# myapp/dialog/dialog_routes.py

import datetime
import os
import logging
from flask import request, jsonify, current_app, has_app_context # Flask utilities
from bson.objectid import ObjectId, InvalidId # For MongoDB ObjectIds
from functools import wraps # Import wraps for dummy decorator


# --- Local Blueprint Import ---
from . import bp # Import the 'bp' instance defined in the local __init__.py

# --- Shared Extensions and Utilities Imports ---
try:
    from ..extensions import mongo  # Import the initialized PyMongo instance
    from ..utils import token_required  # Import the authentication decorator
except ImportError:
    # Fallback or error handling if imports fail
    print("Warning: Could not import mongo or token_required in dialog/dialog_routes.py.")
    mongo = None
    # Define a dummy decorator if token_required is missing
    def token_required(f):
        @wraps(f)
        def wrapper(*args, **kwargs):
            print("ERROR: token_required decorator is not available!")
            return jsonify({"message": "Server configuration error: Missing authentication utility."}), 500
        return wrapper

# --- Schema Imports ---
try:
    # Import the relevant schemas defined in schemas.py
    from ..schemas import (
        DialogCreateSchema, DialogSendMessageSchema,
        DialogSchema, DialogSummarySchema
    )
    from marshmallow import ValidationError
except ImportError:
    print("Warning: Could not import Dialog schemas or ValidationError in dialog/dialog_routes.py.")
    DialogCreateSchema = None
    DialogSendMessageSchema = None
    DialogSchema = None
    DialogSummarySchema = None
    ValidationError = None # Define ValidationError as None if import fails


# --- External API and Langchain Imports ---
# Keep these imports conditional to avoid errors if libraries are not installed
try:
    import google.generativeai as genai
    from google.api_core import exceptions as google_exceptions
except ImportError:
    print("Warning: google.generativeai not installed. Gemini functionality will fail.")
    genai = None
    google_exceptions = None

try:
    from langchain_community.embeddings import OpenAIEmbeddings
    from langchain_community.vectorstores import FAISS
    from langchain.docstore.document import Document
except ImportError:
    print("Warning: Langchain components not installed. Vector store functionality will fail.")
    OpenAIEmbeddings = None
    FAISS = None
    Document = None

# --- Constants ---
# Adjust model name if needed, potentially move to config
MAX_HISTORY_MESSAGES = 20 # Max messages to keep in history for context (if applicable, not used in current Gemini call)

# --- Helper to get logger safely ---
def _get_logger():
    if has_app_context():
        return current_app.logger
    return logging.getLogger(__name__)


##################################################
# Helper Functions (kept within this module)
##################################################

# --- Knowledge Base Helpers ---
# (generate_knowledge_base_message, update_project_knowledge, process_api_response_and_update_knowledge - unchanged from previous version)
def generate_knowledge_base_message(project_id):
    """
    Retrieves all URL documents for the given project and formats them
    into a single knowledge base message string for context.

    Args:
        project_id (ObjectId): The ObjectId of the project.

    Returns:
        str: A formatted string containing knowledge base entries, or an empty string on error.
             Returns "No external knowledge available." if no URLs are found.
    """
    logger = _get_logger()
    if not mongo:
        logger.error("Mongo extension not available in generate_knowledge_base_message.")
        return "" # Cannot proceed without DB connection

    try:
        # Ensure project_id is ObjectId
        if not isinstance(project_id, ObjectId):
             project_id = ObjectId(project_id)

        # Find all URL documents linked to the project ID
        urls_cursor = mongo.db.urls.find({"projectId": project_id})
        knowledge_entries = []
        for doc in urls_cursor:
            # Format keywords with percentages
            keywords_list = [f"{kw.get('word', '')}({kw.get('percentage', 'N/A')}%)" for kw in doc.get("keywords", [])]
            # Aggregate relevant fields into a string entry
            aggregated = (
                f"Title: {doc.get('title', 'N/A')}\n"
                f"URL: {doc.get('url', 'N/A')}\n"
                # f"Starred: {doc.get('starred', False)}\n" # Optionally include starred status
                f"Note: {doc.get('note', 'N/A')}\n"
                f"Keywords: {', '.join(keywords_list) if keywords_list else 'N/A'}\n"
                f"Summary: {doc.get('summary', 'N/A')}"
            )
            knowledge_entries.append(aggregated)

        # Handle case where no URLs are found
        if not knowledge_entries:
            return "No external knowledge available for this project."

        # Combine entries and truncate if necessary
        combined = "\n\n---\n\n".join(knowledge_entries)
        # Use Flask config for max length if available, otherwise default
        max_length = current_app.config.get('KNOWLEDGE_BASE_MAX_LENGTH', 4000) if has_app_context() else 4000
        if len(combined) > max_length:
            combined = combined[:max_length] + " ... [truncated]"
        return combined

    except InvalidId:
         logger.error(f"Invalid project_id format passed to generate_knowledge_base_message: {project_id}")
         return "Error: Invalid project identifier."
    except AttributeError:
         logger.error("PyMongo extension not initialized or available.")
         return "Error: Database configuration issue."
    except Exception as e:
        # Log the error with project ID for easier debugging
        logger.error(f"Error generating knowledge base message for project {project_id}: {e}", exc_info=True)
        return "" # Return empty string on generic error


def update_project_knowledge(project_id):
    """
    Updates the project's 'summary' field with a condensed version of its knowledge base.
    This acts as a cache or snapshot for quick reference.

    Args:
        project_id (ObjectId): The ObjectId of the project.
    """
    logger = _get_logger()
    if not mongo:
        logger.error("Mongo extension not available in update_project_knowledge.")
        return
    try:
        # Ensure project_id is ObjectId
        if not isinstance(project_id, ObjectId):
             project_id = ObjectId(project_id)

        knowledge_message = generate_knowledge_base_message(project_id)
        # Condense the message for storage (e.g., first 1000 chars)
        condensed = knowledge_message[:1000] if len(knowledge_message) > 1000 else knowledge_message

        # Update the project document in the 'projects' collection
        mongo.db.projects.update_one(
            {"_id": project_id},
            {"$set": {"summary": condensed, "updatedAt": datetime.datetime.now(datetime.timezone.utc)}}
        )
    except InvalidId:
         logger.error(f"Invalid project_id format passed to update_project_knowledge: {project_id}")
    except AttributeError:
         logger.error("PyMongo extension not initialized or available.")
    except Exception as e:
        logger.error(f"Error updating project knowledge cache for {project_id}: {e}", exc_info=True)


def process_api_response_and_update_knowledge(api_response, project_id):
    """
    Placeholder function to process LLM responses. Currently updates project knowledge cache.
    """
    # For now, simply update the cached summary in the project document
    update_project_knowledge(project_id)
    # Future enhancements could go here


# --- Vector Store Helpers ---
def build_vector_knowledge_base(project_id, query, k=3):
    """
    Builds a vector index (FAISS) from project URL content and retrieves top-k relevant documents.

    Args:
        project_id (ObjectId): The ObjectId of the project.
        query (str): The user query for similarity search.
        k (int): The number of top similar documents to retrieve.

    Returns:
        List[Document]: A list of LangChain Document objects, or an empty list on error/no data.
    """
    logger = _get_logger()
    # Check if necessary components are available
    if not mongo or not OpenAIEmbeddings or not FAISS or not Document:
        logger.error("Missing dependencies (Mongo, Langchain) for build_vector_knowledge_base.")
        return []

    try:
        # Ensure project_id is ObjectId
        if not isinstance(project_id, ObjectId):
             project_id = ObjectId(project_id)

        # Fetch URL documents from MongoDB
        urls_cursor = mongo.db.urls.find({"projectId": project_id})
        texts = []
        metadatas = []

        for doc in urls_cursor:
            # Aggregate text content for embedding
            keywords_list = [f"{kw.get('word', '')}({kw.get('percentage', 'N/A')}%)" for kw in doc.get("keywords", [])]
            aggregated = (
                f"Title: {doc.get('title', 'N/A')}\n"
                f"URL: {doc.get('url', 'N/A')}\n"
                # f"Starred: {doc.get('starred', False)}\n" # Optionally include more fields
                f"Note: {doc.get('note', 'N/A')}\n"
                f"Keywords: {', '.join(keywords_list) if keywords_list else 'N/A'}\n"
                f"Summary: {doc.get('summary', 'N/A')}"
            )
            texts.append(aggregated)
            # Store relevant metadata alongside the text
            metadatas.append({"url": doc.get("url", ""), "title": doc.get("title", ""), "doc_id": str(doc["_id"])})

        # If no text content found, return empty list
        if not texts:
            logger.info(f"No URL text content found for project {project_id} to build vector base.")
            return []

        # Initialize embeddings model (ensure OPENAI_API_KEY is set in environment or config)
        try:
            # Check if OPENAI_API_KEY exists (more robust check)
            openai_api_key = os.environ.get("OPENAI_API_KEY") or (current_app.config.get("OPENAI_API_KEY") if has_app_context() else None)
            if not openai_api_key:
                 raise ValueError("OPENAI_API_KEY environment variable or Flask config not set.")
            embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
        except Exception as e:
             logger.error(f"Failed to initialize OpenAIEmbeddings: {e}. Check API key.", exc_info=False) # Avoid logging key
             return []


        # Build FAISS vector store from the texts and metadata
        vectorstore = FAISS.from_texts(texts=texts, embedding=embeddings, metadatas=metadatas)

        # Perform similarity search
        docs = vectorstore.similarity_search(query, k=k)
        return docs

    except InvalidId:
         logger.error(f"Invalid project_id format passed to build_vector_knowledge_base: {project_id}")
         return []
    except AttributeError:
         logger.error("PyMongo or Langchain components not initialized or available.")
         return []
    except Exception as e:
        logger.error(f"Error building vector knowledge base for project {project_id}: {e}", exc_info=True)
        return [] # Return empty list on error


def build_vector_based_prompt_with_knowledge(user_message, project_id):
    """
    Constructs a prompt for the LLM, incorporating context from vector search results.

    Args:
        user_message (str): The user's latest message/query.
        project_id (ObjectId): The ObjectId of the project.

    Returns:
        str: The formatted prompt string including retrieved knowledge.
    """
    # Retrieve top 3 relevant documents using vector search
    retrieved_docs = build_vector_knowledge_base(project_id, user_message, k=3)

    # Format the retrieved knowledge for inclusion in the prompt
    if retrieved_docs:
        # Join the page_content of each retrieved LangChain Document
        knowledge_text = "\n\n---\n\n".join([f"Source URL: {doc.metadata.get('url', 'N/A')}\n{doc.page_content}" for doc in retrieved_docs])
    else:
        knowledge_text = "No relevant external knowledge found via vector search for this query."

    # Construct the final prompt with instructions, knowledge, and user query
    # Make prompt more specific about using ONLY the provided knowledge
    prompt = (
        "You are an expert research assistant. Analyze the following retrieved documents, which contain information "
        "(titles, URLs, notes, keywords, summaries) from websites related to the current research project. "
        "Base your response *only* on this provided information and the user's query.\n\n"
        "Common user questions might involve:\n"
        "- Summarizing key topics from the retrieved documents.\n"
        "- Suggesting research directions based *only* on the retrieved documents.\n"
        "- Recommending specific URLs *from the retrieved documents* that are most relevant.\n"
        "- Identifying potentially redundant information *within the retrieved documents*.\n\n"
        "--- Relevant Retrieved Knowledge ---\n"
        f"{knowledge_text}\n"
        "--- End Retrieved Knowledge ---\n\n"
        "User Query:\n"
        f"{user_message}\n\n"
        "Based strictly on the retrieved knowledge and the user query, provide your analysis and recommendations:"
    )
    return prompt


# --- Gemini Message Formatting Helper (Not currently used by send_dialog_message) ---
def format_messages_for_gemini(db_messages, max_history=MAX_HISTORY_MESSAGES):
    """
    Converts dialog history from DB format to Gemini API format.
    Handles role mapping ('system' -> 'model') and ensures role alternation.
    """
    logger = _get_logger()
    contents = []
    last_role = None
    recent_messages = db_messages[-max_history:] # Get the most recent messages

    for msg in recent_messages:
        gemini_role = "model" if msg.get("role") == "system" else "user"
        if gemini_role == last_role:
            logger.warning(f"Skipping consecutive message of role '{gemini_role}' in formatting.")
            continue
        contents.append({
            "role": gemini_role,
            "parts": [{"text": msg.get("content", "")}]
        })
        last_role = gemini_role

    if contents and contents[-1]["role"] != "user":
        logger.warning("Formatted history for Gemini does not end with a 'user' message.")
    return contents


##################################################
# Dialog API Endpoints
##################################################

# Note: Routes use paths relative to the '/api/dialog' prefix.

@bp.route('/', methods=['POST']) # Path relative to prefix
@token_required
def create_dialog_session(current_user):
    """
    Creates a new dialog session associated with a project.
    Uses DialogCreateSchema for input validation.
    Expects JSON: { "projectId": "<ObjectId_string>", "sessionId": "<optional_string>", "startMessage": "<optional_string>" }
    Determines the LLM provider based on the user's selected API key.
    """
    logger = _get_logger()
    # Validate user
    if not current_user or not current_user.get("_id"):
        return jsonify({"message": "Internal authorization error."}), 500
    try:
        user_id = ObjectId(current_user["_id"])
    except (InvalidId, TypeError) as e:
        logger.error(f"User ID conversion error in create_dialog_session: {e}")
        return jsonify({"message": "Invalid user ID format in token."}), 400

    # Check dependencies
    if not mongo: return jsonify({"message": "Database connection not available."}), 500
    if not DialogCreateSchema or not ValidationError:
         return jsonify({"message": "Server configuration error: Schema unavailable."}), 500

    # Get and validate JSON data using the schema
    json_data = request.get_json() or {}
    schema = DialogCreateSchema()
    try:
        validated_data = schema.load(json_data)
    except ValidationError as err:
        logger.warning(f"Create dialog session validation failed: {err.messages}")
        return jsonify(err.messages), 422 # Return validation errors

    # Extract validated data
    project_id_str = validated_data['projectId'] # Already validated as ObjectId string by schema
    session_id = validated_data.get("sessionId", "") # Optional
    start_message = validated_data.get("startMessage", "").strip() # Optional

    try:
        # Convert project ID
        project_obj_id = ObjectId(project_id_str) # Conversion should succeed due to schema validation

        # Find the user's selected API key
        db = mongo.db
        selected_api = db.api_list.find_one({"uid": user_id, "selected": True})
        if not selected_api:
            return jsonify({"message": "User has no selected API provider. Please select one in API Keys."}), 400

        provider = selected_api.get("name")
        api_key_exists = bool(selected_api.get("key")) # Check if key value exists

        # Validate provider and key presence
        allowed_providers = ["Gemini", "Deepseek", "Chatgpt"] # Consider from config
        if provider not in allowed_providers:
            return jsonify({"message": f"Selected provider '{provider}' is not supported."}), 400
        if not api_key_exists:
             return jsonify({"message": f"API key value missing for selected provider '{provider}'."}), 400

        # Verify project exists and user has access
        project = db.projects.find_one({"_id": project_obj_id}, {"ownerId": 1, "collaborators": 1})
        if not project:
             return jsonify({"message": "Project not found."}), 404
        owner_id = project.get("ownerId")
        collaborators = project.get("collaborators", [])
        if owner_id != user_id and user_id not in collaborators:
             return jsonify({"message": "Access denied to the specified project."}), 403

        # Prepare initial messages if startMessage exists
        now = datetime.datetime.now(datetime.timezone.utc)
        messages_array = []
        if start_message:
            messages_array.append({
                "role": "user",
                "content": start_message,
                "timestamp": now # Store timestamp for messages
            })

        # Prepare the new dialog document
        dialog_doc = {
            "uid": user_id,
            "projectId": project_obj_id,
            "provider": provider, # Store the provider used for this session
            "sessionStartedAt": now,
            "sessionEndedAt": None, # Mark as null initially
            "messages": messages_array
        }
        if session_id: dialog_doc["sessionId"] = session_id

        # Insert the new dialog session
        result = db.dialog_activity.insert_one(dialog_doc)

        # Return success response with the new dialog ID
        return jsonify({
            "message": "Dialog session created successfully.",
            "dialog_id": str(result.inserted_id)
        }), 201

    except KeyError: # Should be caught by initial user_id check
        logger.error(f"User ID (_id) not found in token payload for create_dialog_session.")
        return jsonify({"message": "Authentication token is invalid or missing user ID."}), 401
    except AttributeError:
         logger.error("PyMongo extension not initialized or attached correctly.")
         return jsonify({"message": "Database configuration error."}), 500
    except Exception as e:
        logger.error(f"Error creating dialog session for user {current_user.get('_id', 'UNKNOWN')}: {e}", exc_info=True)
        return jsonify({"message": "Internal server error creating dialog session."}), 500


@bp.route('/<string:dialog_id>/send', methods=['POST']) # Path relative to prefix
@token_required
def send_dialog_message(current_user, dialog_id):
    """
    Sends a user message within a specific dialog session.
    Uses DialogSendMessageSchema for input validation.
    Expects JSON: { "content": "User's message text" }
    Retrieves context using vector search, builds a prompt, calls the LLM (Gemini),
    and stores the conversation turn in the dialog history.
    """
    logger = _get_logger()
    # Check dependencies
    if not mongo: return jsonify({"message": "Database connection not available."}), 500
    if not genai or not google_exceptions: return jsonify({"message": "Gemini API library not available."}), 500
    if not DialogSendMessageSchema or not ValidationError:
         return jsonify({"message": "Server configuration error: Schema unavailable."}), 500

    try:
        # Validate IDs
        user_id_str = str(current_user.get("_id"))
        if not user_id_str: return jsonify({"message": "Missing user ID in token."}), 400
        try:
            user_id = ObjectId(user_id_str)
            dialog_obj_id = ObjectId(dialog_id)
        except InvalidId:
            return jsonify({"message": "Invalid user or dialog ID format."}), 400

        # Get and validate user message content using schema
        json_data = request.get_json() or {}
        schema = DialogSendMessageSchema()
        try:
            validated_data = schema.load(json_data)
        except ValidationError as err:
            logger.warning(f"Send dialog message validation failed: {err.messages}")
            return jsonify(err.messages), 422

        content = validated_data['content'] # Use validated content

        # --- Retrieve Dialog and API Key ---
        db = mongo.db
        dialog_doc = db.dialog_activity.find_one({"_id": dialog_obj_id, "uid": user_id})
        if not dialog_doc: return jsonify({"message": "Dialog session not found or access denied."}), 404
        if dialog_doc.get("sessionEndedAt"): return jsonify({"message": "This dialog session has ended."}), 409 # 409 Conflict

        provider = dialog_doc.get("provider")
        if provider != "Gemini": # This endpoint currently only supports Gemini
            return jsonify({"message": f"This endpoint only supports 'Gemini', but session provider is '{provider}'."}), 400

        # Find the active Gemini API key for the user
        api_doc = db.api_list.find_one({"uid": user_id, "name": "Gemini", "selected": True})
        if not (api_doc and api_doc.get("key")):
            logger.error(f"No valid Gemini key found or selected for user {user_id} during send message.")
            return jsonify({"message": "Gemini API key not configured or selected."}), 400
        gemini_key = api_doc["key"]

        # --- Build Prompt with Vector Knowledge ---
        project_id = dialog_doc.get("projectId")
        if not project_id or not isinstance(project_id, ObjectId):
             logger.error(f"Dialog {dialog_id} is missing valid projectId.")
             return jsonify({"message": "Internal error: Project reference missing."}), 500

        # This builds the prompt incorporating vector search results
        detailed_prompt = build_vector_based_prompt_with_knowledge(content, project_id)

        # Prepare history for Gemini (currently just the detailed prompt as a single user turn)
        gemini_history = [{"role": "user", "parts": [{"text": detailed_prompt}]}]

        # --- Call Gemini API ---
        llm_response_text = "[LLM Call Skipped/Failed]" # Default response text
        try:
            genai.configure(api_key=gemini_key)
            model = genai.GenerativeModel(current_app.config["GEMINI_MODEL_NAME"])
            # Consider adding generation_config and safety_settings from Flask config
            llm_response = model.generate_content(gemini_history)

            # Extract text, handling potential blocks or empty responses
            try:
                 llm_response_text = llm_response.text
            except ValueError:
                 logger.warning(f"Gemini response for dialog {dialog_id} may have been blocked or empty. Feedback: {llm_response.prompt_feedback}")
                 llm_response_text = "[Response blocked by safety filters or returned no text content]"

        except google_exceptions.PermissionDenied as ex:
            logger.warning(f"Gemini Permission Denied for user {user_id}: {ex}")
            return jsonify({"message": "Gemini API Error: Invalid API key or insufficient permissions."}), 403
        except google_exceptions.ResourceExhausted as ex:
            logger.warning(f"Gemini Resource Exhausted for user {user_id}: {ex}")
            return jsonify({"message": "Gemini API Error: Rate limit or quota exceeded."}), 429
        except google_exceptions.GoogleAPIError as ex: # Catch other Google API errors
            logger.error(f"Gemini API communication error for user {user_id}: {ex}", exc_info=True)
            return jsonify({"message": "An error occurred while communicating with the Gemini API."}), 503 # 503 Service Unavailable
        except Exception as e: # Catch potential genai configuration errors etc.
             logger.error(f"Unexpected error during Gemini call setup or execution for user {user_id}: {e}", exc_info=True)
             return jsonify({"message": "Internal server error during LLM communication."}), 500


        # --- Process Response and Update DB ---
        now = datetime.datetime.now(datetime.timezone.utc)
        user_msg_entry = {"role": "user", "content": content, "timestamp": now}
        system_msg_entry = {"role": "system", "content": llm_response_text, "timestamp": now} # Use same timestamp for pair

        # Add both messages to the dialog history in MongoDB atomically
        update_res = db.dialog_activity.update_one(
            {"_id": dialog_obj_id},
            {"$push": {"messages": {"$each": [user_msg_entry, system_msg_entry]}}}
        )
        if update_res.modified_count != 1:
            logger.warning(f"Dialog {dialog_id} DB update failed after LLM call (modified_count={update_res.modified_count}).")
            # Decide if this should be an error response to the user

        # Process the response (e.g., update cached knowledge)
        process_api_response_and_update_knowledge(llm_response_text, project_id)

        # Return the LLM's response text to the client
        return jsonify({"message": "LLM response received.", "llmResponse": llm_response_text}), 200

    except KeyError: # Should be caught by initial user_id check
        logger.error(f"User ID (_id) not found in token payload for send_dialog_message.")
        return jsonify({"message": "Authentication token is invalid or missing user ID."}), 401
    except AttributeError:
         logger.error("PyMongo or other extension not initialized correctly.")
         return jsonify({"message": "Server configuration error."}), 500
    except Exception as e:
        logger.error(f"Unexpected error in send_dialog_message for dialog {dialog_id}: {e}", exc_info=True)
        return jsonify({"message": "Internal server error processing message."}), 500


@bp.route('/', methods=['GET']) # Path relative to prefix
@token_required
def list_dialog_sessions(current_user):
    """
    Lists dialog sessions for the authenticated user.
    Uses DialogSummarySchema for output serialization.
    Supports filtering by 'projectId' query parameter.
    Excludes the 'messages' array for brevity.
    """
    logger = _get_logger()
    # Check dependencies
    if not mongo: return jsonify({"message": "Database connection not available."}), 500
    if not DialogSummarySchema: return jsonify({"message": "Server configuration error: Schema unavailable."}), 500

    try:
        # Validate user ID
        user_id_str = str(current_user.get("_id"))
        if not user_id_str: return jsonify({"message": "Missing user ID in token."}), 400
        try:
            user_id = ObjectId(user_id_str)
        except InvalidId:
            return jsonify({"message": "Invalid user ID format in token."}), 400

        # Base query for the user's dialogs
        query = {"uid": user_id}

        # Add projectId filter if provided in query parameters
        project_id_str = request.args.get("projectId")
        if project_id_str:
            try:
                project_obj_id = ObjectId(project_id_str)
                query["projectId"] = project_obj_id
            except InvalidId:
                return jsonify({"message": "Invalid projectId format in query parameter."}), 400

        # Fetch dialogs, excluding the messages field, sort by start time descending
        db = mongo.db
        cursor = db.dialog_activity.find(
            query,
            {"messages": 0} # Projection to exclude messages
        ).sort("sessionStartedAt", -1)

        dialog_docs = list(cursor) # Convert cursor to list

        # --- Serialize results using the schema ---
        output_schema = DialogSummarySchema(many=True)
        # Schema handles ObjectId and datetime conversion, and field exclusion
        serialized_result = output_schema.dump(dialog_docs)

        return jsonify({"dialogs": serialized_result}), 200

    except KeyError: # Should be caught by initial user_id check
        logger.error(f"User ID (_id) not found in token payload for list_dialog_sessions.")
        return jsonify({"message": "Authentication token is invalid or missing user ID."}), 401
    except AttributeError:
         logger.error("PyMongo extension not initialized or attached correctly.")
         return jsonify({"message": "Database configuration error."}), 500
    except Exception as e:
        logger.error(f"Error listing dialogs for user {current_user.get('_id', 'UNKNOWN')}: {e}", exc_info=True)
        return jsonify({"message": "Internal server error listing dialog sessions."}), 500


@bp.route('/<string:dialog_id>', methods=['GET']) # Path relative to prefix
@token_required
def get_dialog_session(current_user, dialog_id):
    """
    Retrieves the full details of a specific dialog session, including messages.
    Uses DialogSchema for output serialization. Verifies ownership.
    """
    logger = _get_logger()
    # Check dependencies
    if not mongo: return jsonify({"message": "Database connection not available."}), 500
    if not DialogSchema: return jsonify({"message": "Server configuration error: Schema unavailable."}), 500

    try:
        # Validate IDs
        user_id_str = str(current_user.get("_id"))
        if not user_id_str: return jsonify({"message": "Missing user ID in token."}), 400
        try:
            user_id = ObjectId(user_id_str)
            dial_obj_id = ObjectId(dialog_id)
        except InvalidId:
            return jsonify({"message": "Invalid user or dialog ID format."}), 400

        # Find the specific dialog owned by the user
        db = mongo.db
        doc = db.dialog_activity.find_one({"_id": dial_obj_id, "uid": user_id})
        if not doc:
            return jsonify({"message": "Dialog session not found or access denied."}), 404

        # --- Serialize results using the schema ---
        output_schema = DialogSchema()
        # Schema handles ObjectId, datetime, and nested message formatting
        serialized_result = output_schema.dump(doc)

        return jsonify(serialized_result), 200

    except KeyError: # Should be caught by initial user_id check
        logger.error(f"User ID (_id) not found in token payload for get_dialog_session.")
        return jsonify({"message": "Authentication token is invalid or missing user ID."}), 401
    except AttributeError:
         logger.error("PyMongo extension not initialized or attached correctly.")
         return jsonify({"message": "Database configuration error."}), 500
    except Exception as e:
        logger.error(f"Error retrieving dialog {dialog_id} for user {current_user.get('_id', 'UNKNOWN')}: {e}", exc_info=True)
        return jsonify({"message": "Internal server error retrieving dialog session."}), 500


@bp.route('/<string:dialog_id>/end', methods=['PUT']) # Path relative to prefix
@token_required
def end_dialog_session(current_user, dialog_id):
    """
    Marks a dialog session as ended by setting the 'sessionEndedAt' timestamp.
    Prevents ending an already ended session. Verifies ownership.
    (No schema needed for input/output here)
    """
    logger = _get_logger()
    if not mongo: return jsonify({"message": "Database connection not available."}), 500

    try:
        # Validate IDs
        user_id_str = str(current_user.get("_id"))
        if not user_id_str: return jsonify({"message": "Missing user ID in token."}), 400
        try:
            user_id = ObjectId(user_id_str)
            dial_obj_id = ObjectId(dialog_id)
        except InvalidId:
            return jsonify({"message": "Invalid user or dialog ID format."}), 400

        # Check if session exists, belongs to user, and is not already ended
        db = mongo.db
        existing_doc = db.dialog_activity.find_one({"_id": dial_obj_id, "uid": user_id}, {"sessionEndedAt": 1})
        if not existing_doc:
            return jsonify({"message": "Dialog session not found or access denied."}), 404
        if existing_doc.get("sessionEndedAt") is not None:
            # 409 Conflict - the session is already in the 'ended' state
            return jsonify({"message": "Dialog session has already been ended."}), 409

        # Update the document to set the end time
        now = datetime.datetime.now(datetime.timezone.utc)
        result = db.dialog_activity.update_one(
            {"_id": dial_obj_id, "uid": user_id, "sessionEndedAt": None}, # Ensure it's not already ended atomically
            {"$set": {"sessionEndedAt": now}}
        )

        # Check if the update was successful
        if result.modified_count == 1:
            return jsonify({"message": "Dialog session marked as ended."}), 200
        elif result.matched_count == 1 and result.modified_count == 0:
             # This could happen if the session was ended between find_one and update_one (race condition)
             logger.warning(f"Dialog {dialog_id} was already ended before update (race condition?).")
             return jsonify({"message": "Dialog session was already ended."}), 409
        else: # matched_count == 0 (shouldn't happen if find_one worked unless deleted concurrently)
             logger.warning(f"Dialog {dialog_id} matched 0 for ending update.")
             return jsonify({"message": "Dialog session not found or already ended."}), 404


    except KeyError: # Should be caught by initial user_id check
        logger.error(f"User ID (_id) not found in token payload for end_dialog_session.")
        return jsonify({"message": "Authentication token is invalid or missing user ID."}), 401
    except AttributeError:
         logger.error("PyMongo extension not initialized or attached correctly.")
         return jsonify({"message": "Database configuration error."}), 500
    except Exception as e:
        logger.error(f"Error ending dialog {dialog_id} for user {current_user.get('_id', 'UNKNOWN')}: {e}", exc_info=True)
        return jsonify({"message": "Internal server error ending dialog session."}), 500


@bp.route('/<string:dialog_id>', methods=['DELETE']) # Path relative to prefix
@token_required
def delete_dialog_session(current_user, dialog_id):
    """
    Deletes an entire dialog session document. Verifies ownership.
    (No schema needed for input/output here)
    """
    logger = _get_logger()
    if not mongo: return jsonify({"message": "Database connection not available."}), 500

    try:
        # Validate IDs
        user_id_str = str(current_user.get("_id"))
        if not user_id_str: return jsonify({"message": "Missing user ID in token."}), 400
        try:
            user_id = ObjectId(user_id_str)
            dial_obj_id = ObjectId(dialog_id)
        except InvalidId:
            return jsonify({"message": "Invalid user or dialog ID format."}), 400

        # Perform deletion, ensuring the user owns the dialog
        db = mongo.db
        result = db.dialog_activity.delete_one({"_id": dial_obj_id, "uid": user_id})

        # Check if a document was deleted
        if result.deleted_count == 1:
            return jsonify({"message": "Dialog session deleted successfully."}), 200 # 200 OK or 204 No Content
        else:
            # If deleted_count is 0, the document either didn't exist or didn't belong to the user
            return jsonify({"message": "Dialog session not found or access denied."}), 404

    except KeyError: # Should be caught by initial user_id check
        logger.error(f"User ID (_id) not found in token payload for delete_dialog_session.")
        return jsonify({"message": "Authentication token is invalid or missing user ID."}), 401
    except AttributeError:
         logger.error("PyMongo extension not initialized or attached correctly.")
         return jsonify({"message": "Database configuration error."}), 500
    except Exception as e:
        logger.error(f"Error deleting dialog {dialog_id} for user {current_user.get('_id', 'UNKNOWN')}: {e}", exc_info=True)
        return jsonify({"message": "Internal server error deleting dialog session."}), 500