app.py

from flask import Flask, render_template, request, redirect, url_for, send_from_directory, flash
from flask_socketio import SocketIO
import threading
import os
from dotenv import load_dotenv
import sqlite3
from werkzeug.utils import secure_filename
import traceback

# 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 
from langchain_core.tools import tool
from langchain_community.agent_toolkits import SQLDatabaseToolkit
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, 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
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.utilities import SQLDatabase

# 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__))
DATABASE_URI = f"sqlite:///{os.path.join(BASE_DIR, 'data', 'mydb.db')}"
print("DATABASE URI:", DATABASE_URI)

# 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")

# Global state: dynamic agent and DB file path
agent_app = None
abs_file_path = None
db_path = None

# =============================================================================
# create_agent_app: Given a database path, initialize the agent workflow.
# =============================================================================
def create_agent_app(db_path: str):
    # Use ChatGroq as our LLM here; swap to ChatMistralAI if preferred.
    from langchain_groq import ChatGroq
    llm = ChatGroq(model="llama3-70b-8192")

    # -------------------------------------------------------------------------
    # Define a tool for executing SQL queries, with an explicit description.
    # -------------------------------------------------------------------------
    @tool(description="Executes a SQL query on the connected SQLite database and returns the result.")
    def db_query_tool(query: str) -> str:
        """
        Executes a SQL query on the connected SQLite database.
        """
        try:
            result = db_instance.run_no_throw(query)
            return result if result else "Error: Query failed. Please rewrite your query and try again."
        except Exception as e:
            return f"Error: {str(e)}"

    # -------------------------------------------------------------------------
    # Pydantic model for final answer.
    # -------------------------------------------------------------------------
    class SubmitFinalAnswer(BaseModel):
        final_answer: str = Field(..., description="The final answer to the user")

    # -------------------------------------------------------------------------
    # Define state type for our workflow.
    # -------------------------------------------------------------------------
    class State(TypedDict):
        messages: Annotated[list[AnyMessage], add_messages]

    # -------------------------------------------------------------------------
    # Set up prompt templates for query checking and generation.
    # -------------------------------------------------------------------------
    query_check_system = (
        "You are a SQL expert with a strong attention to detail.\n"
        "Double check the SQLite query for common mistakes, including:\n"
        "- Using NOT IN with NULL values\n"
        "- Using UNION when UNION ALL should have been used\n"
        "- Using BETWEEN for exclusive ranges\n"
        "- Data type mismatch in predicates\n"
        "- Properly quoting identifiers\n"
        "- Using the correct number of arguments for functions\n"
        "- Casting to the correct data type\n"
        "- Using the proper columns for joins\n\n"
        "If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n"
        "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.\n\n"
        "Given an input question, output a syntactically correct SQLite query to run, then look at the results of the query and return the answer.\n\n"
        "DO NOT call any tool besides SubmitFinalAnswer to submit the final answer.\n\n"
        "When generating the query:\n"
        "Output the SQL query that answers the input question without a tool call.\n"
        "Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.\n"
        "You can order the results by a relevant column to return the most interesting examples in the database.\n"
        "Never query for all the columns from a specific table, only ask for the relevant columns given the question.\n\n"
        "If you get an error while executing a query, rewrite the query and try again.\n"
        "If you get an empty result set, you should try to rewrite the query to get a non-empty result set.\n"
        "NEVER make stuff up if you don't have enough information to answer the query... just say you don't have enough information.\n\n"
        "If you have enough information to answer the input question, simply invoke the appropriate tool to submit the final answer to the user.\n"
        "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])

    # -------------------------------------------------------------------------
    # Update database URI, create SQLDatabase connection.
    # -------------------------------------------------------------------------
    abs_db_path_local = os.path.abspath(db_path)
    global DATABASE_URI
    DATABASE_URI = abs_db_path_local
    db_uri = f"sqlite:///{abs_db_path_local}"
    print("db_uri", db_uri)

    try:
        db_instance = SQLDatabase.from_uri(db_uri)
    except Exception as e:
        raise Exception(f"Failed to create SQLDatabase connection: {e}")
    print("db_instance----->", db_instance)

    # -------------------------------------------------------------------------
    # Create SQL toolkit.
    # -------------------------------------------------------------------------
    toolkit_instance = SQLDatabaseToolkit(db=db_instance, llm=llm)
    tools_instance = toolkit_instance.get_tools()

    # -------------------------------------------------------------------------
    # Define workflow nodes and fallback functions.
    # -------------------------------------------------------------------------
    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):
        try:
            message = query_gen.invoke(state)
        except Exception as e:
            raise Exception(f"Exception in query_gen_node: {e}")
        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) -> 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"
        return "correct_query"

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

    # -------------------------------------------------------------------------
    # Get tools for listing tables and fetching schema.
    # -------------------------------------------------------------------------
    list_tables_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_tables_tool]))
    workflow.add_node("get_schema_tool", create_tool_node_with_fallback([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")

    return workflow.compile()

# =============================================================================
# create_app: The application factory.
# =============================================================================
def create_app():
    flask_app = Flask(__name__, static_url_path='/uploads', static_folder='uploads')
    socketio = SocketIO(flask_app, cors_allowed_origins="*")

    if not os.path.exists(UPLOAD_FOLDER):
        os.makedirs(UPLOAD_FOLDER)
    flask_app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
    flask_app.config['SECRET_KEY'] = os.getenv("FLASK_SECRET_KEY", "mysecretkey")

    @flask_app.route("/files/<path:filename>")
    def uploaded_file(filename):
        try:
            return send_from_directory(flask_app.config['UPLOAD_FOLDER'], filename)
        except Exception as e:
            flash(f"Could not send file: {str(e)}", "error")
            return redirect(url_for("index"))

    def run_agent(prompt, socketio):
        global agent_app, abs_file_path, db_path
        if not abs_file_path:
            socketio.emit("log", {"message": "[ERROR]: No DB file uploaded."})
            socketio.emit("final", {"message": "No database available. Please upload one and try again."})
            return
        try:
            if agent_app is None:
                socketio.emit("log", {"message": "[INFO]: Initializing agent for the first time..."})
                agent_app = create_agent_app(abs_file_path)
                socketio.emit("log", {"message": "[INFO]: Agent initialized."})
                flash("Agent initialized.", "info")
            query = {"messages": [("user", prompt)]}
            result = agent_app.invoke(query)
            try:
                result = result["messages"][-1].tool_calls[0]["args"]["final_answer"]
            except Exception as e:
                result = "Query failed or no valid answer found."
                flash("Query failed or no valid answer found.", "warning")
            print("final_answer------>", result)
            socketio.emit("final", {"message": result})
        except Exception as e:
            error_message = f"Generation failed: {str(e)}"
            socketio.emit("log", {"message": f"[ERROR]: {error_message}"})
            socketio.emit("final", {"message": "Generation failed."})
            flash(error_message, "error")
            traceback.print_exc()

    @flask_app.route("/")
    def index():
        return render_template("index.html")

    @flask_app.route("/generate", methods=["POST"])
    def generate():
        try:
            socketio.emit("log", {"message": "[STEP]: Entering query generation..."})
            data = request.json
            prompt = data.get("prompt", "")
            socketio.emit("log", {"message": f"[INFO]: Received prompt: {prompt}"})
            thread = threading.Thread(target=run_agent, args=(prompt, socketio))
            socketio.emit("log", {"message": f"[INFO]: Starting thread: {thread}"})
            thread.start()
            flash("Query submitted successfully.", "info")
            return "OK", 200
        except Exception as e:
            error_message = f"[ERROR]: {str(e)}"
            socketio.emit("log", {"message": error_message})
            flash(error_message, "error")
            return "ERROR", 500

    @flask_app.route("/upload", methods=["GET", "POST"])
    def upload():
        global abs_file_path, agent_app, db_path
        try:
            if request.method == "POST":
                file = request.files.get("file")
                if not file:
                    flash("No file uploaded.", "error")
                    return "No file uploaded", 400
                filename = secure_filename(file.filename)
                if filename.endswith('.db'):
                    db_path = os.path.join(flask_app.config['UPLOAD_FOLDER'], "uploaded.db")
                    print("Saving file to:", db_path)
                    file.save(db_path)
                    abs_file_path = os.path.abspath(db_path)
                    agent_app = None   # Reset the agent to be lazily reinitialized on next query.
                    print(f"[INFO]: File '{filename}' uploaded. Agent will be initialized on first query.")
                    socketio.emit("log", {"message": f"[INFO]: Database file '{filename}' uploaded."})
                    flash(f"Database file '{filename}' uploaded successfully.", "info")
                    return redirect(url_for("index"))
            return render_template("upload.html")
        except Exception as e:
            error_message = f"[ERROR]: {str(e)}"
            print(error_message)
            flash(error_message, "error")
            socketio.emit("log", {"message": error_message})
            return render_template("upload.html")

    return flask_app, socketio

# =============================================================================
# Create the app for Gunicorn compatibility.
# =============================================================================
app, socketio_instance = create_app()

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