import os
import re
import asyncio
import json
import time
import logging
from typing import Any, Dict
from fastapi.staticfiles import StaticFiles
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
from fastapi.middleware.cors import CORSMiddleware
from dotenv import load_dotenv
from tenacity import RetryError
from openai import RateLimitError
from anthropic import RateLimitError as AnthropicRateLimitError
from google.api_core.exceptions import ResourceExhausted

logger = logging.getLogger()
logger.setLevel(logging.INFO)

CONTEXT_LENGTH = 128000
BUFFER = 10000
MAX_TOKENS_ALLOWED = CONTEXT_LENGTH - BUFFER

# Path to the .env file
ENV_FILE_PATH = os.getenv("WRITABLE_DIR", "/tmp") + "/.env"

# Per-session state
SESSION_STORE: Dict[str, Dict[str, Any]] = {}   

# Format error message for SSE
def format_error_sse(event_type: str, data: str) -> str:
    lines = data.splitlines()
    sse_message = f"event: {event_type}\n"
    for line in lines:
        sse_message += f"data: {line}\n"
    sse_message += "\n"
    return sse_message

# Stop the task on error (non-fastapi)
def stop_on_error():
    state = SESSION_STORE

    if "process_task" in state:
        state["process_task"].cancel()
        del state["process_task"]

# Initialize the components
def initialize_components():
    load_dotenv(ENV_FILE_PATH, override=True)

    from src.search.search_engine import SearchEngine
    from src.query_processing.query_processor import QueryProcessor
    # from src.rag.neo4j_graphrag import Neo4jGraphRAG
    from src.rag.graph_rag import GraphRAG
    from src.evaluation.evaluator import Evaluator
    from src.reasoning.reasoner import Reasoner
    from src.crawl.crawler import CustomCrawler
    from src.utils.api_key_manager import APIKeyManager
    from src.query_processing.late_chunking.late_chunker import LateChunker

    manager = APIKeyManager()
    manager._reinit()
    SESSION_STORE['search_engine'] = SearchEngine()
    SESSION_STORE['query_processor'] = QueryProcessor()
    SESSION_STORE['crawler'] = CustomCrawler(max_concurrent_requests=1000)
    # SESSION_STORE['graph_rag'] = Neo4jGraphRAG(num_workers=os.cpu_count() * 2)
    SESSION_STORE['graph_rag'] = GraphRAG(num_workers=os.cpu_count() * 2)
    SESSION_STORE['evaluator'] = Evaluator()
    SESSION_STORE['reasoner'] = Reasoner()
    SESSION_STORE['model'] = manager.get_llm()
    SESSION_STORE['late_chunker'] = LateChunker()
    SESSION_STORE["initialized"] = True
    SESSION_STORE["session_id"] = None

async def process_query(user_query: str, sse_queue: asyncio.Queue):
    state = SESSION_STORE

    try:
        category = await state["query_processor"].classify_query(user_query)
        cat_lower = category.lower().strip()

        if state["session_id"] is None:
            state["session_id"] = await state["crawler"].create_session()

        user_query = re.sub(r'category:.*', '', user_query, flags=re.IGNORECASE).strip()

        if cat_lower == "basic":
            response = ""
            chunk_counter = 1
            async for chunk in state["reasoner"].reason(user_query):
                await sse_queue.put(("token", json.dumps({"chunk": chunk, "index": chunk_counter})))
                response += chunk
                chunk_counter += 1

            await sse_queue.put(("final_message", response))
            SESSION_STORE["chat_history"].append({"query": user_query, "response": response})
            
            await sse_queue.put(("action", {
                "name": "evaluate",
                "payload": {"query": user_query, "response": response}
            }))

            await sse_queue.put(("complete", "done"))

        elif cat_lower == "advanced":
            await sse_queue.put(("step", "Searching..."))

            optimized_query = await state['search_engine'].generate_optimized_query(user_query)
            search_results = await state['search_engine'].search(
                optimized_query,
                num_results=3,
                exclude_filetypes=["pdf"]
            )

            urls = [r.get('link', 'No URL') for r in search_results]
            search_contents = await state['crawler'].fetch_page_contents(
                urls,
                user_query,
                state["session_id"],
                max_attempts=1
            )

            contents = ""
            if search_contents:
                for k, content in enumerate(search_contents, 1):
                    if isinstance(content, Exception):
                        print(f"Error fetching content: {content}")
                    elif content:
                        contents += f"Document {k}:\n{content}\n\n"

            if len(contents.strip()) > 0:
                await sse_queue.put(("step", "Generating Response..."))

                token_count = state['model'].get_num_tokens(contents)
                if token_count > MAX_TOKENS_ALLOWED:
                    contents = await state['late_chunker'].chunker(contents, user_query, MAX_TOKENS_ALLOWED)

                await sse_queue.put(("sources_read", len(search_contents)))

                response = ""
                chunk_counter = 1
                async for chunk in state["reasoner"].reason(user_query, contents):
                    await sse_queue.put(("token", json.dumps({"chunk": chunk, "index": chunk_counter})))
                    response += chunk
                    chunk_counter += 1

                await sse_queue.put(("final_message", response))
                SESSION_STORE["chat_history"].append({"query": user_query, "response": response})

                await sse_queue.put(("action", {
                    "name": "sources",
                    "payload": {"search_results": search_results, "search_contents": search_contents}
                }))
                await sse_queue.put(("action", {
                    "name": "evaluate",
                    "payload": {"query": user_query, "contents": [contents], "response": response}
                }))

                await sse_queue.put(("complete", "done"))
            else:
                await sse_queue.put(("error", "No results found."))

        elif cat_lower == "pro":
            current_search_results = []
            current_search_contents = []

            await sse_queue.put(("step", "Thinking..."))

            start = time.time()
            intent = await state['query_processor'].get_query_intent(user_query)
            sub_queries, _ = await state['query_processor'].decompose_query(user_query, intent)

            async def sub_query_task(sub_query):
                try:
                    await sse_queue.put(("step", "Searching..."))
                    await sse_queue.put(("task", (sub_query, "RUNNING")))
                    
                    optimized_query = await state['search_engine'].generate_optimized_query(sub_query)
                    search_results = await state['search_engine'].search(
                        optimized_query,
                        num_results=10,
                        exclude_filetypes=["pdf"]
                    )
                    filtered_urls = await state['search_engine'].filter_urls(
                        sub_query,
                        category,
                        search_results
                    )
                    current_search_results.extend(filtered_urls)
                    
                    urls = [r.get('link', 'No URL') for r in filtered_urls]
                    search_contents = await state['crawler'].fetch_page_contents(
                        urls,
                        sub_query,
                        state["session_id"],
                        max_attempts=1
                    )
                    current_search_contents.extend(search_contents)

                    contents = ""
                    if search_contents:
                        for k, c in enumerate(search_contents, 1):
                            if isinstance(c, Exception):
                                logger.info(f"Error fetching content: {c}")
                            elif c:
                                contents += f"Document {k}:\n{c}\n\n"

                    if len(contents.strip()) > 0:
                        await sse_queue.put(("task", (sub_query, "DONE")))
                    else:
                        await sse_queue.put(("task", (sub_query, "FAILED")))

                    return contents
                
                except (RateLimitError, ResourceExhausted, AnthropicRateLimitError, RetryError):
                    await sse_queue.put(("task", (sub_query, "FAILED")))
                    return ""

            tasks = []
            if len(sub_queries) > 1 and sub_queries[0] != user_query:
                for sub_query in sub_queries:
                    tasks.append(sub_query_task(sub_query))
                    
            results = await asyncio.gather(*tasks)
            end = time.time()

            contents = "\n\n".join(r for r in results if r.strip())

            unique_results = []
            seen = set()
            for entry in current_search_results:
                link = entry["link"]
                if link not in seen:
                    seen.add(link)
                    unique_results.append(entry)
            current_search_results = unique_results
            current_search_contents = list(set(current_search_contents))

            if len(contents.strip()) > 0:
                await sse_queue.put(("step", "Generating Response..."))

                token_count = state['model'].get_num_tokens(contents)
                if token_count > MAX_TOKENS_ALLOWED:
                    contents = await state['late_chunker'].chunker(
                        text=contents,
                        query=user_query,
                        max_tokens=MAX_TOKENS_ALLOWED
                    )
                    logger.info(f"Number of tokens in the answer: {token_count}")
                    logger.info(f"Number of tokens in the content: {state['model'].get_num_tokens(contents)}")

                await sse_queue.put(("sources_read", len(current_search_contents)))
                
                response = ""
                chunk_counter = 1
                is_first_chunk = True
                async for chunk in state['reasoner'].reason(user_query, contents):
                    if is_first_chunk:
                        await sse_queue.put(("step", f"Thought and searched for {int(end - start)} seconds"))
                        is_first_chunk = False

                    await sse_queue.put(("token", json.dumps({"chunk": chunk, "index": chunk_counter})))
                    response += chunk
                    chunk_counter += 1

                await sse_queue.put(("final_message", response))
                SESSION_STORE["chat_history"].append({"query": user_query, "response": response})

                await sse_queue.put(("action", {
                    "name": "sources",
                    "payload": {
                        "search_results": current_search_results, 
                        "search_contents": current_search_contents
                    }
                }))
                await sse_queue.put(("action", {
                    "name": "evaluate",
                    "payload": {"query": user_query, "contents": [contents], "response": response}
                }))

                await sse_queue.put(("complete", "done"))
            else:
                await sse_queue.put(("error", "No results found."))

        elif cat_lower == "super":
            current_search_results = []
            current_search_contents = []

            await sse_queue.put(("step", "Thinking..."))

            start = time.time()
            main_query_intent = await state['query_processor'].get_query_intent(user_query)
            sub_queries, _ = await state['query_processor'].decompose_query(user_query, main_query_intent)

            await sse_queue.put(("step", "Searching..."))

            async def sub_query_task(sub_query):
                try:
                    async def sub_sub_query_task(sub_sub_query):
                        optimized_query = await state['search_engine'].generate_optimized_query(sub_sub_query)
                        search_results = await state['search_engine'].search(
                            optimized_query,
                            num_results=10,
                            exclude_filetypes=["pdf"]
                        )
                        filtered_urls = await state['search_engine'].filter_urls(
                            sub_sub_query,
                            category,
                            search_results
                        )
                        current_search_results.extend(filtered_urls)

                        urls = [r.get('link', 'No URL') for r in filtered_urls]
                        search_contents = await state['crawler'].fetch_page_contents(
                            urls,
                            sub_sub_query,
                            state["session_id"],
                            max_attempts=1,
                            timeout=20
                        )
                        current_search_contents.extend(search_contents)

                        contents = ""
                        if search_contents:
                            for k, c in enumerate(search_contents, 1):
                                if isinstance(c, Exception):
                                    logger.info(f"Error fetching content: {c}")
                                elif c:
                                    contents += f"Document {k}:\n{c}\n\n"

                        return contents
                    
                    await sse_queue.put(("task", (sub_query, "RUNNING")))

                    sub_sub_queries, _ = await state['query_processor'].decompose_query(sub_query)

                    tasks = []
                    if len(sub_sub_queries) > 1 and sub_sub_queries[0] != user_query:
                        for sub_sub_query in sub_sub_queries:
                            tasks.append(sub_sub_query_task(sub_sub_query))

                    results = await asyncio.gather(*tasks)

                    if any(result.strip() for result in results):
                        await sse_queue.put(("task", (sub_query, "DONE")))
                    else:
                        await sse_queue.put(("task", (sub_query, "FAILED")))

                    return results
                
                except (RateLimitError, ResourceExhausted, AnthropicRateLimitError, RetryError):
                    await sse_queue.put(("task", (sub_query, "FAILED")))
                    return []

            tasks = []
            if len(sub_queries) > 1 and sub_queries[0] != user_query:
                for sub_query in sub_queries:
                    tasks.append(sub_query_task(sub_query))

            results = await asyncio.gather(*tasks)
            end = time.time()

            previous_contents = []
            for result in results:
                if result:
                    for content in result:
                        if isinstance(content, str) and len(content.strip()) > 0:
                            previous_contents.append(content)
            contents = "\n\n".join(previous_contents)

            unique_results = []
            seen = set()
            for entry in current_search_results:
                link = entry["link"]
                if link not in seen:
                    seen.add(link)
                    unique_results.append(entry)
            current_search_results = unique_results
            current_search_contents = list(set(current_search_contents))

            if len(contents.strip()) > 0:
                await sse_queue.put(("step", "Generating Response..."))

                token_count = state['model'].get_num_tokens(contents)
                if token_count > MAX_TOKENS_ALLOWED:
                    contents = await state['late_chunker'].chunker(
                        text=contents,
                        query=user_query,
                        max_tokens=MAX_TOKENS_ALLOWED
                    )
                    logger.info(f"Number of tokens in the answer: {token_count}")
                    logger.info(f"Number of tokens in the content: {state['model'].get_num_tokens(contents)}")

                await sse_queue.put(("sources_read", len(current_search_contents)))
                
                response = ""
                chunk_counter = 1
                is_first_chunk = True
                async for chunk in state['reasoner'].reason(user_query, contents):
                    if is_first_chunk:
                        await sse_queue.put(("step", f"Thought and searched for {int(end - start)} seconds"))
                        is_first_chunk = False

                    await sse_queue.put(("token", json.dumps({"chunk": chunk, "index": chunk_counter})))
                    response += chunk
                    chunk_counter += 1

                await sse_queue.put(("final_message", response))
                SESSION_STORE["chat_history"].append({"query": user_query, "response": response})

                await sse_queue.put(("action", {
                    "name": "sources",
                    "payload": {
                        "search_results": current_search_results, 
                        "search_contents": current_search_contents
                    }
                }))
                await sse_queue.put(("action", {
                    "name": "evaluate",
                    "payload": {"query": user_query, "contents": [contents], "response": response}
                }))

                await sse_queue.put(("complete", "done"))
            else:
                await sse_queue.put(("error", "No results found."))

        elif cat_lower == "ultra":
            current_search_results = []
            current_search_contents = []

            match = re.search(
                r"^This is the previous context of the conversation:\s*.*?\s*Current Query:\s*(.*)$", 
                user_query, 
                flags=re.DOTALL | re.MULTILINE
            )
            if match:
                user_query = match.group(1)

            await sse_queue.put(("step", "Thinking..."))
            await asyncio.sleep(0.01)   # Sleep for a short time to allow the message to be sent

            async def on_event_callback(event_type, data):
                if event_type == "graph_operation":
                    if data["operation_type"] == "creating_new_graph":
                        await sse_queue.put(("step", "Creating New Graph..."))

                    elif data["operation_type"] == "modifying_existing_graph":
                        await sse_queue.put(("step", "Modifying Existing Graph..."))

                    elif data["operation_type"] == "loading_existing_graph":
                        await sse_queue.put(("step", "Loading Existing Graph..."))

                elif event_type == "sub_query_created":
                    sub_query = data["sub_query"]
                    await sse_queue.put(("task", (sub_query, "RUNNING")))

                elif event_type == "search_process_started":
                    await sse_queue.put(("step", "Searching..."))

                elif event_type == "sub_query_processed":
                    sub_query = data["sub_query"]
                    await sse_queue.put(("task", (sub_query, "DONE")))

                elif event_type == "sub_query_failed":
                    sub_query = data["sub_query"]
                    await sse_queue.put(("task", (sub_query, "FAILED")))

                elif event_type == "search_results_filtered":
                    current_search_results.extend(data["filtered_urls"])

                    filtered_urls = data["filtered_urls"]
                    current_search_results.extend(filtered_urls)

                elif event_type == "search_contents_fetched":
                    current_search_contents.extend(data["contents"])

                    contents = data["contents"]
                    current_search_contents.extend(contents)
                
                elif event_type == "search_process_completed":
                    await sse_queue.put(("step", "Processing final graph tasks..."))
                    await asyncio.sleep(0.01)   # Sleep for a short time to allow the message to be sent

            state['graph_rag'].set_on_event_callback(on_event_callback)

            start = time.time()
            # state['graph_rag'].initialize_schema()
            await state['graph_rag'].process_graph(
                user_query,
                similarity_threshold=0.8,
                relevance_threshold=0.8,
                max_tokens_allowed=MAX_TOKENS_ALLOWED
            )
            end = time.time()

            unique_results = []
            seen = set()
            for entry in current_search_results:
                link = entry["link"]
                if link not in seen:
                    seen.add(link)
                    unique_results.append(entry)
            current_search_results = unique_results
            current_search_contents = list(set(current_search_contents))

            await sse_queue.put(("step", "Generating Response..."))

            answer = state['graph_rag'].query_graph(user_query)
            if answer:
                token_count = state['model'].get_num_tokens(answer)
                if token_count > MAX_TOKENS_ALLOWED:
                    answer = await state['late_chunker'].chunker(
                        text=answer,
                        query=user_query,
                        max_tokens=MAX_TOKENS_ALLOWED
                    )
                    logger.info(f"Number of tokens in the answer: {token_count}")
                    logger.info(f"Number of tokens in the content: {state['model'].get_num_tokens(answer)}")

                await sse_queue.put(("sources_read", len(current_search_contents)))

                response = ""
                chunk_counter = 1
                is_first_chunk = True
                async for chunk in state['reasoner'].reason(user_query, answer):
                    if is_first_chunk:
                        await sse_queue.put(("step", f"Thought and searched for {int(end - start)} seconds"))
                        is_first_chunk = False

                    await sse_queue.put(("token", json.dumps({"chunk": chunk, "index": chunk_counter})))
                    response += chunk
                    chunk_counter += 1

                await sse_queue.put(("final_message", response))
                SESSION_STORE["chat_history"].append({"query": user_query, "response": response})

                await sse_queue.put(("action", {
                    "name": "sources",
                    "payload": {"search_results": current_search_results, "search_contents": current_search_contents},
                }))
                await sse_queue.put(("action", {
                    "name": "graph",
                    "payload": {"query": user_query},
                }))
                await sse_queue.put(("action", {
                    "name": "evaluate",
                    "payload": {"query": user_query, "contents": [answer], "response": response},
                }))

                await sse_queue.put(("complete", "done"))
            else:
                await sse_queue.put(("error", "No results found."))

        else:
            await sse_queue.put(("final_message", "I'm not sure how to handle your query."))

    except Exception as e:
        await sse_queue.put(("error", str(e)))
        stop()

# Create a FastAPI app
app = FastAPI()

# Define allowed origins
origins = [
    "http://localhost:3000",
    "http://localhost:7860"
    "http://localhost:8000",
    "http://localhost"
]

# Add the CORS middleware to your FastAPI app
app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,  # Allows only these origins
    allow_credentials=True,
    allow_methods=["*"],    # Allows all HTTP methods (GET, POST, etc.)
    allow_headers=["*"],    # Allows all headers
)

# Serve the React app (the production build) at the root URL.
app.mount("/static", StaticFiles(directory="frontend/build/static", html=True), name="static")

# Define the routes for the FastAPI app

# Define the route for sources action to display search results
@app.post("/action/sources")
def action_sources(payload: Dict[str, Any]) -> Dict[str, Any]:
    try:
        search_contents = payload.get("search_contents", [])
        search_results = payload.get("search_results", [])
        sources = []
        word_limit = 15  # Maximum number of words for the description

        for result, contents in zip(search_results, search_contents):
            if contents:
                title = result.get('title', 'No Title')
                link = result.get('link', 'No URL')
                snippet = result.get('snippet', 'No snippet')
                cleaned = re.sub(r'<[^>]+>|\[\/?.*?\]', '', snippet)
                
                words = cleaned.split()
                if len(words) > word_limit:
                    description = " ".join(words[:word_limit]) + "..."
                else:
                    description = " ".join(words)
                
                source_obj = {
                    "title": title,
                    "link": link,
                    "description": description
                }
                sources.append(source_obj)

        return {"result": sources}
    except Exception as e:
        return JSONResponse(content={"error": str(e)}, status_code=500)

# Define the route for graph action to display the graph
@app.post("/action/graph")
def action_graph() -> Dict[str, Any]:
    state = SESSION_STORE
    try:
        html_str = state['graph_rag'].display_graph()

        return {"result": html_str}
    except Exception as e:
        return JSONResponse(content={"error": str(e)}, status_code=500)

# Define the route for evaluate action to display evaluation results
@app.post("/action/evaluate")
async def action_evaluate(payload: Dict[str, Any]) -> Dict[str, Any]:
    try:
        query = payload.get("query", "")
        contents = payload.get("contents", [])
        response = payload.get("response", "")
        metrics = payload.get("metrics", [])

        state = SESSION_STORE
        evaluator = state["evaluator"]
        result = await evaluator.evaluate_response(query, response, contents, include_metrics=metrics)
        
        return {"result": result}
    except Exception as e:
        return JSONResponse(content={"error": str(e)}, status_code=500)

@app.post("/settings")
async def update_settings(data: Dict[str, Any]):
    from src.helpers.helper import (
        prepare_provider_key_updates,
        prepare_proxy_list_updates,
        update_env_vars
    )
    
    provider = data.get("Model_Provider", "").strip()
    model_name = data.get("Model_Name", "").strip()
    multiple_api_keys = data.get("Model_API_Keys", "").strip()
    brave_api_key = data.get("Brave_Search_API_Key", "").strip()
    proxy_list = data.get("Proxy_List", "").strip()
    # neo4j_url = data.get("Neo4j_URL", "").strip()
    # neo4j_username = data.get("Neo4j_Username", "").strip()
    # neo4j_password = data.get("Neo4j_Password", "").strip()
    model_temperature = str(data.get("Model_Temperature", 0.0))
    model_top_p = str(data.get("Model_Top_P", 1.0))

    prov_lower = provider.lower()
    key_updates = prepare_provider_key_updates(prov_lower, multiple_api_keys)
    env_updates = {}
    env_updates.update(key_updates)
    px = prepare_proxy_list_updates(proxy_list)

    if px: 
        env_updates.update(px)

    env_updates["BRAVE_API_KEY"] = brave_api_key
    # env_updates["NEO4J_URI"] = neo4j_url
    # env_updates["NEO4J_USER"] = neo4j_username
    # env_updates["NEO4J_PASSWORD"] = neo4j_password
    env_updates["MODEL_PROVIDER"] = prov_lower
    env_updates["MODEL_NAME"] = model_name
    env_updates["MODEL_TEMPERATURE"] = model_temperature
    env_updates["MODEL_TOP_P"] = model_top_p

    update_env_vars(env_updates)
    load_dotenv(override=True)
    initialize_components()

    return {"success": True}

@app.on_event("startup")
def init_chat():
    if not SESSION_STORE:
        print("Initializing chat...")

        SESSION_STORE["settings_saved"] = False
        SESSION_STORE["session_id"] = None
        SESSION_STORE["chat_history"] = []

        print("Chat initialized!")

        return {"sucess": True}
    else:
        print("Chat already initialized!")
        return {"success": False}

@app.get("/message-sse")
async def sse_message(request: Request, user_message: str):
    state = SESSION_STORE
    sse_queue = asyncio.Queue()

    async def event_generator():
        # Build the prompt
        context = state["chat_history"][-3:]
        if context:
            prompt = \
f"""This is the previous context of the conversation:
{context}

Current Query:
{user_message}"""
        else:
            prompt = user_message  

        task = asyncio.create_task(process_query(prompt, sse_queue))
        state["process_task"] = task

        while True:
            if await request.is_disconnected():
                task.cancel()
                break
            try:
                event_type, data = await asyncio.wait_for(sse_queue.get(), timeout=5)

                if event_type == "token":
                    yield f"event: token\ndata: {data}\n\n"

                elif event_type == "final_message":
                    yield f"event: final_message\ndata: {data}\n\n"

                elif event_type == "error":
                    stop_on_error()
                    yield format_error_sse("error", data)

                elif event_type == "step":
                    yield f"event: step\ndata: {data}\n\n"

                elif event_type == "task":
                    subq, status = data
                    j = {"task": subq, "status": status}
                    yield f"event: task\ndata: {json.dumps(j)}\n\n"

                elif event_type == "sources_read":
                    yield f"event: sources_read\ndata: {data}\n\n"

                elif event_type == "action":
                    yield f"event: action\ndata: {json.dumps(data)}\n\n"

                elif event_type == "complete":
                    yield f"event: complete\ndata: {data}\n\n"
                    break

                else:
                    yield f"event: message\ndata: {data}\n\n"

            except asyncio.TimeoutError:
                if task.done():
                    break
                continue

            except asyncio.CancelledError:
                break

        if not task.done():
            task.cancel()

        if "process_task" in state:
            del state["process_task"]

    return StreamingResponse(event_generator(), media_type="text/event-stream")

@app.post("/stop")
def stop():
    state = SESSION_STORE

    if "process_task" in state:
        state["process_task"].cancel()
        del state["process_task"]

    return {"message": "Stopped task manually"}

# Catch-all route for frontend paths.
@app.get("/{full_path:path}")
async def serve_frontend(full_path: str, request: Request):    
    index_path = os.path.join("frontend", "build", "index.html")
    if not os.path.exists(index_path):
        raise HTTPException(status_code=500, detail="Frontend build not found")
    return FileResponse(index_path)