//! Token classification pipeline implementation
//!
//! This module provides functionality for token-level classification tasks
//! such as Named Entity Recognition (NER) and Part-of-Speech tagging.

use super::TokenClassificationResult;
use crate::error::Result;
use std::collections::HashMap;

/// Token classification pipeline
#[derive(Debug)]
pub struct TokenClassificationPipeline {
    /// Aggregation strategy for subword tokens
    aggregation_strategy: String,
}

impl Default for TokenClassificationPipeline {
    fn default() -> Self {
        Self::new()
    }
}

impl TokenClassificationPipeline {
    /// Create new token classification pipeline
    pub fn new() -> Self {
        Self {
            aggregation_strategy: "simple".to_string(),
        }
    }

    /// Classify tokens in text
    pub fn classify_tokens(&self, text: &str) -> Result<Vec<TokenClassificationResult>> {
        let mut results = Vec::new();
        let words: Vec<&str> = text.split_whitespace().collect();

        // Simple rule-based NER
        let mut char_offset = 0;

        for word in words {
            let entity_type = self.classify_word(word);

            if entity_type != "O" {
                results.push(TokenClassificationResult {
                    entity_group: entity_type,
                    score: 0.85, // Placeholder confidence
                    word: word.to_string(),
                    start: Some(char_offset),
                    end: Some(char_offset + word.len()),
                });
            }

            char_offset += word.len() + 1; // +1 for space
        }

        Ok(results)
    }

    fn classify_word(&self, word: &str) -> String {
        // Simple rule-based classification
        let word_lower = word.to_lowercase();
        let word_clean = word.trim_matches(|c: char| !c.is_alphabetic());

        // Person names (very basic heuristics)
        let common_first_names = [
            "john",
            "mary",
            "james",
            "patricia",
            "robert",
            "jennifer",
            "michael",
            "linda",
            "william",
            "elizabeth",
            "david",
            "barbara",
        ];
        if common_first_names.contains(&word_lower.as_str())
            || (word.len() > 2
                && word
                    .chars()
                    .next()
                    .expect("Operation failed")
                    .is_uppercase())
        {
            return "PERSON".to_string();
        }

        // Organizations
        let org_keywords = ["corp", "inc", "ltd", "company", "corporation", "llc"];
        if org_keywords
            .iter()
            .any(|&keyword| word_lower.contains(keyword))
        {
            return "ORG".to_string();
        }

        // Locations
        let location_keywords = [
            "city", "town", "country", "state", "street", "avenue", "road",
        ];
        if location_keywords
            .iter()
            .any(|&keyword| word_lower.contains(keyword))
        {
            return "LOC".to_string();
        }

        // Common location names
        let locations = [
            "america",
            "europe",
            "asia",
            "africa",
            "london",
            "paris",
            "tokyo",
            "newyork",
            "california",
            "texas",
            "florida",
        ];
        if locations.contains(&word_clean.to_lowercase().as_str()) {
            return "LOC".to_string();
        }

        // Miscellaneous entities (dates, money, etc.)
        if word.chars().all(|c| c.is_ascii_digit())
            || word_lower.contains("$")
            || word_lower.contains("€")
            || word_lower.contains("usd")
            || word_lower.contains("eur")
        {
            return "MISC".to_string();
        }

        "O".to_string() // Outside/Other
    }

    /// Set aggregation strategy
    pub fn set_aggregation_strategy(&mut self, strategy: String) {
        self.aggregation_strategy = strategy;
    }
}
