from flask import Flask, render_template, request
from flask_socketio import SocketIO
import threading
import os
from dotenv import load_dotenv

# LangChain and agent imports
from langchain_community.chat_models.huggingface import ChatHuggingFace  # if needed later
from langchain.agents import Tool
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
from langchain_core.callbacks import CallbackManager, BaseCallbackHandler
from langchain_community.agent_toolkits.load_tools import load_tools  # ensure correct import
from langchain_core.tools import tool
from langchain_community.agent_toolkits import PowerBIToolkit
from langchain.chains import LLMMathChain
from langchain import hub
from langchain_community.tools import DuckDuckGoSearchRun

# Agent requirements and type hints
from typing import Annotated, Literal, Sequence, TypedDict, Any
from langchain_core.messages import AIMessage, ToolMessage
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from langgraph.graph import END, StateGraph, START
from langgraph.graph.message import AnyMessage, add_messages
from langchain_core.runnables import RunnableLambda, RunnableWithFallbacks
from langgraph.prebuilt import ToolNode

# Load environment variables
load_dotenv()

# Instead of hardcoding the DB URI, get it from an environment variable.
# This lets you plug in any single DB by changing the DATABASE_URI environment variable.
DATABASE_URI = os.getenv("DATABASE_URI", "sqlite:///employee.db")

GROQ_API_KEY = os.getenv("GROQ_API_KEY")
os.environ["GROQ_API_KEY"] =  GROQ_API_KEY

# Use ChatGroq LLM (which does not require a Hugging Face API token)
from langchain_groq import ChatGroq
llm = ChatGroq(model="llama3-70b-8192")

# Connect to the provided database URI (works with any single DB)
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_uri(DATABASE_URI)

# Create SQL toolkit and get the tools
from langchain_community.agent_toolkits import SQLDatabaseToolkit
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
tools = toolkit.get_tools()

# Define a custom query tool for executing SQL queries
@tool
def db_query_tool(query: str) -> str:
    """
    Execute a SQL query against the database and return the result.
    If the query is invalid or returns no result, an error message will be returned.
    In case of an error, the user is advised to rewrite the query and try again.
    """
    result = db.run_no_throw(query)
    if not result:
        return "Error: Query failed. Please rewrite your query and try again."
    return result

# Define a Pydantic model for submitting the final answer
class SubmitFinalAnswer(BaseModel):
    """Submit the final answer to the user based on the query results."""
    final_answer: str = Field(..., description="The final answer to the user")

# Define the state type
class State(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

# Define prompt templates for query checking and query generation
from langchain_core.prompts import ChatPromptTemplate

query_check_system = """You are a SQL expert with a strong attention to detail.
Double check the SQLite query for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joins

If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.

You will call the appropriate tool to execute the query after running this check."""
query_check_prompt = ChatPromptTemplate.from_messages([("system", query_check_system), ("placeholder", "{messages}")])
query_check = query_check_prompt | llm.bind_tools([db_query_tool])

query_gen_system = """You are a SQL expert with a strong attention to detail.

Given an input question, output a syntactically correct SQLite query to run, then look at the results of the query and return the answer.

DO NOT call any tool besides SubmitFinalAnswer to submit the final answer.

When generating the query:

Output the SQL query that answers the input question without a tool call.

Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.

If you get an error while executing a query, rewrite the query and try again.

If you get an empty result set, you should try to rewrite the query to get a non-empty result set.
NEVER make stuff up if you don't have enough information to answer the query... just say you don't have enough information.

If you have enough information to answer the input question, simply invoke the appropriate tool to submit the final answer to the user.

DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database. Do not return any sql query except answer."""
query_gen_prompt = ChatPromptTemplate.from_messages([("system", query_gen_system), ("placeholder", "{messages}")])
query_gen = query_gen_prompt | llm.bind_tools([SubmitFinalAnswer])

# Define nodes and fallback functions for the workflow
def first_tool_call(state: State) -> dict[str, list[AIMessage]]:
    return {"messages": [AIMessage(content="", tool_calls=[{"name": "sql_db_list_tables", "args": {}, "id": "tool_abcd123"}])]}

def handle_tool_error(state: State) -> dict:
    error = state.get("error")
    tool_calls = state["messages"][-1].tool_calls
    return {
        "messages": [
            ToolMessage(content=f"Error: {repr(error)}\n please fix your mistakes.", tool_call_id=tc["id"])
            for tc in tool_calls
        ]
    }

def create_tool_node_with_fallback(tools_list: list) -> RunnableWithFallbacks[Any, dict]:
    return ToolNode(tools_list).with_fallbacks([RunnableLambda(handle_tool_error)], exception_key="error")

def query_gen_node(state: State):
    message = query_gen.invoke(state)
    # Check for incorrect tool calls
    tool_messages = []
    if message.tool_calls:
        for tc in message.tool_calls:
            if tc["name"] != "SubmitFinalAnswer":
                tool_messages.append(
                    ToolMessage(
                        content=f"Error: The wrong tool was called: {tc['name']}. Please fix your mistakes. Remember to only call SubmitFinalAnswer to submit the final answer. Generated queries should be outputted WITHOUT a tool call.",
                        tool_call_id=tc["id"],
                    )
                )
    return {"messages": [message] + tool_messages}

def should_continue(state: State) -> Literal[END, "correct_query", "query_gen"]:
    messages = state["messages"]
    last_message = messages[-1]
    if getattr(last_message, "tool_calls", None):
        return END
    if last_message.content.startswith("Error:"):
        return "query_gen"
    else:
        return "correct_query"

def model_check_query(state: State) -> dict[str, list[AIMessage]]:
    """Double-check if the query is correct before executing it."""
    return {"messages": [query_check.invoke({"messages": [state["messages"][-1]]})]}

# Get tools for listing tables and fetching schema
list_tables_tool = next((tool for tool in tools if tool.name == "sql_db_list_tables"), None)
get_schema_tool = next((tool for tool in tools if tool.name == "sql_db_schema"), None)

# Define the workflow (state graph)
workflow = StateGraph(State)
workflow.add_node("first_tool_call", first_tool_call)
workflow.add_node("list_tables_tool", create_tool_node_with_fallback([list_tables_tool]))
workflow.add_node("get_schema_tool", create_tool_node_with_fallback([get_schema_tool]))
model_get_schema = llm.bind_tools([get_schema_tool])
workflow.add_node("model_get_schema", lambda state: {"messages": [model_get_schema.invoke(state["messages"])],})
workflow.add_node("query_gen", query_gen_node)
workflow.add_node("correct_query", model_check_query)
workflow.add_node("execute_query", create_tool_node_with_fallback([db_query_tool]))

workflow.add_edge(START, "first_tool_call")
workflow.add_edge("first_tool_call", "list_tables_tool")
workflow.add_edge("list_tables_tool", "model_get_schema")
workflow.add_edge("model_get_schema", "get_schema_tool")
workflow.add_edge("get_schema_tool", "query_gen")
workflow.add_conditional_edges("query_gen", should_continue)
workflow.add_edge("correct_query", "execute_query")
workflow.add_edge("execute_query", "query_gen")

# Compile the workflow into an agent application.
agent_app = workflow.compile()

# Initialize Flask and SocketIO
flask_app = Flask(__name__)
socketio = SocketIO(flask_app, cors_allowed_origins="*")

# Function to run the agent in a separate thread
def run_agent(prompt):
    try:
        query = {"messages": [("user", prompt)]}
        result = agent_app.invoke(query)
        result = result["messages"][-1].tool_calls[0]["args"]["final_answer"]
        print("final_answer------>", result)
        socketio.emit("final", {"message": f"{result}"})
    except Exception as e:
        socketio.emit("log", {"message": f"[ERROR]: {str(e)}"})
        socketio.emit("final", {"message": "Generation failed."})
        
@flask_app.route("/")
def index():
    return render_template("index.html")

@flask_app.route("/generate", methods=["POST"])
def generate():
    data = request.json
    prompt = data.get("prompt", "")
    socketio.emit("log", {"message": f"[INFO]: Received prompt: {prompt}\n"})
    # Run the agent in a separate thread
    thread = threading.Thread(target=run_agent, args=(prompt,))
    thread.start()
    return "OK", 200

if __name__ == "__main__":
    socketio.run(flask_app, debug=True)