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#─── Basic imports ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
import os
import math
import sqlite3
import fitz  # PyMuPDF for PDF parsing
from flask_socketio import SocketIO

# ─── Langchain Frameworks ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
from langchain.tools import Tool
from langchain.chat_models import ChatOpenAI
from langchain_groq import ChatGroq
from langchain_mistralai import ChatMistralAI
from langchain.agents import initialize_agent, AgentType
from langchain.schema import Document
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.prompts import PromptTemplate
from langchain_community.document_loaders import TextLoader, PyMuPDFLoader
# taking global variables from the app.py file
#from app import DB_PATH, DOC_PATH, IMG_PATH, OTH_PATH

# ─── File paths ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
import config
# Ensure this is at the very top

# ─── SQL Agent ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain.chat_models import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langchain.agents import create_sql_agent

# ─── Memory ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
from langchain.memory import ConversationBufferMemory
from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool
from typing import List, Callable
from langchain.memory import ConversationBufferMemory
from langchain.schema import BaseMemory, AIMessage, HumanMessage, SystemMessage
from langchain.llms.base import LLM
from langchain.memory.chat_memory import BaseChatMemory
from pydantic import PrivateAttr
from langchain_core.messages import get_buffer_string

# 1) Create your memory object
from typing import List
from langchain.memory import ConversationBufferMemory
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain.llms.base import LLM
from langchain.memory.chat_memory import BaseChatMemory
from pydantic import PrivateAttr

class AutoSummaryMemory(ConversationBufferMemory):
    _llm: LLM = PrivateAttr()
    _max_entries: int = PrivateAttr()
    _reduce_to: int = PrivateAttr()
    _summary_system_prompt: str = PrivateAttr()

    def __init__(
        self,
        llm: LLM,
        memory_key: str = "chat_history",
        return_messages: bool = True,
        max_entries: int = 20,
        reduce_to: int = 5,
        summary_system_prompt: str = (
            "Summarize the following conversation so far in a concise paragraph. "
            "Keep important facts and questions."
        )
    ):
        super().__init__(memory_key=memory_key, return_messages=return_messages)
        self._llm = llm  # PrivateAttr
        self._max_entries = max_entries  # PrivateAttr
        self._reduce_to = reduce_to  # PrivateAttr
        self._summary_system_prompt = summary_system_prompt  # PrivateAttr

    def add_memory(self, inputs: dict, outputs: dict) -> None:
        # Add the new turn as normal
        super().add_memory(inputs=inputs, outputs=outputs)
        
        # Check if memory length exceeded
        msgs = self.chat_memory.messages
        if len(msgs) >= self._max_entries:
            full_text = "\n".join([f"{m.type}: {m.content}" for m in msgs])
            summary = self._llm.predict(f"{self._summary_system_prompt}\n\n{full_text}")

            recent = msgs[-self._reduce_to:]
            self.chat_memory.messages = [
                SystemMessage(content="Conversation summary: " + summary),
                *recent
            ]
            
            
# ─── Image Processing ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
 
from PIL import Image
import pytesseract
from transformers import pipeline
from groq import Groq
import config
import requests
from io import BytesIO
from PIL import Image
from transformers import pipeline, TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests
from io import BytesIO
import base64
from PIL import UnidentifiedImageError

# ─── Browser var ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
from typing import List, Dict
import json
from io import BytesIO
from langchain.tools import tool  # or langchain_core.tools
from playwright.sync_api import sync_playwright
from duckduckgo_search import DDGS
from bs4 import BeautifulSoup
import requests



from playwright.sync_api import sync_playwright
# Attempt to import Playwright for dynamic page rendering
try:
    from playwright.sync_api import sync_playwright
    _playwright_available = True
except ImportError:
    _playwright_available = False

# Define forbidden keywords for basic NSFW filtering
_forbidden = ["porn", "sex", "xxx", "nude", "erotic"]


# ─── LLM Setup ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────


# Load OpenAI API key from environment (required for LLM and embeddings)
import os

# API Keys from .env file
os.environ.setdefault("OPENAI_API_KEY", "<YOUR_OPENAI_KEY>")  # Set your own key or env var
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY", "default_key_or_placeholder")
os.environ["MISTRAL_API_KEY"] = os.getenv("MISTRAL_API_KEY", "default_key_or_placeholder")

# Tavily API Key
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY", "default_key_or_placeholder")
_forbidden = ["nsfw", "porn", "sex", "explicit"]
_playwright_available = True  # set False to disable Playwright

# Globals for RAG system
vector_store = None
rag_chain = None
DB_PATH = None  # will be set when a .db is uploaded
DOC_PATH = None  # will be set when a document is uploaded
IMG_PATH = None  # will be set when an image is uploaded
OTH_PATH = None  # will be set when an other file is uploaded


# ─── LLMS ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#llm = ChatOpenAI(model_name="gpt-3.5-turbo", streaming=True, temperature=0)
llm = ChatGroq(model="meta-llama/llama-4-maverick-17b-128e-instruct", streaming=True, temperature=0)
#llm = ChatMistralAI(model="mistral-large-latest", streaming=True, temperature=0)


# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Tool for browsing ────────────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

def tavily_search(query: str, top_k: int = 3) -> List[Dict]:
    """Call Tavily API and return a list of result dicts."""
    if not TAVILY_API_KEY:
        print("[Tavily] No API key set. Skipping Tavily search.")
        return []
    url = "https://api.tavily.com/search"
    headers = {
        "Authorization": f"Bearer {TAVILY_API_KEY}",
        "Content-Type": "application/json",
    }
    payload = {"query": query, "num_results": top_k}
    try:
        resp = requests.post(url, headers=headers, json=payload, timeout=10)
        resp.raise_for_status()
        data = resp.json()
        results = []
        for item in data.get("results", []):
            results.append({
                "title": item.get("title", ""),
                "url": item.get("url", ""),
                "snippet": item.get("content", "")[:200],
                "source": "Tavily"
            })
        return results
    except (requests.exceptions.RequestException, ValueError) as e:
        print(f"[Tavily] search failed: {e}")
        return []

def duckduckgo_search(query: str, top_k: int = 3) -> List[Dict]:
    """Query DuckDuckGo and return up to top_k raw SERP hits."""
    try:
        results = []
        with DDGS() as ddgs:
            for hit in ddgs.text(query, safesearch="On", max_results=top_k):
                results.append({
                    "title": hit.get("title", ""),
                    "url": hit.get("href") or hit.get("url", ""),
                    "snippet": hit.get("body", ""),
                    "source": "DuckDuckGo"
                })
                if len(results) >= top_k:
                    break
        return results
    except Exception as e:
        print(f"[DuckDuckGo] search failed: {e}")
        return []

def hybrid_web_search(query: str, top_k: int = 3) -> str:
    """
    Returns a JSON string with combined Tavily + DuckDuckGo results.
    Always returns non-empty JSON with at least a placeholder result.
    """
    tavily = tavily_search(query, top_k)
    ddg = duckduckgo_search(query, top_k)
    combined = tavily + ddg

    # Always return at least a message to avoid agent crashes
    if not combined:
        combined = [{
            "title": "No results found",
            "url": "",
            "snippet": f"Could not find suitable web results for '{query}'.",
            "source": "None"
        }]
    output = {"query": query, "results": combined}
    return json.dumps(output, ensure_ascii=False, indent=2)

def web_search(query: str, top_k: int = 3) -> str:
    """
    Full hybrid search with Playwright/BeautifulSoup scraping + Tavily/DuckDuckGo.
    Always returns valid JSON output.
    """
    results: List[Dict] = []

    # Step 1: DuckDuckGo + scraping
    try:
        with DDGS() as ddgs:
            hits = ddgs.text(query, safesearch="On", max_results=top_k)
    except Exception as e:
        print(f"[web_search] DuckDuckGo lookup failed: {e}")
        hits = []

    for hit in hits:
        url = hit.get("href") or hit.get("url")
        if not url:
            continue

        try:
            with sync_playwright() as pw:
                browser = pw.chromium.launch(headless=True)
                page = browser.new_page()
                page.goto(url, wait_until="domcontentloaded", timeout=15000)
                html = page.content()
                browser.close()
            soup = BeautifulSoup(html, "html.parser")
            text = soup.get_text(separator=" ", strip=True)
        except Exception as e:
            print(f"[web_search] scraping failed for {url}: {e}")
            continue

        if any(f in text.lower() for f in _forbidden):
            continue

        excerpt = " ".join(text.split()[:200])
        results.append({
            "title": hit.get("title", ""),
            "url": url,
            "snippet": hit.get("body", ""),
            "content": excerpt
        })

    # Step 2: Parse hybrid Tavily + DDG JSON into list
    try:
        raw = hybrid_web_search(query, top_k)
        parsed = json.loads(raw)
        other = parsed.get("results", [])
    except Exception as e:
        print(f"[web_search] parsing hybrid results failed: {e}")
        other = []

    # Step 3: Combine and return
    combined = results + other
    if not combined:
        combined = [{
            "title": "No results found",
            "url": "",
            "snippet": f"Could not find suitable content for '{query}'.",
            "source": "None"
        }]

    output = {
        "query": query,
        "sources_count": len(combined),
        "results": combined,
        "sources": list({item.get("url", "") for item in combined if item.get("url")})
    }
    return json.dumps(output, ensure_ascii=False, indent=2)

# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Tool for calculation ─────────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

def calculate(expr: str) -> str:
    """
    Evaluates a mathematical expression safely.
    Uses Python's numexpr for security and speed:contentReference[oaicite:21]{index=21}.
    """
    try:
        # Allow math constants
        local_dict = {"pi": math.pi, "e": math.e}
        # Evaluate expression using numexpr for safety/performance
        import numexpr
        result = numexpr.evaluate(expr, local_dict=local_dict)
        return str(result.item())
    except Exception as e:
        return f"Error calculating expression: {e}"
    
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Tool for Date and time ───────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

def get_current_date(_: str = "") -> str:
    """
    Returns the current date and time. Ignoring input.
    """
    from datetime import datetime
    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Tool for SQL Database ────────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────



def create_sql_agent_function(db_uri: str, top_k: int = 5):
    """
    Creates a full-fledged SQL agent function that can answer natural language questions over a SQL database.

    Args:
        db_uri (str): The SQLAlchemy database URI, e.g. "sqlite:///Chinook.db"
        top_k (int): Number of rows to limit in results (default 5)

    Returns:
        agent_executor: LangChain agent that can .run() or .stream()
    """

    # 1) Initialize the database + LLM + toolkit
    db = SQLDatabase.from_uri(db_uri)
    llm = ChatGroq(model="meta-llama/llama-4-maverick-17b-128e-instruct", streaming=False, temperature=0)
    toolkit = SQLDatabaseToolkit(db=db, llm=llm)

    # 2) Prompt with all required variables declared AND used
    prompt = PromptTemplate(
        template="""
        You are an agent designed to interact with a SQL database.
        Given the user question below, first generate a syntactically correct {dialect} query.
        Then look at the results of that query, and return the answer.
        Always limit to at most {top_k} rows unless the user specifies otherwise.
        If you encounter an error, rewrite your SQL and retry.
        DO NOT issue any INSERT/UPDATE/DELETE/DROP/ statements.
        DO NOT try to create new database tables or columns when user has not asked for.
        Always inspect the schema before querying.

        Available tools: {tools}
        Tool names: {tool_names}

        User question: {input}

        {agent_scratchpad}
        """.strip(),
                input_variables=["input", "dialect", "top_k", "agent_scratchpad", "tools", "tool_names"],
            )

    # 3) Create the agent with prompt + toolkit tools
    agent_executor = create_sql_agent(
        llm=llm,
        toolkit=toolkit,
        prompt=prompt,
        verbose=False,
        # pass top_k dynamically
        extra_prompt_kwargs={"top_k": str(top_k), "dialect": db.dialect},
    )

    return agent_executor

def execute_sql(query: str) -> str:
    """
    Executes a SQL query against the uploaded SQLite DB (GLOBAL_DB_PATH).
    Returns a string of results or error.
    """
    if DB_PATH is None:
        return "No database uploaded. Please upload a SQLite file first."
    
    print("DB_PATH--------->:", DB_PATH)
    
    db_uri = f"sqlite:///{DB_PATH}"
    agent_executor2 = create_sql_agent_function(db_uri, top_k=5)
  
    try:
        result = agent_executor2.run(query)
    except Exception as e:
        result = f"Agent / SQL error: {e}"
    return result
    
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Tool for RAG (Document Intelligence) ─────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    
def rag_index_document(DOC_PATH: str) -> str:
    """
    Indexes the given document into the RAG vector store.
    Supports text files or PDFs. Uses recursive text splitting for better chunking.
    """
    global vector_store, rag_chain
    text = ""
    
    # Read text from file
    if DOC_PATH and DOC_PATH.lower().endswith(".pdf"):
        doc = fitz.open(DOC_PATH)
        for page in doc:
            text += page.get_text()
    else:
        with open(DOC_PATH, 'r', encoding='utf-8') as f:
            text = f.read()
    
    # Split text using recursive text splitter
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,  # You can adjust this (e.g., 500-1000)
        chunk_overlap=100  # Overlap for better context between chunks
    )
    
    # Split into chunks
    texts = text_splitter.split_text(text)
    
    # Create Document objects with metadata
    docs = [Document(page_content=t, metadata={"source": DOC_PATH}) for t in texts]
    
    # Initialize or append to FAISS vector store
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
    
    if vector_store is None:
        vector_store = FAISS.from_documents(docs, embeddings)
    else:
        vector_store.add_documents(docs)
        
    retriever = vector_store.as_retriever(
        search_type="mmr",
        search_kwargs={
            "k": 10,
            "fetch_k": 10,
            "lambda_mult": 0.25
        }
    )
    
    # Build or update the RetrievalQA chain
    rag_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=False
    )


def rag_answer(query: str) -> str:
    """
    Answers a question using the RAG chain (on indexed documents).
    """
    global rag_chain
    if rag_chain is None:
        return "No documents indexed. Please upload documents via /upload_doc."
    try:
        answer = rag_chain.run(query)
        return answer
    except Exception as e:
        return f"RAG error: {e}"

# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ───────────────────────────────────── Tool for Image (understading, captioning & classification) ─────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

# Vision tools and functions  
# Load image function
# def _load_image():
#     try:
#         if IMG_PATH.startswith("http"):
#             res = requests.get(IMG_PATH)
#             res.raise_for_status()
#             img = Image.open(BytesIO(res.content))
#         else:
#             img = Image.open(IMG_PATH)
#         return img.convert("RGB")
#     except Exception as e:
#         raise RuntimeError(f"Failed to load image: {e}")


def _load_image(resize_to=(512, 512)):
    """
    Load and resize the image from IMG_PATH.
    If the image is not valid, raise an error.
    """    
    try:
        if IMG_PATH is None:
            raise ValueError("No image uploaded. Please upload an image first.")
            #return "No image uploaded. Please upload an image first."
        with open(IMG_PATH, "rb") as f:
            img = Image.open(f)
            img.verify()  # Verify it's an image
        img = Image.open(IMG_PATH).convert("RGB")  # Reopen after verify and convert
        img = img.resize(resize_to)  # resize image to reduce token size
        return img
    except UnidentifiedImageError:
        raise ValueError(f"File at {IMG_PATH} is not a valid image.")
    except Exception as e:
        raise ValueError(f"Failed to load image at {IMG_PATH}: {str(e)}")

def _encode_image_to_base64():
    img = _load_image()
    buffer = BytesIO()
    img.save(buffer, format="PNG", optimize=True)  # save optimized PNG
    return base64.b64encode(buffer.getvalue()).decode("utf-8")

def _call_llama_llm(prompt_text: str) -> str:
    b64 = _encode_image_to_base64()
    message = HumanMessage(
        content=[
            {"type": "text", "text": prompt_text},
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/png;base64,{b64}"
                }
            }
        ]
    )
    response = llm.invoke([message])
    return response.content.strip()

def vision_query(task_prompt: str) -> str:
    try:
        return _call_llama_llm(task_prompt)
    except Exception as llama_error:
        print(f"[LLaMA-4V failed] {llama_error}")
        try:
            img = _load_image()
            return pytesseract.image_to_string(img).strip()
        except Exception as ocr_error:
            print(f"[OCR fallback failed] {ocr_error}")
            return "Unable to process the image or image is not uploaded. Please try again with a different input."
    
#### Create LangChain Tools ####
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Assigning tools as list ──────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

tool_list = [
    Tool(name="browse", func=web_search, description="Search the web and scrape top results. Uses DuckDuckGo (safe mode) for query. Prefers Playwright for loading pages, with requests/BeautifulSoup as fallback. Filters out any explicit content. Returns JSON with titles, URLs, and page text."),
    Tool(name="calculate", func=calculate, description="Perform math calculations safely."),
    Tool(name="date", func=get_current_date, description="Fetch the current date and time."),
    Tool(name="sql", func=execute_sql, description="Execute SQL query on the uploaded database."),
    Tool(name="rag", func=rag_answer, description="Answer questions using the uploaded documents with retrieval-augmented generation (RAG)."),
    Tool(
    name="vision",
    func=vision_query,
    description=(
        "Perform any image-understanding task—e.g. read text, classify objects, "
        "generate captions, count or locate items, answer questions about the scene, "
        "detect NSFW content, etc.—powered by LLaMA 4-Vision. "
        "If the request is OCR-style and LLaMA fails, it falls back to Tesseract OCR."
    ),
    ),
]

# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Added Memory to Agent ────────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

# 1) instantiate with your LLM
memory = AutoSummaryMemory(
    llm=llm,
    max_entries=20,     # when chat ≥20 messages, trigger summary
    reduce_to=5         # keep only last 5 after summarizing
)

# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Initialize Agent ─────────────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

# Initialize the agent with OpenAI and our tools. We use a zero-shot-react-description agent.
agent_executor = initialize_agent(
    tools=tool_list,
    llm=llm,
    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
    memory=memory,
    verbose=True,
    handle_parsing_errors=True,
    #max_iterations=10,
)

# ─── Streaming & Fallback ─────────────────────────────────────────────────────
# ─── Streaming helper ────────────────────────────────────────────────────────────
def run_stream(query: str, data_paths: List[str] = None):
    """
    Progressive token‐by‐token streaming from the agent.

    Args:
      query: The user’s natural-language question.
      data_paths: List of file paths (DB_PATH, DOC_PATH, IMG_PATH, OTH_PATH).
    """
    # If no explicit list passed, rebuild from module globals
    # if not data_paths:
    #     data_paths = [DB_PATH, DOC_PATH, IMG_PATH, OTH_PATH]
    data_paths = [p for p in data_paths if p]
    print(f"Data paths----------------->: {data_paths}")
    # Re-inject each into the appropriate global (optional—keeps them current)
    for path in data_paths:
        ext = os.path.splitext(path)[1].lower()
        if ext in {".png", ".jpg", ".jpeg", ".gif"}:
            globals()['IMG_PATH'] = path
        elif ext in {".pdf", ".txt", ".doc", ".docx"}:
            globals()['DOC_PATH'] = path
        elif ext in {".db", ".sqlite"}:
            globals()['DB_PATH'] = path
        else:
            globals()['OTH_PATH'] = path

    # Stream the agent response
    hist = get_buffer_string(memory.chat_memory.messages)
    print("Memory now contains:", memory.chat_memory.messages)
    for chunk in agent_executor.stream({"input": query}):
        text = chunk.get("text")
        if text:
            yield text
            
# # ─── Streaming & Fallback ─────────────────────────────────────────────────────
# def run_stream(query: str, data: str = None):
#     """
#     Progressive token‐by‐token streaming from the agent.

#     Args:
#       query: The user’s natural-language question.
#       data: Path to a single uploaded file (image, document, or database).
#             We will inspect its extension and set the appropriate config variable:
#               .png/.jpg/.jpeg/.gif → IMG_PATH
#               .pdf/.txt/.doc/.docx   → DOC_PATH
#               .db/.sqlite            → DB_PATH
#               others                 → OTH_PATH
#     """
#     global DB_PATH, DOC_PATH, IMG_PATH, OTH_PATH
#     # 1) If data provided, dispatch into the right config variable
#     if data:
#         ext = os.path.splitext(data)[1].lower()
#         if ext in {".png", ".jpg", ".jpeg", ".gif"}:
#             IMG_PATH = data
#             print(f"Image path set to: {IMG_PATH}")
#         elif ext in {".pdf", ".txt", ".doc", ".docx"}:
#             DOC_PATH = data
#             print(f"Document path set to: {DOC_PATH}")
#         elif ext in {".db", ".sqlite"}:
#             DB_PATH = data
#             print(f"Database path set to: {DB_PATH}")
#         else:
#             OTH_PATH = data
#             print(f"Other file path set to: {OTH_PATH}")

#     # 2) Stream the agent’s response
#     for chunk in agent_executor.stream({"input": query}):
#         text = chunk.get("text")
#         if text:
#             yield text

def run_full(query: str) -> str:
    """
    Fallback single‐shot answer (for pure-tool or final completeness).
    """
    return agent_executor.run(query)

# Expose for Flask
class AgentInterface:
    def __init__(self, executor):
        self.executor = executor
    def run_stream(self, q):
        return run_stream(q)
    def run_full(self, q):
        return run_full(q)

agent = AgentInterface(agent_executor)

__all__ = [
    'agent_executor', 'run_stream', 'run_full',
    'AgentInterface', 'GLOBAL_DB_PATH', 'rag_index_document'
]

# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# ─────────────────────────────────────────────── Refresh Memory Session ───────────────────────────────────────────────────────────────────
# ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# Refresh Memory 
def refresh_memory():
    memory.clear()  # clear memory at start of each new session
    memory.chat_memory.clear()  # clear chat history