserve the original user intent and correct only the broken parts.

=== PRIORITY RULE FOR ID VS NAME ===
- Priority 1: name + id together, if possible
- Priority 2: name only, if name exists but id cannot be included
- Priority 3: id only, only if no readable name exists

=== OUTPUT FORMAT REQUIREMENT ===
Return a SQL query whose selected columns are self-explanatory.
Do not rely on positional meaning like column 1, column 2 without aliases.

=== CURRENT DATE ===
Today's date is {current_date}.
""")
    
    final_msg = [
        system_prompt,
        HumanMessage(content=f"LATEST USER REQUEST:\n{current_query_string}")
    ]

    response = llm_with_structured_output.invoke(final_msg)

    return {'sql_query' : response.generated_sql_query , "error" : None}

def execute_node(state : State) :
    url = state.get("connection_url")
    sql_query = state.get("sql_query")
    retry = state.get("retry" , 0)

    try :
        db = SQLDatabase.from_uri(url)

        result = db.run(sql_query)

        return {"query_result" : result , "error" : None , "retry" : 0}
    
    except Exception as e :
        return {'error' : str(e) , "retry" : retry+1}
    
def routing(state : State) :
    error = state.get("error")
    retry = state.get('retry' , 0)

    if error and retry<3 :
        return "generate_node"
    else :
        return "answer_node"
    
def answer_node(state : State) :
    messages = state.get("messages")
    query_result = state.get("query_result" , "No records found.")
    sql_query = state.get("sql_query", "")
    error = state.get("error")

    history_messages = messages[:-1]
    user_query = messages[-1].content

    if history_messages:
        history_text = "\n".join([
            f"{msg.type.capitalize()}: {msg.content}" 
            for msg in history_messages
        ])
    else:
        history_text = "This is the first user request. No history exists."

    system_prompt = f"""
You are a helpful Data Analyst communicating directly with a user.

=== CONVERSATION HISTORY ===
{history_text}

=== EXECUTION CONTEXT ===
SQL QUERY USED:
{sql_query}

RAW DATABASE RESULT:
{query_result}

=== INSTRUCTIONS ===
1. Use ONLY the returned data.
2. Interpret the result using the SQL query and its selected aliases.
3. If the query selected columns like customer_id, customer_name, total_amount, use those exact labels in the final response.
4. If the result is positional, map values to the SQL SELECT order.
5. Never invent a name or ID.
6. For who/which questions:
   - prefer name + id
   - if name is missing, give the id and clearly say no readable name was returned
7. If the result contains an ID and a value like total_amount, explain them clearly.
8. Do not mention SQL or the database in the final answer.
9. Give a clean, professional response.
"""
        
    final_msg = [
        SystemMessage(content=system_prompt),
        HumanMessage(content=f"LATEST USER REQUEST:\n{user_query}")
    ]
    
    response = llm.invoke(final_msg)

    return {"messages": [response], "final_result": response.content}

workflow = StateGraph(State)

workflow.add_node("retrieve_node" , retrieve_node)
workflow.add_node("generate_node" , generate_node)
workflow.add_node("execute_node" , execute_node)
workflow.add_node("answer_node" , answer_node)

workflow.add_edge(START , "retrieve_node")
workflow.add_edge("retrieve_node" , "generate_node")
workflow.add_edge("generate_node" , "execute_node")
workflow.add_conditional_edges("execute_node" , routing , {
    "answer_node" : "answer_node" , "generate_node" : "generate_node"
})
workflow.add_edge("answer_node" , END)