from flask import Flask, jsonify, render_template, request

# Disable ChromaDB anonymized telemetry for local development so the
# library doesn't attempt to call external PostHog telemetry endpoints.
# This avoids noisy errors in server logs and respects developer privacy.
try:
    import chromadb

    # Turn off anonymized telemetry (the chromadb package defaults this to True)
    chromadb.configure(anonymized_telemetry=False)
except Exception:
    # If chromadb isn't installed in this environment yet, ignore silently.
    pass

app = Flask(__name__)


@app.route("/")
def index():
    """
    Renders the main page.
    """
    return render_template("index.html")


@app.route("/health")
def health():
    """
    Health check endpoint.
    """
    return jsonify({"status": "ok"}), 200


@app.route("/ingest", methods=["POST"])
def ingest():
    """Endpoint to trigger document ingestion with embeddings"""
    try:
        from src.config import (
            CORPUS_DIRECTORY,
            DEFAULT_CHUNK_SIZE,
            DEFAULT_OVERLAP,
            RANDOM_SEED,
        )
        from src.ingestion.ingestion_pipeline import IngestionPipeline

        # Get optional parameters from request
        data = request.get_json() if request.is_json else {}
        store_embeddings = data.get("store_embeddings", True)

        pipeline = IngestionPipeline(
            chunk_size=DEFAULT_CHUNK_SIZE,
            overlap=DEFAULT_OVERLAP,
            seed=RANDOM_SEED,
            store_embeddings=store_embeddings,
        )

        result = pipeline.process_directory_with_embeddings(CORPUS_DIRECTORY)

        # Create response with enhanced information
        response = {
            "status": result["status"],
            "chunks_processed": result["chunks_processed"],
            "files_processed": result["files_processed"],
            "embeddings_stored": result["embeddings_stored"],
            "store_embeddings": result["store_embeddings"],
            "message": (
                f"Successfully processed {result['chunks_processed']} chunks "
                f"from {result['files_processed']} files"
            ),
        }

        # Include failed files info if any
        if result["failed_files"]:
            response["failed_files"] = result["failed_files"]
            failed_count = len(result["failed_files"])
            response["warnings"] = f"{failed_count} files failed to process"

        return jsonify(response)

    except Exception as e:
        return jsonify({"status": "error", "message": str(e)}), 500


@app.route("/search", methods=["POST"])
def search():
    """
    Endpoint to perform semantic search on ingested documents.

    Accepts JSON requests with query text and optional parameters.
    Returns semantically similar document chunks.
    """
    try:
        # Validate request contains JSON data
        if not request.is_json:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": "Content-Type must be application/json",
                    }
                ),
                400,
            )

        data = request.get_json()

        # Validate required query parameter
        query = data.get("query")
        if query is None:
            return (
                jsonify({"status": "error", "message": "Query parameter is required"}),
                400,
            )

        if not isinstance(query, str) or not query.strip():
            return (
                jsonify(
                    {"status": "error", "message": "Query must be a non-empty string"}
                ),
                400,
            )

        # Extract optional parameters with defaults
        top_k = data.get("top_k", 5)
        threshold = data.get("threshold", 0.3)

        # Validate parameters
        if not isinstance(top_k, int) or top_k <= 0:
            return (
                jsonify(
                    {"status": "error", "message": "top_k must be a positive integer"}
                ),
                400,
            )

        if not isinstance(threshold, (int, float)) or not (0.0 <= threshold <= 1.0):
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": "threshold must be a number between 0 and 1",
                    }
                ),
                400,
            )

        # Initialize search components
        from src.config import COLLECTION_NAME, VECTOR_DB_PERSIST_PATH
        from src.embedding.embedding_service import EmbeddingService
        from src.search.search_service import SearchService
        from src.vector_store.vector_db import VectorDatabase

        vector_db = VectorDatabase(VECTOR_DB_PERSIST_PATH, COLLECTION_NAME)
        embedding_service = EmbeddingService()
        search_service = SearchService(vector_db, embedding_service)

        # Perform search
        results = search_service.search(
            query=query.strip(), top_k=top_k, threshold=threshold
        )

        # Format response
        response = {
            "status": "success",
            "query": query.strip(),
            "results_count": len(results),
            "results": results,
        }

        return jsonify(response)

    except ValueError as e:
        return jsonify({"status": "error", "message": str(e)}), 400

    except Exception as e:
        return jsonify({"status": "error", "message": f"Search failed: {str(e)}"}), 500


@app.route("/chat", methods=["POST"])
def chat():
    """
    Endpoint for conversational RAG interactions.

    Accepts JSON requests with user messages and returns AI-generated
    responses based on corporate policy documents.
    """
    try:
        # Validate request contains JSON data
        if not request.is_json:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": "Content-Type must be application/json",
                    }
                ),
                400,
            )

        data = request.get_json()

        # Validate required message parameter
        message = data.get("message")
        if message is None:
            return (
                jsonify(
                    {"status": "error", "message": "message parameter is required"}
                ),
                400,
            )

        if not isinstance(message, str) or not message.strip():
            return (
                jsonify(
                    {"status": "error", "message": "message must be a non-empty string"}
                ),
                400,
            )

        # Extract optional parameters
        conversation_id = data.get("conversation_id")
        include_sources = data.get("include_sources", True)
        include_debug = data.get("include_debug", False)

        # Initialize RAG pipeline components
        try:
            from src.config import COLLECTION_NAME, VECTOR_DB_PERSIST_PATH
            from src.embedding.embedding_service import EmbeddingService
            from src.llm.llm_service import LLMService
            from src.rag.rag_pipeline import RAGPipeline
            from src.rag.response_formatter import ResponseFormatter
            from src.search.search_service import SearchService
            from src.vector_store.vector_db import VectorDatabase

            # Initialize services
            vector_db = VectorDatabase(VECTOR_DB_PERSIST_PATH, COLLECTION_NAME)
            embedding_service = EmbeddingService()
            search_service = SearchService(vector_db, embedding_service)

            # Initialize LLM service from environment
            llm_service = LLMService.from_environment()

            # Initialize RAG pipeline
            rag_pipeline = RAGPipeline(search_service, llm_service)

            # Initialize response formatter
            formatter = ResponseFormatter()

        except ValueError as e:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": f"LLM service configuration error: {str(e)}",
                        "details": (
                            "Please ensure OPENROUTER_API_KEY or GROQ_API_KEY "
                            "environment variables are set"
                        ),
                    }
                ),
                503,
            )
        except Exception as e:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": f"Service initialization failed: {str(e)}",
                    }
                ),
                500,
            )

        # Generate RAG response
        rag_response = rag_pipeline.generate_answer(message.strip())

        # Format response for API
        if include_sources:
            formatted_response = formatter.format_api_response(
                rag_response, include_debug
            )
        else:
            formatted_response = formatter.format_chat_response(
                rag_response, conversation_id, include_sources=False
            )

        return jsonify(formatted_response)

    except Exception as e:
        return (
            jsonify({"status": "error", "message": f"Chat request failed: {str(e)}"}),
            500,
        )


@app.route("/chat/health", methods=["GET"])
def chat_health():
    """
    Health check endpoint for RAG chat functionality.

    Returns the status of all RAG pipeline components.
    """
    try:
        from src.config import COLLECTION_NAME, VECTOR_DB_PERSIST_PATH
        from src.embedding.embedding_service import EmbeddingService
        from src.llm.llm_service import LLMService
        from src.rag.rag_pipeline import RAGPipeline
        from src.rag.response_formatter import ResponseFormatter
        from src.search.search_service import SearchService
        from src.vector_store.vector_db import VectorDatabase

        # Initialize services for health check
        vector_db = VectorDatabase(VECTOR_DB_PERSIST_PATH, COLLECTION_NAME)
        embedding_service = EmbeddingService()
        search_service = SearchService(vector_db, embedding_service)

        try:
            llm_service = LLMService.from_environment()
            rag_pipeline = RAGPipeline(search_service, llm_service)
            formatter = ResponseFormatter()

            # Perform health check
            health_data = rag_pipeline.health_check()
            health_response = formatter.create_health_response(health_data)

            # Determine HTTP status based on health
            if health_data.get("pipeline") == "healthy":
                return jsonify(health_response), 200
            elif health_data.get("pipeline") == "degraded":
                return jsonify(health_response), 200  # Still functional
            else:
                return jsonify(health_response), 503  # Service unavailable

        except ValueError as e:
            return (
                jsonify(
                    {
                        "status": "error",
                        "message": f"LLM configuration error: {str(e)}",
                        "health": {
                            "pipeline_status": "unhealthy",
                            "components": {
                                "llm_service": {
                                    "status": "unconfigured",
                                    "error": str(e),
                                }
                            },
                        },
                    }
                ),
                503,
            )

    except Exception as e:
        return (
            jsonify({"status": "error", "message": f"Health check failed: {str(e)}"}),
            500,
        )


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