opencompletion.com/app.py

# import eventlet
# eventlet.monkey_patch()

import gevent
from gevent import monkey

monkey.patch_all()


import json
import yaml
import os

import random

import boto3
import requests
import together

# Unsandbox SDK for code execution
import un
from flask import (
    Flask,
    render_template,
    request,
    send_from_directory,
    jsonify,
    Response,
    redirect,
    url_for,
    session,
)

from flask_socketio import SocketIO, emit, join_room, leave_room

from flask_sqlalchemy import SQLAlchemy
from sqlalchemy.exc import InvalidRequestError

from models import db, Room, UserSession, Message, ActivityState, User, OTPToken

app = Flask(__name__, instance_relative_config=True)

app.config["SECRET_KEY"] = os.environ.get("SECRET_KEY", "dev-key-change-in-production")
app.config["SQLALCHEMY_DATABASE_URI"] = (
    f"sqlite:///{os.path.join(app.instance_path, 'chat.db')}"
)
app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False
# Enable template auto-reload to prevent stale templates during development
app.config["TEMPLATES_AUTO_RELOAD"] = True

db.init_app(app)

from flask_migrate import Migrate

migrate = Migrate(app, db)

# socketio = SocketIO(app, async_mode="eventlet")
socketio = SocketIO(app, async_mode="gevent")

# Global dictionary to keep track of cancellation requests
cancellation_requests = {}

from openai import OpenAI
import activity
import auth


# Build a list of endpoints dynamically.
ENDPOINTS = []
MAX_ENDPOINTS = 1000

for i in range(MAX_ENDPOINTS):
    endpoint = os.environ.get(f"MODEL_ENDPOINT_{i}")
    if not endpoint:
        continue
    # API key is optional; if not provided, use a default.
    api_key = os.environ.get(f"MODEL_API_KEY_{i}", "not-needed")
    ENDPOINTS.append(
        {
            "base_url": endpoint,
            "api_key": api_key,
        }
    )

if not ENDPOINTS:
    raise Exception("No MODEL_ENDPOINT_x environment variables found!")

# Build a dynamic model map by querying each endpoint.
MODEL_CLIENT_MAP = {}
SYSTEM_USERS = []


def get_client_for_endpoint(endpoint, api_key):
    # All providers use the OpenAI client; no endpoint URLs are hardcoded here.
    return OpenAI(api_key=api_key, base_url=endpoint)


VISION_MODELS = []  # Populated at startup with available vision models


def is_vision_model(model_name: str) -> bool:
    """Check if a model supports vision/image input."""
    if not model_name:
        return False
    model_lower = model_name.lower()
    vision_indicators = ["-vl", "vl:", "vision", "gpt-4o", "gpt-4-turbo"]
    return any(indicator in model_lower for indicator in vision_indicators)


def initialize_model_map():
    global SYSTEM_USERS
    MODEL_CLIENT_MAP.clear()
    VISION_MODELS.clear()
    for ep_config in ENDPOINTS:
        base_url = ep_config["base_url"]
        api_key = ep_config["api_key"]
        client = get_client_for_endpoint(base_url, api_key)
        try:
            response = client.models.list()
            model_list = response.data  # Assume each model object has an 'id' attribute
            print(f"[DEBUG] {base_url} returned models: {[m.id for m in model_list]}")
        except Exception as e:
            print(f"[WARN] Could not list models for endpoint '{base_url}': {e}")
            continue

        for m in model_list:
            model_id = m.id
            if model_id and model_id not in MODEL_CLIENT_MAP:
                MODEL_CLIENT_MAP[model_id] = (client, base_url)

    # Populate SYSTEM_USERS with dynamically loaded models.
    SYSTEM_USERS = list(MODEL_CLIENT_MAP.keys()) + ["system"]  # Include "system" for fetched images
    print("Loaded models:", list(MODEL_CLIENT_MAP.keys()))

    # Detect and track available vision models
    for model_id in MODEL_CLIENT_MAP.keys():
        if is_vision_model(model_id):
            VISION_MODELS.append(model_id)
    if VISION_MODELS:
        print(f"Vision models available: {VISION_MODELS}")
    else:
        print("No vision models available")


if MODEL_CLIENT_MAP:
    pass
else:
    initialize_model_map()


# 4) Lookup function: get an OpenAI client for a given model name
def get_client_for_model(model_name: str):
    """
    If the model name is known, return its dedicated client.
    Otherwise, return None and LOL at the user when everything breaks.
    """
    if model_name in MODEL_CLIENT_MAP:
        print(f"Completion Endpoint Processing: {MODEL_CLIENT_MAP[model_name][1]}")
        return MODEL_CLIENT_MAP[model_name][0]


def extract_base64_from_img_tag(content: str) -> tuple[str, str] | None:
    """Extract base64 data and media type from an HTML img tag.

    Returns (media_type, base64_data) or None if not found.
    """
    import re
    # Match data:image/TYPE;base64,DATA patterns in img src
    pattern = r'<img[^>]*src="data:image/(jpeg|png|gif|webp);base64,([^"]+)"'
    match = re.search(pattern, content)
    if match:
        media_type = f"image/{match.group(1)}"
        base64_data = match.group(2)
        return (media_type, base64_data)
    return None


def extract_external_image_url(content: str) -> str | None:
    """Extract external image URL from an HTML img tag.

    Returns the URL or None if not found.
    """
    import re
    # Match external URLs in img src (http/https)
    pattern = r'<img[^>]*src="(https?://[^"]+)"'
    match = re.search(pattern, content)
    if match:
        return match.group(1)
    return None


# Cache for fetched external images (URL -> base64 data URL)
_external_image_cache = {}

# CORS proxy for ethical fetching (respects robots.txt)
CORS_PROXY_URL = "https://cors-proxy.uncloseai.com/api/fetch"


def escape_like_pattern(s: str) -> str:
    """Escape special characters for SQL LIKE patterns."""
    # Escape %, _, and \ which have special meaning in LIKE
    return s.replace('\\', '\\\\').replace('%', '\\%').replace('_', '\\_')


def find_saved_base64_for_url(external_url: str, room_id: int) -> str | None:
    """Look up if we've already saved a base64 version of this URL in this room.

    Returns the data URL if found, None otherwise.
    """
    # Check in-memory cache first
    if external_url in _external_image_cache:
        return _external_image_cache[external_url]

    # Look for a saved message with this URL in the alt text
    try:
        # Escape special LIKE characters in URL (%, _, \)
        escaped_url = escape_like_pattern(external_url)
        # Search for messages containing "Fetched from {external_url}"
        saved_msg = Message.query.filter(
            Message.room_id == room_id,
            Message.content.like(f'%alt="Fetched from {escaped_url}"%', escape='\\')
        ).first()

        if saved_msg:
            # Extract base64 from the saved message
            img_data = extract_base64_from_img_tag(saved_msg.content)
            if img_data:
                media_type, base64_data = img_data
                data_url = f"data:{media_type};base64,{base64_data}"
                # Add to memory cache for faster lookups
                _external_image_cache[external_url] = data_url
                print(f"Found saved base64 for {external_url} in message {saved_msg.id}")
                return data_url
    except Exception as e:
        print(f"Error looking up saved base64: {e}")

    return None


def fetch_external_image_as_base64(image_url: str) -> str | None:
    """Fetch external image via CORS proxy and convert to base64.

    Returns data URL (data:image/...;base64,...) or None on failure.
    """
    import base64
    import httpx

    # Check cache first
    if image_url in _external_image_cache:
        return _external_image_cache[image_url]

    try:
        proxy_url = f"{CORS_PROXY_URL}?uri_target={image_url}"
        with httpx.Client(timeout=15.0) as client:
            response = client.get(proxy_url)

            if response.status_code == 403:
                print(f"Image blocked by robots.txt: {image_url}")
                return None

            response.raise_for_status()

            # Check content type
            content_type = response.headers.get("content-type", "")
            if not content_type.startswith("image/"):
                print(f"Not an image: {image_url} ({content_type})")
                return None

            # Convert to base64
            b64_data = base64.b64encode(response.content).decode("utf-8")
            media_type = content_type.split(";")[0]  # Remove charset if present
            data_url = f"data:{media_type};base64,{b64_data}"

            # Cache it
            _external_image_cache[image_url] = data_url
            return data_url

    except Exception as e:
        print(f"Failed to fetch external image {image_url}: {e}")
        return None


def save_fetched_image_as_message(external_url: str, data_url: str, room_id: int) -> None:
    """Save a fetched external image as a new message in the database.

    This persists the base64 version so we don't need to fetch again.
    Only saves if no saved version exists for this URL in this room.
    """
    print(f"[Vision Save] Attempting to save base64 for room {room_id}: {external_url[:60]}...")
    try:
        # Check if already saved for this URL in this room
        # Escape special LIKE characters in URL (%, _, \)
        escaped_url = escape_like_pattern(external_url)
        existing = Message.query.filter(
            Message.room_id == room_id,
            Message.content.like(f'%alt="Fetched from {escaped_url}"%', escape='\\')
        ).first()

        if existing:
            print(f"[Vision Save] Already exists as message {existing.id}")
            return

        # Create img tag with base64 data
        img_content = f'<img src="{data_url}" alt="Fetched from {external_url}">'
        new_message = Message(
            username="system",  # Mark as system message
            content=img_content,
            room_id=room_id
        )
        db.session.add(new_message)
        db.session.commit()
        print(f"[Vision Save] SUCCESS - Saved as message {new_message.id}")

        # Also cache it in memory
        _external_image_cache[external_url] = data_url
    except Exception as e:
        print(f"[Vision Save] FAILED: {e}")
        import traceback
        traceback.print_exc()
        db.session.rollback()


def build_message_content(msg, is_vision: bool, room_id: int = None) -> dict | str:
    """Build message content, handling images for vision models.

    For vision models with images, returns multimodal content array.
    Otherwise returns plain text content.

    If room_id is provided and an external image is fetched, saves the
    base64 version as a new message for future use.
    """
    if not is_vision:
        return msg.content

    # Check if this message contains a base64 image
    img_data = extract_base64_from_img_tag(msg.content)
    if img_data:
        media_type, base64_data = img_data
        # Return multimodal content with image
        return [
            {"type": "image_url", "image_url": {"url": f"data:{media_type};base64,{base64_data}"}}
        ]

    # Check for external image URL
    external_url = extract_external_image_url(msg.content)
    if external_url:
        print(f"[Vision] Found external URL in message {msg.id}: {external_url[:80]}...")

        # First check if we already have a saved base64 version
        if room_id is not None:
            saved_data_url = find_saved_base64_for_url(external_url, room_id)
            if saved_data_url:
                print(f"[Vision] Using saved base64 for {external_url[:50]}...")
                return [
                    {"type": "image_url", "image_url": {"url": saved_data_url}}
                ]

        # Not saved yet - fetch and save
        print(f"[Vision] Fetching external image: {external_url[:80]}...")
        data_url = fetch_external_image_as_base64(external_url)
        if data_url:
            print(f"[Vision] Fetched successfully, saving to room {room_id}...")
            # Save the fetched image as a new message for persistence
            if room_id is not None:
                save_fetched_image_as_message(external_url, data_url, room_id)
            return [
                {"type": "image_url", "image_url": {"url": data_url}}
            ]
        else:
            print(f"[Vision] Failed to fetch external image")

    # Plain text message
    return msg.content


def extract_first_image_for_og(room_id: int) -> str | None:
    """Extract the first image URL from messages for Open Graph meta tags.

    Checks messages in the room for:
    1. Base64 images (returns as data URL - may be large)
    2. External image URLs in markdown format ![alt](url)
    3. External image URLs in img tags

    Returns image URL/data URL or None if no image found.
    """
    import re

    messages = Message.query.filter_by(room_id=room_id).order_by(Message.id.asc()).limit(50).all()

    for msg in messages:
        if not msg.content:
            continue

        # Check for base64 image
        if msg.is_base64_image():
            img_data = extract_base64_from_img_tag(msg.content)
            if img_data:
                media_type, base64_data = img_data
                return f"data:{media_type};base64,{base64_data}"

        # Check for markdown image ![alt](url)
        md_img_match = re.search(r'!\[[^\]]*\]\(([^)]+)\)', msg.content)
        if md_img_match:
            url = md_img_match.group(1)
            if url.startswith(('http://', 'https://')):
                return url

        # Check for img tag with src
        img_src_match = re.search(r'<img[^>]+src=["\']([^"\']+)["\']', msg.content)
        if img_src_match:
            url = img_src_match.group(1)
            if url.startswith(('http://', 'https://')):
                return url

    return None


def generate_og_description(room, max_chars: int = 500) -> str:
    """Generate a description for Open Graph meta tags.

    Uses room title if available, then extracts text from first few messages.
    Strips markdown/code/images and limits to max_chars.
    """
    import re

    parts = []

    # Start with room title if available
    if room and room.title:
        parts.append(room.title)

    # Get first few messages for description
    if room:
        messages = Message.query.filter_by(room_id=room.id).order_by(Message.id.asc()).limit(10).all()

        for msg in messages:
            if not msg.content:
                continue

            # Skip base64 images
            if msg.is_base64_image():
                continue

            text = msg.content

            # Remove code blocks
            text = re.sub(r'```[\s\S]*?```', '', text)
            text = re.sub(r'`[^`]+`', '', text)

            # Remove markdown images
            text = re.sub(r'!\[[^\]]*\]\([^)]+\)', '', text)

            # Remove HTML tags
            text = re.sub(r'<[^>]+>', '', text)

            # Remove markdown links but keep text
            text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)

            # Remove markdown headers
            text = re.sub(r'^#{1,6}\s+', '', text, flags=re.MULTILINE)

            # Remove bold/italic markers
            text = re.sub(r'\*{1,2}([^*]+)\*{1,2}', r'\1', text)
            text = re.sub(r'_{1,2}([^_]+)_{1,2}', r'\1', text)

            # Clean up whitespace
            text = re.sub(r'\s+', ' ', text).strip()

            if text:
                parts.append(text)

            # Stop if we have enough content
            if len(' '.join(parts)) > max_chars:
                break

    description = ' '.join(parts)

    # Truncate to max_chars
    if len(description) > max_chars:
        description = description[:max_chars - 3].rsplit(' ', 1)[0] + '...'

    return description if description else "AI-powered chat room on OpenCompletion"


def get_openai_client_and_model(
    model_name="adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic",
):
    """Get OpenAI client and model name.

    Supports MODEL_X references (e.g., MODEL_1, MODEL_2, MODEL_3) that map to
    environment variables MODEL_ENDPOINT_X and MODEL_API_KEY_X.
    """
    # Handle MODEL_X references
    if model_name and model_name.startswith("MODEL_"):
        try:
            model_num = model_name.split("_")[1]
            endpoint_key = f"MODEL_ENDPOINT_{model_num}"
            api_key_key = f"MODEL_API_KEY_{model_num}"

            endpoint = os.environ.get(endpoint_key)
            api_key = os.environ.get(api_key_key)

            if endpoint and api_key:
                client = get_client_for_endpoint(endpoint, api_key)

                # Look up actual model name from MODEL_CLIENT_MAP for this endpoint
                actual_model = None
                for model_id, (registered_client, base_url) in MODEL_CLIENT_MAP.items():
                    if base_url == endpoint:
                        actual_model = model_id
                        break

                if actual_model:
                    return client, actual_model
                else:
                    # Fallback: query endpoint for models if not in map yet
                    try:
                        response = client.models.list()
                        if response.data:
                            actual_model = response.data[0].id
                            print(
                                f"[DEBUG] Using first model from {endpoint}: {actual_model}"
                            )
                            return client, actual_model
                    except Exception as e:
                        print(f"Warning: Could not query models from {endpoint}: {e}")

                    # Final fallback
                    print(
                        f"Warning: No models found for {endpoint}, using 'model' as fallback"
                    )
                    return client, "model"
            else:
                print(
                    f"Warning: MODEL_{model_num} not configured ({endpoint_key} or {api_key_key} missing)"
                )
                # Fall back to default model
                model_name = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic"
        except Exception as e:
            print(f"Warning: Failed to load {model_name}: {e}, falling back to default")
            model_name = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic"

    return get_client_for_model(model_name), model_name


HELP_MESSAGE = """
**Available Commands:**
- `/activity [s3_file_path]`: Start an activity from the specified S3 file path.
- `/activity cancel`: Cancel the current activity.
- `/activity info`: Display information about the current activity.
- `/activity metadata`: Display metadata for the current activity.
- `/s3 ls [s3_file_path_pattern]`: List files in S3 matching the pattern.
- `/s3 load [s3_file_path]`: Load a file from S3.
- `/s3 save [s3_key_path]`: Save the most recent code block from the chatroom to S3.
- `/title new`: Generates a new title which reflects conversation content for the current chatroom.
- `/cancel`: Cancel the most recent chat completion from streaming into the chatroom.
- `/help`: Display this help message.

**Interacting with AI Models:**
- Select a model from the dropdown menu above the chat input. Available models are dynamically loaded from configured endpoints and include options like `gpt-4o-mini`, `llama3-70b-8192`, `anthropic.claude-3-sonnet-20240229-v1:0`, and `dall-e-3` for image generation.
- Type your message and send it. The selected model will respond if it's not "None".
- For image generation, select `dall-e-3` and provide a prompt (e.g., "A futuristic cityscape").

**Getting Started:**
Welcome to the chatroom! Here, you can explore various AI models and engage in interactive activities. Here's how you can get started:

1. **Explore the Chatroom:**
   - Join a chatroom by navigating to its unique URL. You can see the list of available chatrooms on the main page.
   - Once inside, you can start a conversation by typing your message in the chatbox.

2. **Start an Activity:**
   - To begin an educational activity, use the `/activity` command followed by the path to the activity YAML file. For example:
**Getting Started:**

Welcome to the chatroom! Here, you can explore various AI models and engage in interactive activities. Here's how you can get started:

1. **Explore the Chatroom:**
   - Join a chatroom by navigating to its unique URL. You can see the list of available chatrooms on the main page.
   - Once inside, you can start a conversation by typing your message in the chatbox.

2. **Start an Activity:**
   - To begin an educational activity, use the `/activity` command followed by the path to the activity YAML file. For example:
     ```
     /activity research/activity0.yaml
     ```
   - The AI will guide you through the activity, providing feedback and information as you progress.

3. **Interact with AI Models:**
   - To interact with a specific AI model, simply type the model's command followed by your prompt. For example:
     ```
     gpt-4 What is the capital of France?
     ```
   - The system will process your message and provide a response from the selected model.

4. **Manage Files with S3:**
   - Use the `/s3` commands to load, save, or list files in your S3 bucket. For example, to list all files, use:
     ```
     /s3 ls *
     ```

5. **Get Help:**
   - If you need assistance or want to see a list of available commands, type `/help` to display this message.

Feel free to explore and experiment with different commands and models. Enjoy your time in the chatroom!
"""


def get_room(room_name):
    """Utility function to get room from room name."""
    room = Room.query.filter_by(name=room_name).first()
    if room:
        return room
    else:
        # Create a new room since it doesn't exist
        new_room = Room()
        new_room.name = room_name
        db.session.add(new_room)
        db.session.commit()
        return new_room


def get_s3_client():
    """Utility function to get the S3 client with the appropriate profile."""
    if app.config.get("PROFILE_NAME"):
        session = boto3.Session(profile_name=app.config["PROFILE_NAME"])
        s3_client = session.client("s3")
    else:
        s3_client = boto3.client("s3")
    return s3_client


# Initialize activity module after socketio and db are configured
activity.init_activity_module(
    app,
    socketio,
    db,
    {
        "get_room": get_room,
        "get_s3_client": get_s3_client,
        "get_openai_client_and_model": get_openai_client_and_model,
        "SYSTEM_USERS": SYSTEM_USERS,
    },
)


@app.route("/favicon.ico")
def favicon():
    return send_from_directory(os.path.join(app.root_path, "static"), "favicon.ico")


@app.route("/")
def index():
    total_public_rooms = Room.query.filter_by(is_private=False, is_archived=False).count()
    total_private_rooms = 0
    user = auth.get_current_user()
    if user:
        total_private_rooms = Room.query.filter_by(is_private=True, is_archived=False, owner_id=user.id).count()

    stats = {
        'total_public_rooms': total_public_rooms,
        'total_private_rooms': total_private_rooms,
    }

    return render_template("index.html", stats=stats, user=user)


@app.route("/auth")
def auth_page():
    """Authentication page"""
    return render_template("auth.html")


@app.route("/browse")
def browse_rooms():
    """Browse all rooms (public and user's private rooms)"""
    user = auth.get_current_user()

    # Get public rooms ordered by last updated
    public_rooms = Room.query.filter_by(
        is_private=False,
        is_archived=False
    ).order_by(Room.updated_at.desc()).all()

    # Get user's private rooms if authenticated
    private_rooms = []
    if user:
        private_rooms = Room.query.filter_by(
            is_private=True,
            is_archived=False,
            owner_id=user.id
        ).order_by(Room.updated_at.desc()).all()

    return render_template(
        "browse.html",
        public_rooms=public_rooms,
        private_rooms=private_rooms,
        user=user
    )


@app.route("/models", methods=["GET"])
def get_models():
    # Optionally refresh or reinitialize the model map here.
    # For now we simply return the keys.
    return jsonify({"models": list(MODEL_CLIENT_MAP.keys())})


@app.route("/vision", methods=["GET"])
def get_vision_status():
    """Return vision model availability status."""
    return jsonify({
        "available": len(VISION_MODELS) > 0,
        "models": VISION_MODELS,
        "default": VISION_MODELS[0] if VISION_MODELS else None
    })


@app.route("/vision/describe", methods=["POST"])
def describe_image():
    """Generate alt text description for an image using vision model."""
    if not VISION_MODELS:
        return jsonify({"error": "No vision models available"}), 503

    data = request.get_json()
    if not data or "image" not in data:
        return jsonify({"error": "Missing 'image' field (base64 data URL)"}), 400

    image_url = data["image"]  # Expected format: data:image/jpeg;base64,...
    prompt = data.get("prompt", "Describe this image in one brief sentence for use as alt text.")
    model_name = data.get("model", VISION_MODELS[0])

    if model_name not in VISION_MODELS:
        return jsonify({"error": f"Model {model_name} is not a vision model"}), 400

    try:
        client = get_client_for_model(model_name)
        response = client.chat.completions.create(
            model=model_name,
            messages=[{
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {"type": "image_url", "image_url": {"url": image_url}}
                ]
            }],
            max_tokens=150,
            temperature=0.3
        )
        description = response.choices[0].message.content.strip()
        return jsonify({"description": description, "model": model_name})
    except Exception as e:
        return jsonify({"error": str(e)}), 500


# Authentication endpoints
@app.route("/auth/send-otp", methods=["POST"])
def send_otp():
    """Send OTP to user's email"""
    data = request.get_json()
    email = data.get('email', '').strip().lower()

    if not email:
        return jsonify({'error': 'Email is required'}), 400

    # Basic email validation
    if '@' not in email or '.' not in email.split('@')[1]:
        return jsonify({'error': 'Invalid email address'}), 400

    # Create OTP token
    otp_token = auth.create_otp_token(email)

    # Send OTP via email
    if auth.send_otp_email(email, otp_token.otp_code):
        return jsonify({
            'success': True,
            'message': 'OTP sent to your email',
            'email': email
        })
    else:
        return jsonify({'error': 'Failed to send OTP email'}), 500


@app.route("/auth/verify-otp", methods=["POST"])
def verify_otp():
    """Verify OTP code and check if user exists"""
    data = request.get_json()
    email = data.get('email', '').strip().lower()
    otp_code = data.get('otp_code', '').strip()

    if not email or not otp_code:
        return jsonify({'error': 'Email and OTP code are required'}), 400

    # Verify OTP
    otp_token = auth.verify_otp(email, otp_code)
    if not otp_token:
        return jsonify({'error': 'Invalid or expired OTP code'}), 400

    # Check if user exists
    user = auth.get_or_create_user(email)

    if user:
        # Existing user - log them in
        auth.login_user(user)
        return jsonify({
            'success': True,
            'needs_display_name': False,
            'user': {
                'email': user.email,
                'display_name': user.display_name
            }
        })
    else:
        # New user - needs to claim display name
        # Store email in session temporarily
        session['pending_email'] = email
        return jsonify({
            'success': True,
            'needs_display_name': True,
            'email': email
        })


@app.route("/auth/claim-name", methods=["POST"])
def claim_name():
    """Claim display name for new user (after OTP verification)"""
    data = request.get_json()
    display_name = data.get('display_name', '').strip()
    email = session.get('pending_email')

    if not email:
        return jsonify({'error': 'No pending email verification'}), 400

    if not display_name:
        return jsonify({'error': 'Display name is required'}), 400

    # Validate display name (alphanumeric, underscores, hyphens only, 3-50 chars)
    import re
    if not re.match(r'^[a-zA-Z0-9_-]{3,50}$', display_name):
        return jsonify({
            'error': 'Display name must be 3-50 characters (letters, numbers, underscores, hyphens only)'
        }), 400

    # Create user
    user, error = auth.create_user(email, display_name)
    if error:
        return jsonify({'error': error}), 400

    # Log in user
    auth.login_user(user)

    # Clear pending email
    session.pop('pending_email', None)

    return jsonify({
        'success': True,
        'user': {
            'email': user.email,
            'display_name': user.display_name
        }
    })


@app.route("/auth/status", methods=["GET"])
def auth_status():
    """Get current authentication status"""
    user = auth.get_current_user()
    if user:
        return jsonify({
            'authenticated': True,
            'user': {
                'email': user.email,
                'display_name': user.display_name
            }
        })
    else:
        return jsonify({'authenticated': False})


@app.route("/auth/logout", methods=["POST"])
def logout():
    """Log out current user"""
    auth.logout_user()
    return jsonify({'success': True})


@app.route("/profile")
@auth.require_auth
def profile_page():
    """Profile settings page"""
    user = auth.get_current_user()
    return render_template("profile.html", user=user)


@app.route("/api/check-username", methods=["GET"])
def check_username():
    """Check if username is available"""
    username = request.args.get('username', '').strip()

    if not username:
        return jsonify({'available': False, 'error': 'Username is required'}), 400

    # Validate format
    import re
    if not re.match(r'^[a-zA-Z0-9_-]{3,50}$', username):
        return jsonify({'available': False, 'error': 'Invalid format'}), 400

    # Check if username exists
    existing_user = User.query.filter_by(display_name=username).first()

    return jsonify({'available': existing_user is None})


@app.route("/api/update-username", methods=["POST"])
@auth.require_auth
def update_username():
    """Update user's display name"""
    user = auth.get_current_user()
    data = request.get_json()
    new_username = data.get('new_username', '').strip()

    if not new_username:
        return jsonify({'error': 'Username is required'}), 400

    # Validate format
    import re
    if not re.match(r'^[a-zA-Z0-9_-]{3,50}$', new_username):
        return jsonify({
            'error': 'Username must be 3-50 characters (letters, numbers, underscores, hyphens only)'
        }), 400

    # Check if username is already taken
    existing_user = User.query.filter_by(display_name=new_username).first()
    if existing_user and existing_user.id != user.id:
        return jsonify({'error': 'Username is already taken'}), 400

    # Update username
    user.display_name = new_username
    db.session.commit()

    return jsonify({
        'success': True,
        'user': {
            'email': user.email,
            'display_name': user.display_name
        }
    })


@app.route("/api/activities", methods=["GET"])
def get_activities():
    """Return the list of available activities."""
    activities = []

    if app.config.get("LOCAL_ACTIVITIES"):
        # List local activity files from research directory
        import os

        research_dir = "research"
        if os.path.exists(research_dir):
            for filename in sorted(os.listdir(research_dir)):
                if filename.endswith((".yaml", ".yml")):
                    activities.append(f"research/{filename}")
    else:
        # For S3 activities, you would list from S3
        # This is a placeholder - you'd need to implement S3 listing
        pass

    return jsonify({"activities": activities})


@app.route("/api/rooms", methods=["GET"])
def get_rooms_api():
    """Get list of rooms (public or user's private rooms)"""
    user = auth.get_current_user()

    # Get public rooms
    public_rooms = Room.query.filter_by(is_private=False, is_archived=False).order_by(Room.id.desc()).all()

    # Get private rooms if authenticated
    private_rooms = []
    if user:
        private_rooms = Room.query.filter_by(is_private=True, is_archived=False, owner_id=user.id).order_by(Room.id.desc()).all()

    return jsonify({
        'public_rooms': [{
            'id': r.id,
            'name': r.name,
            'title': r.title,
            'active_users_count': len(r.get_active_users())
        } for r in public_rooms],
        'private_rooms': [{
            'id': r.id,
            'name': r.name,
            'title': r.title,
            'active_users_count': len(r.get_active_users())
        } for r in private_rooms]
    })


@app.route("/api/rooms/create", methods=["POST"])
def create_room_api():
    """Create a new room"""
    user = auth.get_current_user()
    data = request.get_json() or {}
    room_name = data.get('name', '').strip()
    is_private = data.get('is_private', False)

    if not room_name:
        return jsonify({'error': 'Room name is required'}), 400

    # Private rooms require authentication
    if is_private and not user:
        return jsonify({'error': 'Authentication required to create private rooms'}), 401

    # Check if room already exists
    existing_room = Room.query.filter_by(name=room_name).first()
    if existing_room:
        return jsonify({'error': 'Room name already exists'}), 400

    # Create room
    new_room = Room()
    new_room.name = room_name
    new_room.is_private = is_private
    new_room.owner_id = user.id if user else None

    db.session.add(new_room)
    db.session.commit()

    # Broadcast new room to all users so it appears in sidebar
    new_room_data = {
        'id': new_room.id,
        'name': new_room.name,
        'title': new_room.title,
        'is_private': new_room.is_private,
        'is_new': True  # Flag to indicate this is a new room, not an update
    }
    socketio.emit("update_room_list", new_room_data, room=None)

    return jsonify({
        'success': True,
        'room': {
            'id': new_room.id,
            'name': new_room.name,
            'is_private': new_room.is_private
        }
    })


@app.route("/api/rooms/<int:room_id>/fork", methods=["POST"])
def fork_room(room_id):
    """Fork a room (authenticated users can fork to private or public)"""
    user = auth.get_current_user()
    data = request.get_json() or {}
    make_private = data.get('private', False)

    # Get source room
    source_room = Room.query.get(room_id)
    if not source_room:
        return jsonify({'error': 'Room not found'}), 404

    # Private rooms can only be forked by their owner
    if source_room.is_private:
        if not user or source_room.owner_id != user.id:
            return jsonify({'error': 'Cannot fork private rooms you do not own'}), 403

    # Private rooms require authentication
    if make_private and not user:
        return jsonify({'error': 'Authentication required to create private rooms'}), 401

    # Generate new room name
    base_name = f"{source_room.name}_fork"
    new_name = base_name
    counter = 1
    while Room.query.filter_by(name=new_name).first():
        new_name = f"{base_name}_{counter}"
        counter += 1

    # Create forked room
    new_room = Room()
    new_room.name = new_name
    new_room.title = f"Fork of {source_room.title or source_room.name}"
    new_room.is_private = make_private
    new_room.owner_id = user.id if user else None
    new_room.forked_from_id = source_room.id

    db.session.add(new_room)
    db.session.commit()

    # Copy messages from source room
    source_messages = Message.query.filter_by(room_id=source_room.id).all()
    for msg in source_messages:
        new_msg = Message(
            username=msg.username,
            content=msg.content,
            room_id=new_room.id
        )
        db.session.add(new_msg)

    db.session.commit()

    return jsonify({
        'success': True,
        'room': {
            'id': new_room.id,
            'name': new_room.name,
            'title': new_room.title,
            'is_private': new_room.is_private
        }
    })


@app.route("/api/rooms/<int:room_id>/archive", methods=["POST"])
@auth.require_auth
def archive_room(room_id):
    """Archive a room (owner only)"""
    user = auth.get_current_user()
    room = Room.query.get(room_id)

    if not room:
        return jsonify({'error': 'Room not found'}), 404

    if room.owner_id != user.id:
        return jsonify({'error': 'Only room owner can archive rooms'}), 403

    room.is_archived = True
    db.session.commit()

    return jsonify({'success': True})


@app.route("/api/rooms/<int:room_id>/delete", methods=["DELETE"])
@auth.require_auth
def delete_room(room_id):
    """Delete a room (owner only)"""
    user = auth.get_current_user()
    room = Room.query.get(room_id)

    if not room:
        return jsonify({'error': 'Room not found'}), 404

    if room.owner_id != user.id:
        return jsonify({'error': 'Only room owner can delete rooms'}), 403

    # Delete all messages in the room
    Message.query.filter_by(room_id=room.id).delete()

    # Delete activity state if any
    ActivityState.query.filter_by(room_id=room.id).delete()

    # Delete user sessions
    UserSession.query.filter_by(room_id=room.id).delete()

    # Delete the room
    db.session.delete(room)
    db.session.commit()

    return jsonify({'success': True})


@app.route("/api/generate-artifact-name", methods=["POST"])
def generate_artifact_name():
    """Generate a meaningful filename for an artifact using AI.

    Returns a 1-3 word filename with underscores based on what the code does.
    Respects ENABLE_CODE_GEN_FILENAMES environment variable (enabled by default).
    """
    # Check if feature is enabled (default: true)
    enabled = os.environ.get("ENABLE_CODE_GEN_FILENAMES", "true").lower() == "true"
    if not enabled:
        return jsonify({"filename": "compiled_binary"})

    try:
        data = request.get_json()
        code = data.get("code", "")
        language = data.get("language", "")

        if not code:
            return jsonify({"filename": "compiled_binary"})

        # Use MODEL_1 (Hermes) to generate filename
        client, model = get_openai_client_and_model("MODEL_1")

        system_prompt = """You are a filename generator. Given code, generate a SHORT, descriptive filename that represents what the code does.

Rules:
- Output ONLY the filename, nothing else
- Use 1-3 words maximum
- Use lowercase with underscores between words (e.g., "fizzbuzz" or "hello_world" or "prime_checker")
- NO file extension
- NO explanations or commentary
- Be specific about what the code does

Examples:
- Code that prints "Hello World" → "hello_world"
- Code that checks for prime numbers → "prime_checker"
- Code that plays FizzBuzz → "fizzbuzz"
- Code that sorts an array → "array_sort"
- Code that calculates factorial → "factorial"
"""

        user_prompt = f"Language: {language}\n\nCode:\n{code}\n\nGenerate filename:"

        response = client.chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ],
            temperature=0.3,
            max_tokens=20
        )

        filename = response.choices[0].message.content.strip()

        # Clean up the filename (remove quotes, extensions, whitespace)
        filename = filename.strip('"\'')
        filename = filename.split('.')[0]  # Remove any extension
        filename = filename.replace(' ', '_')
        filename = filename.lower()

        # Validate filename (alphanumeric and underscores only)
        import re
        if not re.match(r'^[a-z0-9_]+$', filename):
            filename = "compiled_binary"

        # Ensure it's not too long (max 50 chars)
        if len(filename) > 50:
            filename = filename[:50]

        return jsonify({"filename": filename})

    except Exception as e:
        print(f"Error generating artifact name: {e}")
        return jsonify({"filename": "compiled_binary"})


def _unsandbox_error_response(e, log_label):
    """Turn an Unsandbox SDK exception into an informative JSON error.

    Surfaces the real upstream HTTP status + body when available instead of a
    flat 500, so failures are debuggable from the browser console and logs.
    Never serializes credentials: public/secret keys live in request headers,
    never in response bodies, so echoing the upstream body is safe.
    """
    import traceback

    if isinstance(e, requests.exceptions.HTTPError) and e.response is not None:
        status = e.response.status_code
        body = (e.response.text or "")[:2000]
        print(f"{log_label}: HTTP {status} from Unsandbox: {body}")
        traceback.print_exc()
        out_status = status if 400 <= status < 600 else 502
        return (
            jsonify(
                {
                    "error": "Unsandbox upstream error",
                    "upstream_status": status,
                    "upstream_body": body,
                }
            ),
            out_status,
        )

    print(f"{log_label}: {e}")
    traceback.print_exc()
    return jsonify({"error": f"{log_label}: {e}"}), 500


# Unsandbox API proxy endpoints - keeps API keys server-side
@app.route("/api/code/execute", methods=["POST"])
def proxy_code_execute():
    """Proxy code execution requests to Unsandbox API.

    Keeps UNSANDBOX_PUBLIC_KEY and UNSANDBOX_SECRET_KEY secure on the server side.
    Uses official Unsandbox Python SDK for authentication and execution.
    Supports artifacts parameter for compiled binaries, images, etc.
    """
    try:
        data = request.get_json()
        if not data:
            return jsonify({"error": "Request body required"}), 400

        # Check if credentials are configured
        public_key = os.environ.get("UNSANDBOX_PUBLIC_KEY")
        secret_key = os.environ.get("UNSANDBOX_SECRET_KEY")
        if not public_key or not secret_key:
            return jsonify({"error": "Code execution not configured"}), 503

        # Extract parameters from request
        language = data.get("language")
        code = data.get("code")

        if not language or not code:
            return jsonify({"error": "Language and code are required"}), 400

        # Build request body with all supported parameters
        request_body = {
            "language": language,
            "code": code,
            "return_artifact": True,
        }

        # Use SDK's internal _make_request for full parameter support
        result = un._make_request(
            "POST",
            "/execute",
            public_key,
            secret_key,
            request_body
        )

        # Return job_id from response
        return jsonify({"job_id": result.get("job_id")}), 200

    except Exception as e:
        return _unsandbox_error_response(e, "Error proxying code execution")


@app.route("/api/code/jobs/<job_id>", methods=["GET"])
def proxy_job_status(job_id):
    """Proxy job status requests to Unsandbox API using SDK."""
    try:
        # Check if credentials are configured
        if not os.environ.get("UNSANDBOX_PUBLIC_KEY") or not os.environ.get("UNSANDBOX_SECRET_KEY"):
            return jsonify({"error": "Code execution not configured"}), 503

        # Use SDK's get_job method
        result = un.get_job(job_id)

        # SDK returns job status dict
        return jsonify(result), 200

    except Exception as e:
        return _unsandbox_error_response(e, "Error fetching job status")


@app.route("/api/code/jobs/<job_id>", methods=["DELETE"])
def proxy_job_cancel(job_id):
    """Proxy job cancellation requests to Unsandbox API using SDK."""
    try:
        # Check if credentials are configured
        if not os.environ.get("UNSANDBOX_PUBLIC_KEY") or not os.environ.get("UNSANDBOX_SECRET_KEY"):
            return jsonify({"error": "Code execution not configured"}), 503

        # Use SDK's cancel_job method
        result = un.cancel_job(job_id)

        # SDK returns success status
        return jsonify(result), 200

    except Exception as e:
        return _unsandbox_error_response(e, "Error cancelling job")


@app.route("/api/fix-code", methods=["POST"])
def fix_code():
    """Auto-fix code errors by asking AI to fix issues based on stderr output.

    Accepts code, language, stderr, and attempt number.
    Returns fixed code block or error message.
    """
    try:
        data = request.get_json()
        code = data.get("code", "")
        language = data.get("language", "")
        stderr = data.get("stderr", "")
        exit_code = data.get("exit_code", 1)
        attempt = data.get("attempt", 1)

        if not code or not language:
            return jsonify({"error": "Code and language are required"}), 400

        # Try MODEL_3 (Qwen Coder) first for better code fixing, fall back to MODEL_1 (Hermes)
        try:
            client, model = get_openai_client_and_model("MODEL_3")
            print(f"[INFO] Using MODEL_3 for code fixing: {model}")
        except Exception as e:
            print(f"[WARN] MODEL_3 not available, falling back to MODEL_1: {e}")
            client, model = get_openai_client_and_model("MODEL_1")
            print(f"[INFO] Using MODEL_1 for code fixing: {model}")

        system_prompt = f"""You are an expert {language} programmer and debugger. Your task is to fix code that has errors.

CRITICAL RULES:
- Output ONLY the fixed code, nothing else
- NO explanations, NO comments about what you changed
- NO markdown code fences (```), just the raw code
- Preserve the original code structure and logic as much as possible
- Fix ONLY the errors reported in stderr
- If the error mentions missing imports/includes, add them at the top
- If the error is a syntax error, fix the syntax
- Keep the same variable names and overall approach

The code should be immediately executable without any modifications."""

        user_prompt = f"""The following {language} code has errors:

```{language}
{code}
```

Error output (exit code {exit_code}):
```
{stderr}
```

Fix the code (output ONLY the corrected code, no explanations):"""

        response = client.chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ],
            temperature=0.2,  # Low temperature for consistent fixes
            max_tokens=2000
        )

        fixed_code = response.choices[0].message.content.strip()

        # Clean up any markdown code fences that might have slipped through
        if fixed_code.startswith("```"):
            lines = fixed_code.split("\n")
            # Remove first line if it's a fence
            if lines[0].startswith("```"):
                lines = lines[1:]
            # Remove last line if it's a fence
            if lines and lines[-1].strip() == "```":
                lines = lines[:-1]
            fixed_code = "\n".join(lines)

        return jsonify({
            "success": True,
            "fixed_code": fixed_code,
            "attempt": attempt
        })

    except Exception as e:
        print(f"Error fixing code: {e}")
        return jsonify({"error": f"Failed to fix code: {str(e)}"}), 500


@app.route("/chat/<room_name>")
def chat(room_name):
    user = auth.get_current_user()

    # Get or create the room
    room = Room.query.filter_by(name=room_name).first()

    # If room doesn't exist yet, it will be created in get_room() when user joins
    # But check if they're trying to access a private room they don't own
    if room and room.is_private:
        if not user or room.owner_id != user.id:
            return "Access denied: This is a private room", 403

    # Query public rooms and user's private rooms for sidebar
    public_rooms = Room.query.filter_by(is_private=False, is_archived=False).order_by(Room.id.desc()).all()
    private_rooms = []
    if user:
        private_rooms = Room.query.filter_by(is_private=True, is_archived=False, owner_id=user.id).order_by(Room.id.desc()).all()

    # Use authenticated user's display name, or None (will prompt on client side)
    username = user.display_name if user else None

    # Generate Open Graph metadata for social sharing
    og_image = None
    og_description = "AI-powered chat room on OpenCompletion"
    og_title = f"{room_name} - OpenCompletion"

    if room:
        # Try to get first image from messages
        og_image = extract_first_image_for_og(room.id)
        og_description = generate_og_description(room)
        if room.title:
            og_title = f"{room.title} - OpenCompletion"

    # Pass username, rooms, room (current room), and user into the template
    return render_template(
        "chat.html",
        room_name=room_name,
        current_room=room,
        public_rooms=public_rooms,
        private_rooms=private_rooms,
        username=username,
        user=user,
        og_title=og_title,
        og_description=og_description,
        og_image=og_image
    )


@app.route("/download_chat_history", methods=["GET"])
def download_chat_history():
    room_name = request.args.get("room_name")
    room = get_room(room_name)

    if not room:
        return jsonify({"error": "Room not found"}), 404

    messages = Message.query.filter_by(room_id=room.id).all()

    if not messages:
        return jsonify({"error": "No messages found"}), 404

    chat_history = [
        {
            "role": "system" if message.username in SYSTEM_USERS else "user",
            "content": message.content,
        }
        for message in messages
        if not message.is_base64_image()
    ]

    if not chat_history:
        return jsonify({"error": "No valid messages found"}), 404

    response = Response(
        response=json.dumps(chat_history, indent=2),
        status=200,
        mimetype="application/json",
    )
    response.headers["Content-Disposition"] = f"attachment; filename={room.name}.json"
    return response


@app.route("/download_chat_history_md", methods=["GET"])
def download_chat_history_md():
    room_name = request.args.get("room_name")
    room = get_room(room_name)

    if not room:
        return jsonify({"error": "Room not found"}), 404

    messages = Message.query.filter_by(room_id=room.id).all()

    if not messages:
        return jsonify({"error": "No messages found"}), 404

    # Access system users from the existing context
    chat_history_md = []
    toc = []
    for index, message in enumerate(messages):
        if not message.is_base64_image():  # Correctly call the method
            role = "System" if message.username in SYSTEM_USERS else "User"
            header = f"### {role}: {message.username} (Turn {index + 1})"
            toc.append(
                f"- [{role}: {message.username} (Turn {index + 1})](#{role.lower()}-{message.username.lower().replace(' ', '-')}-turn-{index + 1})"
            )
            chat_history_md.append(f"{header}\n\n{message.content}\n\n---\n")

    if not chat_history_md:
        return jsonify({"error": "No valid messages found"}), 404

    markdown_content = (
        f"# Chat History for {room.name}\n\n## Table of Contents\n"
        + "\n".join(toc)
        + "\n\n"
        + "\n".join(chat_history_md)
    )

    response = Response(response=markdown_content, status=200, mimetype="text/markdown")
    response.headers["Content-Disposition"] = f'attachment; filename="{room.name}.md"'
    return response


@app.route("/search")
def search_page():
    # Query all rooms so that newest is first.
    rooms = Room.query.order_by(Room.id.desc()).all()

    keywords = request.args.get("keywords", "")
    username = request.args.get("username", "guest")
    if not keywords:
        return render_template(
            "search.html",
            rooms=rooms,
            keywords=keywords,
            results=[],
            username=username,
            error="Keywords are required",
        )

    # Call the function to search messages
    search_results = search_messages(keywords)

    # If there's exactly one search result, redirect directly to that room
    if len(search_results) == 1:
        room_result = search_results[0]
        room_name = room_result["room_name"]

        # Build the redirect URL with current parameters
        redirect_params = {}
        if username and username != "guest":
            redirect_params["username"] = username

        # Preserve other URL parameters like model, voice, etc.
        for param in ["model", "voice"]:
            value = request.args.get(param)
            if value:
                redirect_params[param] = value

        redirect_url = url_for("chat", room_name=room_name, **redirect_params)
        return redirect(redirect_url)

    return render_template(
        "search.html",
        rooms=rooms,
        keywords=keywords,
        results=search_results,
        username=username,
        error=None,
    )


def search_messages(keywords):
    search_results = {}

    # Split the keywords by spaces and sanitize
    keyword_list = keywords.lower().split()

    # Sanitize keywords to prevent SQL injection
    sanitized_keywords = []
    for keyword in keyword_list:
        # Remove potentially dangerous characters and limit length
        sanitized_keyword = "".join(
            c for c in keyword if c.isalnum() or c.isspace() or c in "-_"
        )[:50]
        if sanitized_keyword.strip():  # Only add non-empty keywords
            sanitized_keywords.append(sanitized_keyword.strip())

    if not sanitized_keywords:
        return {}

    # Search for messages containing any of the sanitized keywords using parameterized query
    messages = Message.query.filter(
        db.or_(
            *[Message.content.ilike(f"%{keyword}%") for keyword in sanitized_keywords]
        )
    ).all()

    for message in messages:
        room = Room.query.get(message.room_id)
        if room:
            # Calculate the score based on the number of occurrences of all keywords
            score = sum(
                message.content.lower().count(keyword) for keyword in keyword_list
            )

            if room.id not in search_results:
                search_results[room.id] = {
                    "room_id": room.id,
                    "room_name": room.name,
                    "room_title": room.title,
                    "score": 0,
                }

            search_results[room.id]["score"] += score

    # Convert the dictionary to a list and sort results by score in descending order
    search_results_list = list(search_results.values())
    search_results_list.sort(key=lambda x: x["score"], reverse=True)

    return search_results_list


# Handle user joining a room
@socketio.on("join")
def on_join(data):
    room_name = data["room_name"]
    username = data["username"]
    room = get_room(room_name)

    # Set owner for newly created rooms (if room has no owner and user is authenticated)
    if room.owner_id is None:
        user = auth.get_current_user()
        if user:
            room.owner_id = user.id
            db.session.add(room)
            db.session.commit()

    # Add the user to the active users list
    room.add_user(username)

    # Store session data in the database
    user_session = UserSession(
        session_id=request.sid, username=username, room_name=room_name, room_id=room.id
    )
    db.session.add(user_session)
    db.session.commit()

    # Emit the active and inactive users list to the new joiner
    emit(
        "active_users",
        {
            "active_users": room.get_active_users(),
            "inactive_users": room.get_inactive_users(),
        },
        room=request.sid,
    )

    # Emit the active and inactive users list to everyone in the room
    emit(
        "active_users",
        {
            "active_users": room.get_active_users(),
            "inactive_users": room.get_inactive_users(),
        },
        room=room_name,
        include_self=False,
    )

    # This makes the client start listening for new events for this room.
    join_room(room_name)

    # update the title bar with the proper room title, if it exists for just this new client.
    if room.title:
        socketio.emit("update_room_title", {"title": room.title}, room=request.sid)

    # Fetch previous messages from the database
    previous_messages = Message.query.filter_by(room_id=room.id).all()

    # count the number of tokens in this room.
    total_token_count = 0

    # Send the history of messages only to the newly connected client.
    for message in previous_messages:
        if not message.is_base64_image():
            total_token_count += message.token_count
        emit(
            "previous_messages",
            {
                "id": message.id,
                "username": message.username,
                "content": message.content,
            },
            room=request.sid,
        )

    message_count = len(previous_messages)
    if room.title is None and message_count >= 6:
        room.title = gpt_generate_room_title(previous_messages)
        db.session.add(room)
        socketio.emit("update_room_title", {"title": room.title}, room=room.name)
        # Emit an event to update this room's title in the sidebar for all users.
        updated_room_data = {"id": room.id, "name": room.name, "title": room.title}
        socketio.emit("update_room_list", updated_room_data, room=None)

    # commit session & active user list and title to database.
    db.session.commit()

    # Broadcast to all clients in the room that a new user has joined.
    emit(
        "chat_message",
        {"id": None, "content": f"{username} has joined the room."},
        room=room.name,
    )
    emit(
        "chat_message",
        {
            "id": None,
            "content": f"Estimated {total_token_count} total tokens in conversation.",
        },
        room=request.sid,
    )


# Handle user leaving a room
@socketio.on("disconnect")
def on_disconnect():
    sid = request.sid
    user_session = UserSession.query.filter_by(session_id=sid).first()

    if user_session:
        room_name = user_session.room_name
        username = user_session.username
        room = Room.query.filter_by(name=room_name).first()
        room.remove_user(username)
        leave_room(room_name)
        # Broadcast to all clients in the room that a user has left the room.
        # Emit the active and inactive users list to everyone in the room
        emit(
            "active_users",
            {
                "active_users": room.get_active_users(),
                "inactive_users": room.get_inactive_users(),
            },
            room=room.name,
            include_self=False,
        )
        emit(
            "chat_message",
            {"id": None, "content": f"{username} has left the room."},
            room=room.name,
            include_self=False,
        )
        # Remove session data from the database
        db.session.delete(user_session)
        db.session.commit()


@socketio.on("chat_message")
def handle_message(data):
    room_name = data["room_name"]
    room = get_room(room_name)
    username = data["username"]
    message = data["message"].strip()
    model = data.get("model", "None")

    new_message = Message(
        username=username,
        content=message,
        room_id=room.id,
    )
    db.session.add(new_message)

    # Update room's updated_at timestamp (Unix epoch)
    from datetime import datetime
    room.updated_at = int(datetime.utcnow().timestamp())
    db.session.add(room)

    db.session.commit()

    emit(
        "chat_message",
        {
            "id": new_message.id,
            "username": username,
            "content": message,
        },
        room=room.name,
    )

    commands = message.splitlines()
    for command in commands:
        if command.startswith("/help"):
            socketio.emit(
                "chat_message",
                {"id": "tmp-1", "username": "System", "content": HELP_MESSAGE},
                room=room_name,
            )
            return
        if command.startswith("/activity cancel"):
            gevent.spawn(activity.cancel_activity, room_name, username)
            return
        if command.startswith("/activity info"):
            gevent.spawn(activity.display_activity_info, room_name, username)
            return
        if command.startswith("/activity metadata"):
            gevent.spawn(activity.display_activity_metadata, room_name, username)
            return
        if command.startswith("/activity"):
            s3_file_path = command.split(" ", 1)[1].strip()
            gevent.spawn(activity.start_activity, room_name, s3_file_path, username)
            return
        if command.startswith("/s3 ls"):
            s3_file_path_pattern = command.split(" ", 2)[2].strip()
            gevent.spawn(list_s3_files, room.name, s3_file_path_pattern, username)
        if command.startswith("/s3 load"):
            s3_file_path = command.split(" ", 2)[2].strip()
            gevent.spawn(load_s3_file, room_name, s3_file_path, username)
        if command.startswith("/s3 save"):
            s3_key_path = command.split(" ", 2)[2].strip()
            gevent.spawn(save_code_block_to_s3, room_name, s3_key_path, username)
        if command.startswith("/title new"):
            gevent.spawn(generate_new_title, room_name, username)
            return
        if command.startswith("/cancel"):
            gevent.spawn(cancel_generation, room_name)
            return

    activity_state = ActivityState.query.filter_by(room_id=room.id).first()
    if activity_state:
        gevent.spawn(
            activity.handle_activity_response, room_name, message, username, model
        )
        return

    if model != "None":
        emit(
            "chat_message",
            {"id": None, "content": "<span id='processing'>Processing...</span>"},
            room=room.name,
        )
        if "anthropic.claude" in model:
            gevent.spawn(chat_claude, username, room_name, model_name=model)
        if "dall-e" in model:
            gevent.spawn(generate_dalle_image, room_name, message, username)
        else:
            # All other models (Groq, Together, Mistral, etc.) use OpenAI client
            enable_thinking = data.get("enable_thinking", True)
            gevent.spawn(
                chat_gpt,
                username,
                room_name,
                model_name=model,
                enable_thinking=enable_thinking,
            )


@socketio.on("delete_message")
def handle_delete_message(data):
    msg_id = data["message_id"]
    # Delete the message from the database
    message = db.session.query(Message).filter(Message.id == msg_id).one_or_none()
    if message:
        db.session.delete(message)
        db.session.commit()

    # Notify all clients in the room to remove the message from their DOM
    emit("message_deleted", {"message_id": msg_id}, room=data["room_name"])


@socketio.on("update_message")
def handle_update_message(data):
    message_id = data["message_id"]
    new_content = data["content"]
    room_name = data["room_name"]

    # Find the message by ID
    message = Message.query.get(message_id)
    if message:
        # Update the message content
        message.content = new_content
        message.count_tokens()
        db.session.add(message)
        db.session.commit()

        # Emit an event to update the message on all clients
        emit(
            "message_updated",
            {
                "message_id": message_id,
                "content": new_content,
                "username": message.username,
            },
            room=room_name,
        )


@socketio.on("get_activity_status")
def handle_get_activity_status(data):
    """Get the current activity status for a room."""
    activity.handle_get_activity_status(data)


def group_consecutive_roles(messages):
    if not messages:
        return []

    grouped_messages = []
    current_role = messages[0]["role"]
    current_content = []

    for message in messages:
        if message["role"] == current_role:
            current_content.append(message["content"])
        else:
            grouped_messages.append(
                {"role": current_role, "content": " ".join(current_content)}
            )
            current_role = message["role"]
            current_content = [message["content"]]

    # Append the last grouped message
    grouped_messages.append(
        {"role": current_role, "content": " ".join(current_content)}
    )

    return grouped_messages


def chat_claude(
    # username, room_name, model_name="anthropic.claude-3-5-sonnet-20240620-v1:0"
    username,
    room_name,
    model_name="anthropic.claude-3-sonnet-20240229-v1:0",
):
    with app.app_context():
        room = get_room(room_name)
        # claude has a 200,000 token context window for prompts.
        all_messages = (
            Message.query.filter_by(room_id=room.id).order_by(Message.id.desc()).all()
        )

    chat_history = []
    for msg in reversed(all_messages):
        if msg.is_base64_image():
            continue
        role = "assistant" if msg.username in SYSTEM_USERS else "user"
        chat_history.append({"role": role, "content": msg.content})

    # only claude cares about this constrant.
    chat_history = group_consecutive_roles(chat_history)

    # Initialize the Bedrock client using boto3 and profile name.
    if app.config.get("PROFILE_NAME"):
        session = boto3.Session(profile_name=app.config["PROFILE_NAME"])
        client = session.client("bedrock-runtime", region_name="us-west-2")
    else:
        client = boto3.client("bedrock-runtime", region_name="us-west-2")

    # Define the request parameters
    params = {
        "modelId": model_name,
        "contentType": "application/json",
        "accept": "*/*",
        "body": json.dumps(
            {
                "messages": chat_history,
                "max_tokens": 4096,
                "temperature": 0,
                "top_k": 250,
                "top_p": 0.999,
                "stop_sequences": ["\n\nHuman:"],
                "anthropic_version": "bedrock-2023-05-31",
            }
        ).encode(),
    }

    # Process the event stream
    buffer = ""

    # save empty message, we need the ID when we chunk the response.
    with app.app_context():
        new_message = Message(username=model_name, content=buffer, room_id=room.id)
        db.session.add(new_message)
        db.session.commit()
        msg_id = new_message.id

    try:
        # Invoke the model with response stream
        response = client.invoke_model_with_response_stream(**params)["body"]

        first_chunk = True
        for event in response:
            content = ""

            # Check if there has been a cancellation request, break if there is.
            if cancellation_requests.get(msg_id):
                del cancellation_requests[msg_id]
                break

            if "chunk" in event:
                chunk_data = json.loads(event["chunk"]["bytes"].decode())

                if chunk_data["type"] == "content_block_delta":
                    if chunk_data["delta"]["type"] == "text_delta":
                        content = chunk_data["delta"]["text"]

            if content:
                buffer += content  # Accumulate content

                if first_chunk:
                    socketio.emit(
                        "message_chunk",
                        {
                            "id": msg_id,
                            "content": content,
                            "username": username,
                            "model_name": model_name,
                            "is_first_chunk": True,
                        },
                        room=room_name,
                    )
                    first_chunk = False
                else:
                    socketio.emit(
                        "message_chunk",
                        {"id": msg_id, "content": content},
                        room=room_name,
                    )
                socketio.sleep(0)  # Force immediate handling

    except Exception as e:
        with app.app_context():
            message_content = f"AWS Bedrock Error: {e}"
            new_message = (
                db.session.query(Message).filter(Message.id == msg_id).one_or_none()
            )
            if new_message:
                new_message.content = message_content
                new_message.count_tokens()
                db.session.add(new_message)
                db.session.commit()
        socketio.emit(
            "chat_message",
            {
                "id": msg_id,
                "username": model_name,
                "content": message_content,
            },
            room=room_name,
        )
        socketio.emit("delete_processing_message", msg_id, room=room_name)
        # exit early to avoid clobbering the error message.
        return None

    # Save the entire completion to the database
    with app.app_context():
        new_message = (
            db.session.query(Message).filter(Message.id == msg_id).one_or_none()
        )
        if new_message:
            new_message.content = buffer
            new_message.count_tokens()
            db.session.add(new_message)
            db.session.commit()

    socketio.emit(
        "message_chunk",
        {"id": msg_id, "content": "", "is_complete": True},
        room=room_name,
    )

    socketio.emit("delete_processing_message", msg_id, room=room_name)


def chat_gpt(username, room_name, model_name="gpt-4o-mini", enable_thinking=True):
    openai_client, model_name = get_openai_client_and_model(model_name)

    temperature = 0
    limit = 20
    if "gpt-4" in model_name:
        limit = 1000
    if "o1-" in model_name:
        temperature = 1
    if "o3-" in model_name:
        temperature = 1
    if "o4-" in model_name:
        temperature = 1

    # Check if this is a vision-capable model
    vision_enabled = is_vision_model(model_name)
    if vision_enabled:
        print(f"Vision model detected: {model_name}")

    with app.app_context():
        room = get_room(room_name)
        room_id = room.id  # Save room_id to avoid DetachedInstanceError later
        last_messages = (
            Message.query.filter_by(room_id=room_id)
            .order_by(Message.id.desc())
            .limit(limit)
            .all()
        )

        chat_history = []

        # Find the most recent image message ID (only this one gets base64)
        most_recent_image_id = None
        if vision_enabled:
            for msg in last_messages:  # newest first
                is_image_msg = msg.is_base64_image() or extract_external_image_url(msg.content)
                if is_image_msg:
                    most_recent_image_id = msg.id
                    print(f"Vision: will include base64 for most recent image (msg {msg.id})")
                    break

        # Build chat history (oldest first) - include all text, only most recent image
        for msg in reversed(last_messages):
            is_image_msg = msg.is_base64_image() or extract_external_image_url(msg.content)

            # Skip ALL images for non-vision models
            if is_image_msg and not vision_enabled:
                continue

            role = "assistant" if msg.username in SYSTEM_USERS else "user"

            # For vision: only include base64 for the most recent image
            # Older images are skipped entirely (they're just base64, no useful text)
            if vision_enabled and is_image_msg:
                if msg.id == most_recent_image_id:
                    content = build_message_content(msg, vision_enabled, room_id=room_id)
                    chat_history.append({"role": role, "content": content})
                # Skip older image messages - they have no text context
                continue

            # Regular text message - always include
            chat_history.append({"role": role, "content": msg.content})

    buffer = ""  # Content buffer for accumulating the chunks

    # save empty message, we need the ID when we chunk the response.
    with app.app_context():
        new_message = Message(username=model_name, content=buffer, room_id=room_id)
        db.session.add(new_message)
        db.session.commit()
        msg_id = new_message.id

    first_chunk = True

    create_kwargs = {
        "model": model_name,
        "messages": chat_history,
        "n": 1,
        "stream": True,
    }
    if "o3" not in model_name:
        # o3 does not support temperature at all!
        create_kwargs["temperature"] = temperature
    if not enable_thinking:
        # Qwen3 / vLLM-style switch to suppress chain-of-thought and save
        # tokens. OpenAI's hosted API rejects unknown body params, so only
        # send to self-hosted OpenAI-compatible endpoints; o1/o3 think
        # unconditionally and would 400 on this anyway. Models whose chat
        # template ignores the kwarg (e.g. Hermes) simply drop it.
        base_url = str(getattr(openai_client, "base_url", "") or "")
        if "api.openai.com" not in base_url:
            create_kwargs["extra_body"] = {
                "chat_template_kwargs": {"enable_thinking": False}
            }

    try:
        chunks = openai_client.chat.completions.create(**create_kwargs)
    except Exception as e:
        with app.app_context():
            message_content = f"{model_name} Error: {e}"
            new_message = (
                db.session.query(Message).filter(Message.id == msg_id).one_or_none()
            )
            if new_message:
                new_message.content = message_content
                new_message.count_tokens()
                db.session.add(new_message)
                db.session.commit()
        socketio.emit(
            "chat_message",
            {
                "id": msg_id,
                "username": model_name,
                "content": message_content,
            },
            room=room_name,
        )
        socketio.emit("delete_processing_message", msg_id, room=room_name)
        # exit early to avoid clobbering the error message.
        return None

    for chunk in chunks:
        # Check if there has been a cancellation request, break if there is.
        if cancellation_requests.get(msg_id):
            del cancellation_requests[msg_id]
            break

        delta = chunk.choices[0].delta
        # Qwen3.x / Deepseek-R1 / o1: thinking streams via delta.reasoning_content,
        # final answer via delta.content. Forward reasoning deltas to the client
        # so it can lazy-create a collapsible thinking block; do NOT persist them
        # (transient, model-private intermediate state).
        reasoning = getattr(delta, "reasoning_content", None)
        if reasoning:
            socketio.emit(
                "message_chunk",
                {"id": msg_id, "reasoning_content": reasoning},
                room=room_name,
            )
            socketio.sleep(0)

        content = delta.content

        if content:
            buffer += content  # Accumulate content

            if first_chunk:
                socketio.emit(
                    "message_chunk",
                    {
                        "id": msg_id,
                        "content": content,
                        "username": username,
                        "model_name": model_name,
                        "is_first_chunk": True,
                    },
                    room=room_name,
                )
                first_chunk = False
            else:
                socketio.emit(
                    "message_chunk",
                    {"id": msg_id, "content": content},
                    room=room_name,
                )
            socketio.sleep(0)  # Force immediate handling

    # Save the entire completion to the database
    with app.app_context():
        new_message = (
            db.session.query(Message).filter(Message.id == msg_id).one_or_none()
        )
        if new_message:
            new_message.content = buffer
            new_message.count_tokens()
            db.session.add(new_message)
            db.session.commit()

    socketio.emit(
        "message_chunk",
        {"id": msg_id, "content": "", "is_complete": True},
        room=room_name,
    )

    socketio.emit("delete_processing_message", msg_id, room=room_name)


def chat_llama(username, room_name, model_name="mistral-7b-instruct-v0.2.Q3_K_L.gguf"):
    import llama_cpp

    # https://llama-cpp-python.readthedocs.io/en/latest/api-reference/
    model = llama_cpp.Llama(model_name, n_gpu_layers=-1, n_ctx=32000)

    limit = 15
    with app.app_context():
        room = get_room(room_name)
        last_messages = (
            Message.query.filter_by(room_id=room.id)
            .order_by(Message.id.desc())
            .limit(limit)
            .all()
        )

        chat_history = [
            {
                "role": "system" if msg.username in SYSTEM_USERS else "user",
                "content": f"{msg.username}: {msg.content}",
            }
            for msg in reversed(last_messages)
            if not msg.is_base64_image()
        ]

    buffer = ""  # Content buffer for accumulating the chunks

    # save empty message, we need the ID when we chunk the response.
    with app.app_context():
        new_message = Message(username=model_name, content=buffer, room_id=room.id)
        db.session.add(new_message)
        db.session.commit()
        msg_id = new_message.id

    first_chunk = True

    try:
        chunks = model.create_chat_completion(
            messages=chat_history,
            stream=True,
        )
    except Exception as e:
        with app.app_context():
            message_content = f"LLama Error: {e}"
            new_message = (
                db.session.query(Message).filter(Message.id == msg_id).one_or_none()
            )
            if new_message:
                new_message.content = message_content
                new_message.count_tokens()
                db.session.add(new_message)
                db.session.commit()
        socketio.emit(
            "chat_message",
            {
                "id": msg_id,
                "username": model_name,
                "content": message_content,
            },
            room=room_name,
        )
        socketio.emit("delete_processing_message", msg_id, room=room_name)
        # exit early to avoid clobbering the error message.
        return None

    for chunk in chunks:
        # Check if there has been a cancellation request, break if there is.
        if cancellation_requests.get(msg_id):
            del cancellation_requests[msg_id]
            break

        delta = chunk["choices"][0]["delta"]
        # See OpenAI-client path above for rationale on reasoning_content.
        reasoning = delta.get("reasoning_content")
        if reasoning:
            socketio.emit(
                "message_chunk",
                {"id": msg_id, "reasoning_content": reasoning},
                room=room_name,
            )
            socketio.sleep(0)

        content = delta.get("content")

        if content:
            buffer += content  # Accumulate content

            if first_chunk:
                socketio.emit(
                    "message_chunk",
                    {
                        "id": msg_id,
                        "content": content,
                        "username": username,
                        "model_name": model_name,
                        "is_first_chunk": True,
                    },
                    room=room_name,
                )
                first_chunk = False
            else:
                socketio.emit(
                    "message_chunk",
                    {"id": msg_id, "content": content},
                    room=room_name,
                )
            socketio.sleep(0)  # Force immediate handling

    # Save the entire completion to the database
    with app.app_context():
        new_message = (
            db.session.query(Message).filter(Message.id == msg_id).one_or_none()
        )
        if new_message:
            new_message.content = buffer
            new_message.count_tokens()
            db.session.add(new_message)
            db.session.commit()

    socketio.emit(
        "message_chunk",
        {"id": msg_id, "content": "", "is_complete": True},
        room=room_name,
    )

    socketio.emit("delete_processing_message", msg_id, room=room_name)


def gpt_generate_room_title(messages):
    """
    Generate a title for the room based on a list of messages.
    """
    openai_client, model_name = get_openai_client_and_model()

    chat_history = [
        {
            "role": "system" if msg.username in SYSTEM_USERS else "user",
            "content": f"{msg.username}: {msg.content}",
        }
        for msg in reversed(messages)
        if not msg.is_base64_image()
    ]

    chat_history.append(
        {
            "role": "system",
            "content": "return a short title for the title bar of this conversation.",
        }
    )

    # Interaction with LLM to generate summary
    # For example, using OpenAI's GPT model
    response = openai_client.chat.completions.create(
        messages=chat_history,
        model=model_name,  # or any appropriate model
        max_tokens=20,
        n=1,
    )

    title = response.choices[0].message.content
    return title.replace('"', "")


def generate_new_title(room_name, username):
    with app.app_context():
        room = get_room(room_name)
        # Get the last few messages to generate a title
        last_messages = (
            Message.query.filter_by(room_id=room.id)
            .order_by(Message.id.desc())
            .limit(1000)  # Adjust the limit as needed
            .all()
        )

        # Generate the title using the messages
        new_title = gpt_generate_room_title(last_messages)

        # Update the room title in the database
        room.title = new_title
        db.session.add(room)
        db.session.commit()

        # Emit the new title to the room.
        socketio.emit("update_room_title", {"title": new_title}, room=room_name)

        # Emit an event to update this rooms title in the sidebar for all users.
        updated_room_data = {"id": room.id, "name": room.name, "title": room.title}
        socketio.emit("update_room_list", updated_room_data, room=None)

        # Optionally, send a confirmation message to the room
        confirmation_message = f"New title created: {new_title}"
        new_message = Message(
            username=username, content=confirmation_message, room_id=room.id
        )
        db.session.add(new_message)
        db.session.commit()
        socketio.emit(
            "chat_message",
            {
                "id": new_message.id,
                "username": username,
                "content": confirmation_message,
            },
            room=room_name,
        )


def generate_dalle_image(room_name, message, username):
    socketio.emit(
        "chat_message",
        {"id": None, "content": "Processing..."},
        room=room_name,
    )

    openai_client = OpenAI()
    # Initialize the content variable to hold either the image tag or an error message
    content = ""

    try:
        # Call the DALL-E 3 API to generate an image in base64 format
        response = openai_client.images.generate(
            model="dall-e-3",
            prompt=message,
            n=1,
            size="1024x1024",
            response_format="b64_json",
        )

        # Access the base64-encoded image data
        image_data = response.data[0].b64_json
        revised_prompt = response.data[0].revised_prompt

        # Create an HTML img tag with the base64 data (escape user input for XSS protection)
        import html

        escaped_message = html.escape(message)
        escaped_prompt = html.escape(revised_prompt)
        content = f'<img src="data:image/jpeg;base64,{image_data}" alt="{escaped_message}"><p>{escaped_prompt}</p>'

    except Exception as e:
        # Set the content to an error message
        content = f"Error generating image: {e}"

    # Store the content in the database and emit to the frontend
    with app.app_context():
        room = get_room(room_name)
        new_message = Message(
            username=username,
            content=content,  # Store the img tag or error message as the content
            room_id=room.id,  # Make sure you have the room ID available
        )
        db.session.add(new_message)
        db.session.commit()

        # Emit the message with the content to the frontend
        socketio.emit(
            "chat_message",
            {"id": new_message.id, "username": username, "content": content},
            room=room_name,
        )


def find_most_recent_code_block(room_name):
    with app.app_context():
        # Get the room object from the database
        room = get_room(room_name)
        if not room:
            return None  # Room not found

        # Get the most recent message for the room
        latest_message = (
            Message.query.filter_by(room_id=room.id)
            .order_by(Message.id.desc())
            .offset(1)
            .first()
        )

    if latest_message:
        # Split the message content into lines
        lines = latest_message.content.split("\n")
        # Initialize variables to store the code block
        code_block_lines = []
        code_block_started = False
        for line in lines:
            # Check if the line starts with a code block fence
            if line.startswith("```"):
                # If we've already started capturing, this fence ends the block
                if code_block_started:
                    break
                else:
                    # Start capturing from the next line
                    code_block_started = True
                    continue
            elif code_block_started:
                # If we're inside a code block, capture the line
                code_block_lines.append(line)

        # Join the captured lines to form the code block content
        code_block_content = "\n".join(code_block_lines)
        return code_block_content

    # No code block found in the latest message
    return None


def save_code_block_to_s3(room_name, s3_key_path, username):
    # Initialize the S3 client
    s3_client = get_s3_client()

    # Assuming the bucket name is set in an environment variable
    bucket_name = os.environ.get("S3_BUCKET_NAME")

    # Find the most recent code block
    code_block_content = find_most_recent_code_block(room_name)

    # Initialize a variable to hold the message content
    message_content = ""

    if code_block_content:
        try:
            # Save the code block content to S3
            s3_client.put_object(
                Bucket=bucket_name, Key=s3_key_path, Body=code_block_content
            )
            # Set the success message content
            message_content = f"Code block saved to S3 at {s3_key_path}"
        except Exception as e:
            # Set the error message content if S3 save fails
            message_content = f"Error saving file to S3: {e}"
    else:
        # Set the error message content if no code block is found
        message_content = "No code block found to save to S3."

    # Save the message to the database and emit to the frontend
    with app.app_context():
        # Get the room object from the database
        room = get_room(room_name)
        if room:
            # Create a new message object
            new_message = Message(
                username=username, content=message_content, room_id=room.id
            )
            # Add the new message to the session and commit
            db.session.add(new_message)
            db.session.commit()

            # Emit the message to the frontend with the new message ID
            socketio.emit(
                "chat_message",
                {
                    "id": new_message.id,
                    "username": username,
                    "content": message_content,
                },
                room=room_name,
            )


def load_s3_file(room_name, s3_file_path, username):
    # Initialize the S3 client
    s3_client = get_s3_client()

    # Assuming the bucket name is set in an environment variable
    bucket_name = os.environ.get("S3_BUCKET_NAME")

    # Initialize message content variable
    message_content = ""

    try:
        # Retrieve the file content from S3
        response = s3_client.get_object(Bucket=bucket_name, Key=s3_file_path)
        file_content = response["Body"].read().decode("utf-8")

        # Format the file content as a code block
        message_content = f"```\n{file_content}\n```"

    except Exception as e:
        # Handle errors (e.g., file not found, access denied)
        message_content = f"Error loading file from S3: {e}"

    # Save the message to the database and emit to the chatroom
    with app.app_context():
        room = get_room(room_name)
        new_message = Message(
            username=username,
            content=message_content,
            room_id=room.id,
        )
        db.session.add(new_message)
        db.session.commit()

        # Emit the message to the chatroom with the message ID
        socketio.emit(
            "chat_message",
            {
                "id": new_message.id,
                "username": username,
                "content": message_content,
            },
            room=room_name,
        )


def list_s3_files(room_name, s3_file_path_pattern, username):
    import fnmatch
    from datetime import timezone

    # Initialize the S3 client
    s3_client = get_s3_client()

    # Assuming the bucket name is set in an environment variable
    bucket_name = os.environ.get("S3_BUCKET_NAME")

    # Initialize the list to hold all file information
    files = []

    # Initialize the pagination token
    continuation_token = None

    # Loop to handle pagination
    while True:
        # List objects in the S3 bucket with pagination support
        list_kwargs = {
            "Bucket": bucket_name,
        }
        if continuation_token:
            list_kwargs["ContinuationToken"] = continuation_token

        response = s3_client.list_objects_v2(**list_kwargs)

        # Process the current page of results
        for obj in response.get("Contents", []):
            key = obj["Key"]
            if s3_file_path_pattern == "*" or fnmatch.fnmatch(
                key, s3_file_path_pattern
            ):
                size = obj["Size"]
                last_modified = obj["LastModified"]
                # Convert last_modified to a timezone-aware datetime object
                last_modified = (
                    last_modified.replace(tzinfo=timezone.utc)
                    .astimezone(tz=None)
                    .strftime("%Y-%m-%d %H:%M:%S %Z")
                )
                files.append(
                    f"{key} (Size: {size} bytes, Last Modified: {last_modified})"
                )

        # Check if there are more pages
        if response.get("IsTruncated"):
            continuation_token = response.get("NextContinuationToken")
        else:
            break  # No more pages

    # Format the message content with the list of files and metadata
    message_content = (
        "```\n" + "\n".join(files) + "\n```" if files else "No files found."
    )

    # Save the message to the database and emit to the chatroom
    with app.app_context():
        room = Room.query.filter_by(name=room_name).first()
        if room:
            new_message = Message(
                username=username,
                content=message_content,
                room_id=room.id,
            )
            db.session.add(new_message)
            db.session.commit()

            # Emit the message to the chatroom with the message ID
            socketio.emit(
                "chat_message",
                {
                    "id": new_message.id,
                    "username": username,
                    "content": message_content,
                },
                room=room_name,
            )


def cancel_generation(room_name):
    with app.app_context():
        room = get_room(room_name)
        # Get the most recent message for the room that is being generated
        latest_message = (
            Message.query.filter_by(room_id=room.id)
            .order_by(Message.id.desc())
            .offset(1)
            .first()
        )

    if latest_message:
        # Set the cancellation request for the given message ID
        cancellation_requests[latest_message.id] = True
        # Optionally, inform the user that the generation has been canceled
        socketio.emit(
            "chat_message",
            {
                "id": None,
                "username": "System",
                "content": f"Generation for message ID {latest_message.id} has been canceled.",
            },
            room=room_name,
        )


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(
        description="Run the SocketIO application with optional configurations."
    )
    parser.add_argument("--profile", help="AWS profile name", default=None)
    parser.add_argument(
        "--local-activities",
        action="store_true",
        help="Use local activity files instead of S3",
    )
    parser.add_argument(
        "--port",
        type=int,
        default=5001,
        help="Port number to run the SocketIO server on (default: 5001)",
    )
    args = parser.parse_args()
    # Set profile_name and other configurations as global attributes of the app object
    app.config["PROFILE_NAME"] = args.profile
    app.config["LOCAL_ACTIVITIES"] = args.local_activities

    # Run the SocketIO server with the specified port
    # Disable reloader to avoid gevent fork compatibility issues
    socketio.run(app, host="0.0.0.0", port=args.port, use_reloader=False)