app.py

from flask import Flask, render_template, request, redirect, url_for, flash
from flask_socketio import SocketIO
import os
from dotenv import load_dotenv
from werkzeug.utils import secure_filename

# LangChain and agent imports
from typing import Annotated, Literal
from langchain_core.messages import AIMessage, ToolMessage
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import AnyMessage, add_messages
from langchain_core.runnables import RunnableLambda, RunnableWithFallbacks
from langgraph.prebuilt import ToolNode
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_core.tools import tool
import traceback

# Load environment variables
load_dotenv()

# Global configuration variables
UPLOAD_FOLDER = os.path.join(os.getcwd(), "uploads")
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
BASE_DIR = os.path.abspath(os.path.dirname(__file__))

# API Keys from .env file
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
os.environ["MISTRAL_API_KEY"] = os.getenv("MISTRAL_API_KEY")

# Flask and SocketIO setup
flask_app = Flask(__name__)
flask_app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
socketio = SocketIO(flask_app, cors_allowed_origins="*")

# Global state
agent_app = None
abs_file_path = None

def create_agent_app(db_path: str):
    from langchain_groq import ChatGroq
    llm = ChatGroq(model="llama3-70b-8192")

    abs_db_path = os.path.abspath(db_path)
    db_instance = SQLDatabase.from_uri(f"sqlite:///{abs_db_path}")

    @tool
    def db_query_tool(query: str) -> str:
        result = db_instance.run_no_throw(query)
        return result or "Error: Query failed. Please rewrite your query and try again."

    class SubmitFinalAnswer(BaseModel):
        final_answer: str = Field(...)

    class State(TypedDict):
        messages: Annotated[list[AnyMessage], add_messages]

    query_check = ChatPromptTemplate.from_messages([
        ("system", "You are a SQL expert. Fix common issues in SQLite queries."),
        ("placeholder", "{messages}")
    ]) | llm.bind_tools([db_query_tool])

    query_gen = ChatPromptTemplate.from_messages([
        ("system", "You are a SQL expert. Generate SQLite query and return answer using SubmitFinalAnswer tool."),
        ("placeholder", "{messages}")
    ]) | llm.bind_tools([SubmitFinalAnswer])

    toolkit = SQLDatabaseToolkit(db=db_instance, llm=llm)
    tools_instance = toolkit.get_tools()

    def first_tool_call(state: State):
        return {"messages": [AIMessage(content="", tool_calls=[{"name": "sql_db_list_tables", "args": {}, "id": "tool_abcd123"}])]} 

    def handle_tool_error(state: State):
        tool_calls = state["messages"][-1].tool_calls
        return {"messages": [
            ToolMessage(content="Error occurred. Please revise.", tool_call_id=tc["id"]) for tc in tool_calls
        ]}

    def create_tool_node_with_fallback(tools_list):
        return ToolNode(tools_list).with_fallbacks([RunnableLambda(handle_tool_error)], exception_key="error")

    def query_gen_node(state: State):
        message = query_gen.invoke(state)
        tool_messages = []
        if message.tool_calls:
            for tc in message.tool_calls:
                if tc["name"] != "SubmitFinalAnswer":
                    tool_messages.append(ToolMessage(
                        content=f"Error: Wrong tool called: {tc['name']}",
                        tool_call_id=tc["id"]
                    ))
        return {"messages": [message] + tool_messages}

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

    def model_check_query(state: State):
        return {"messages": [query_check.invoke({"messages": [state["messages"][-1]]})]}

    list_tool = next((t for t in tools_instance if t.name == "sql_db_list_tables"), None)
    schema_tool = next((t for t in tools_instance if t.name == "sql_db_schema"), None)
    model_get_schema = llm.bind_tools([schema_tool])

    workflow = StateGraph(State)
    workflow.add_node("first_tool_call", first_tool_call)
    workflow.add_node("list_tables_tool", create_tool_node_with_fallback([list_tool]))
    workflow.add_node("get_schema_tool", create_tool_node_with_fallback([schema_tool]))
    # Corrected the unterminated string literal in the lambda function below:
    workflow.add_node("model_get_schema", lambda s: {"messages": [model_get_schema.invoke(s["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")

    return workflow.compile()

@flask_app.route("/", methods=["GET"])
def index():
    return render_template("index.html")

@flask_app.route("/upload", methods=["GET", "POST"])
def upload():
    global abs_file_path, agent_app
    try:
        if request.method == "POST":
            file = request.files.get("file")
            if not file:
                return "No file uploaded", 400

            filename = secure_filename(file.filename)
            if filename.endswith('.db'):
                save_path = os.path.join(flask_app.config['UPLOAD_FOLDER'], "uploaded.db")
                file.save(save_path)
                abs_file_path = os.path.abspath(save_path)
                agent_app = None  # Reset agent; reinitialize on next query.
                socketio.emit("log", {"message": f"Database '{filename}' uploaded."})
                return redirect(url_for("index"))
        return render_template("upload.html")
    except Exception as e:
        socketio.emit("log", {"message": f"[ERROR]: {str(e)}"})
        return render_template("upload.html")

@socketio.on("user_input")
def handle_user_input(data):
    prompt = data.get("message")
    if not prompt:
        socketio.emit("log", {"message": "[ERROR]: Empty prompt."})
        return
    run_agent(prompt)

def run_agent(prompt):
    global agent_app, abs_file_path
    if not abs_file_path:
        socketio.emit("final", {"message": "No DB uploaded."})
        return
    try:
        if agent_app is None:
            agent_app = create_agent_app(abs_file_path)
            socketio.emit("log", {"message": "[INFO]: Agent initialized."})

        query = {"messages": [("user", prompt)]}
        result = agent_app.invoke(query)
        try:
            result = result["messages"][-1].tool_calls[0]["args"]["final_answer"]
        except Exception:
            result = "Query failed or no valid answer found."
        socketio.emit("final", {"message": result})
    except Exception as e:
        socketio.emit("log", {"message": f"[ERROR]: {str(e)}"})
        socketio.emit("final", {"message": "Generation failed."})

# Expose the Flask app as "app" for Gunicorn
app = flask_app

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