import logging
import time
import traceback
import asyncio
from sklearn.feature_extraction.text import TfidfVectorizer
from typing import Optional, List, Dict, Any, Tuple, Union
import pandas as pd
from pathlib import Path

from classifiers import TFIDFClassifier, LLMClassifier
from utils import load_data, validate_results
from client import get_client


def update_api_key(api_key: str) -> Tuple[bool, str]:
    """Update the OpenAI API key"""
    from client import initialize_client
    return initialize_client(api_key)


async def process_file_async(
    file: Union[str, Path],
    text_columns: List[str],
    categories: Optional[str],
    classifier_type: str,
    show_explanations: bool
) -> Tuple[Optional[pd.DataFrame], Optional[str]]:
    """Async version of process_file"""
    # Initialize result_df and validation_report
    result_df: Optional[pd.DataFrame] = None
    validation_report: Optional[str] = None

    try:
        # Load data from file
        if isinstance(file, str):
            df: pd.DataFrame = load_data(file)
        else:
            df: pd.DataFrame = load_data(file.name)

        if not text_columns:
            return None, "Please select at least one text column"

        # Check if all selected columns exist
        missing_columns: List[str] = [col for col in text_columns if col not in df.columns]
        if missing_columns:
            return (
                None,
                f"Columns not found in the file: {', '.join(missing_columns)}. Available columns: {', '.join(df.columns)}",
            )

        # Combine text from selected columns
        texts: List[str] = []
        for _, row in df.iterrows():
            combined_text: str = " ".join(str(row[col]) for col in text_columns)
            texts.append(combined_text)

        # Parse categories if provided
        category_list: List[str] = []
        if categories:
            category_list = [cat.strip() for cat in categories.split(",")]

        # Select classifier based on data size and user choice
        num_texts: int = len(texts)

        # If no specific model is chosen, select the most appropriate one
        if classifier_type == "auto":
            if num_texts <= 500:
                classifier_type = "gpt4"
            elif num_texts <= 1000:
                classifier_type = "gpt35"
            elif num_texts <= 5000:
                classifier_type = "hybrid"
            else:
                classifier_type = "tfidf"

        # Get the client instance
        client = get_client()

        # Initialize appropriate classifier
        if classifier_type == "tfidf":
            classifier: TFIDFClassifier = TFIDFClassifier()
            results: List[Dict[str, Any]] = classifier.classify(texts, category_list)
        elif classifier_type in ["gpt35", "gpt4"]:
            if client is None:
                return (
                    None,
                    "Erreur : Le client API n'est pas initialisé. Veuillez configurer une clé API valide dans l'onglet 'Setup'.",
                )
            model: str = "gpt-3.5-turbo" if classifier_type == "gpt35" else "gpt-4"
            classifier: LLMClassifier = LLMClassifier(client=client, model=model)
            results: List[Dict[str, Any]] = await classifier.classify_async(texts, category_list)
        else:  # hybrid
            if client is None:
                return (
                    None,
                    "Erreur : Le client API n'est pas initialisé. Veuillez configurer une clé API valide dans l'onglet 'Setup'.",
                )
            # First pass with TF-IDF
            tfidf_classifier: TFIDFClassifier = TFIDFClassifier()
            tfidf_results: List[Dict[str, Any]] = tfidf_classifier.classify(texts, category_list)

            # Second pass with LLM for low confidence results
            llm_classifier: LLMClassifier = LLMClassifier(client=client, model="gpt-3.5-turbo")
            results: List[Optional[Dict[str, Any]]] = []
            low_confidence_texts: List[str] = []
            low_confidence_indices: List[int] = []

            for i, (text, tfidf_result) in enumerate(zip(texts, tfidf_results)):
                if tfidf_result["confidence"] < 70:  # If confidence is below 70%
                    low_confidence_texts.append(text)
                    low_confidence_indices.append(i)
                    results.append(None)  # Placeholder
                else:
                    results.append(tfidf_result)

            if low_confidence_texts:
                llm_results: List[Dict[str, Any]] = await llm_classifier.classify_async(
                    low_confidence_texts, category_list
                )
                for idx, llm_result in zip(low_confidence_indices, llm_results):
                    results[idx] = llm_result

        # Create results dataframe
        result_df = df.copy()
        result_df["Category"] = [r["category"] for r in results]
        result_df["Confidence"] = [r["confidence"] for r in results]

        if show_explanations:
            result_df["Explanation"] = [r["explanation"] for r in results]

        # Validate results using LLM
        validation_report = validate_results(result_df, text_columns, client)

        return result_df, validation_report

    except Exception as e:
        error_traceback: str = traceback.format_exc()
        return None, f"Error: {str(e)}\n{error_traceback}"


def process_file(
    file: Union[str, Path],
    text_columns: List[str],
    categories: Optional[str],
    classifier_type: str,
    show_explanations: bool
) -> Tuple[Optional[pd.DataFrame], Optional[str]]:
    """Synchronous wrapper for process_file_async"""
    return asyncio.run(process_file_async(file, text_columns, categories, classifier_type, show_explanations))


def export_results(df: pd.DataFrame, format_type: str) -> Optional[str]:
    """Export results to a file and return the file path for download"""
    if df is None:
        return None

    # Create a temporary file
    import tempfile
    import os

    # Create a temporary directory if it doesn't exist
    temp_dir: str = "temp_exports"
    os.makedirs(temp_dir, exist_ok=True)

    # Generate a unique filename
    timestamp: str = time.strftime("%Y%m%d-%H%M%S")
    filename: str = f"classification_results_{timestamp}"

    if format_type == "excel":
        file_path: str = os.path.join(temp_dir, f"{filename}.xlsx")
        df.to_excel(file_path, index=False)
    else:
        file_path: str = os.path.join(temp_dir, f"{filename}.csv")
        df.to_csv(file_path, index=False)

    return file_path