akana-cli 0.5.0

use akana_core::*;
use clap::{Parser, Subcommand};
use std::path::PathBuf;

#[derive(Parser)]
#[command(name = "akana")]
#[command(about = "Akana: Modern and blazingly fast Turkish NLP CLI", long_about = None)]
struct Cli {
    #[command(subcommand)]
    command: Commands,
}

#[derive(Subcommand)]
enum Commands {
    /// Tokenize Turkish text into words
    Tokenize {
        /// Text to tokenize
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Perform morphological analysis on a word (Zemberek-compatible format)
    Analyze {
        /// Word to analyze
        word: String,
    },
    /// Perform Google-style syntactic morphological analysis with Inflectional Groups (FSMNLP 2019)
    SyntacticAnalyze {
        /// Word to analyze
        word: String,
    },
    /// Generate a surface word from lemma and tags
    Generate {
        /// Base lemma
        lemma: String,
        /// Morpheme tags (e.g. A3pl, P1sg, Dat)
        #[arg(short, long, value_delimiter = ',')]
        tags: Vec<String>,
    },
    /// De-asciify Turkish text
    Deasciify {
        /// Text to restore diacritics
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Asciify Turkish text
    Asciify {
        /// Text to convert to ASCII
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Normalize informal text
    Normalize {
        /// Text to normalize
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Check spelling and suggest corrections
    Spellcheck {
        /// Word to check
        word: String,
    },
    /// Parse sentence dependency tree
    Parse {
        /// Sentence to parse
        sentence: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Calculate modern and classical Turkish readability metrics
    Readability {
        /// Text to analyze
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Syllabify and hyphenate a Turkish word
    Syllabify {
        /// Word to syllabify
        word: String,
    },
    /// Convert number to Turkish written words
    Number {
        /// Number to convert
        num: i64,
    },
    /// Extract Named Entities (PER, LOC, ORG, DATE, MONEY, PERCENT)
    Ner {
        /// Text to analyze
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Extract top keywords from Turkish text
    Keywords {
        /// Text to extract keywords from
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
        /// Top K keywords
        #[arg(short, long, default_value_t = 10)]
        top_k: usize,
    },
    /// Extractive text summarization
    Summarize {
        /// Text to summarize
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
        /// Maximum number of sentences
        #[arg(short, long, default_value_t = 3)]
        sentences: usize,
    },
    /// Find morphological root / stem of a word
    Stem {
        /// Word to stem
        word: String,
    },
    /// Audit text for AI writing signatures, clichés, and punctuation anomalies
    AiAudit {
        /// Text to audit
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Generate an actionable LLM rewrite prompt to humanize Turkish text
    HumanizePrompt {
        /// Text to humanize
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
        /// Target register (e.g. blog, akademik, haber, edebi, hukuki)
        #[arg(short, long, default_value = "blog")]
        register: String,
    },
    /// Generate 256-dimensional Turkish sentence embedding vector
    Embed {
        /// Text to embed
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
    },
    /// Calculate semantic cosine similarity between two Turkish texts
    Similarity {
        /// First text
        text_a: String,
        /// Second text
        text_b: String,
    },
    /// Split Turkish text into chunks (semantic, sentence, or sdpm)
    Chunk {
        /// Text to chunk
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
        /// Chunking strategy: semantic, sentence, sdpm
        #[arg(short, long, default_value = "semantic")]
        strategy: String,
        /// Maximum tokens per chunk
        #[arg(short = 'c', long, default_value_t = 512)]
        chunk_size: usize,
        /// Overlap tokens between chunks (for sentence strategy)
        #[arg(short, long, default_value_t = 0)]
        overlap: usize,
        /// Minimum tokens before allowing a semantic split
        #[arg(short = 'k', long, default_value_t = 20)]
        min_chunk_size: usize,
        /// Similarity threshold (0.0 to 1.0) or cutoff value
        #[arg(short, long)]
        threshold: Option<f32>,
        /// Threshold calculation mode: similarity, percentile, stdev, iqr, auto
        #[arg(short = 'm', long, default_value = "percentile")]
        mode: String,
        /// Output formatted JSON
        #[arg(short, long)]
        json: bool,
    },
    /// Mask Turkish PII entities in text (KVKK aligned)
    PiiMask {
        /// Text to mask
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
        /// Masking mode: placeholder, surrogate, tag, anonymize
        #[arg(short, long, default_value = "placeholder")]
        mode: String,
        /// Output formatted JSON including entities and mapping
        #[arg(short, long)]
        json: bool,
    },
    /// Restore masked Turkish PII text using a JSON mapping dictionary
    PiiRestore {
        /// Masked text
        text: Option<String>,
        /// Read text from file
        #[arg(short, long)]
        file: Option<PathBuf>,
        /// Path to JSON mapping file
        #[arg(short, long)]
        mapping: PathBuf,
    },
}

fn resolve_input_text(text: Option<String>, file: Option<PathBuf>) -> Result<String, String> {
    if let Some(f) = file {
        std::fs::read_to_string(&f)
            .map_err(|e| format!("Failed to read file '{}': {}", f.display(), e))
    } else if let Some(t) = text {
        Ok(t)
    } else {
        Err("Please provide input text as argument or use -f/--file <PATH>".to_string())
    }
}

fn main() {
    let cli = Cli::parse();

    match cli.command {
        Commands::Tokenize { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let tokens = tokenization::TurkishTokenizer::tokenize_words(&input);
            println!("{}", serde_json::to_string_pretty(&tokens).unwrap());
        }
        Commands::Analyze { word } => {
            let morph = morphology::TurkishMorphology::new();
            let parses = morph.analyze(&word);
            println!("{}", serde_json::to_string_pretty(&parses).unwrap());
        }
        Commands::SyntacticAnalyze { word } => {
            let morph = syntactic_morphology::TurkishSyntacticMorphology::new();
            let parses = morph.analyze(&word);
            println!("{}", serde_json::to_string_pretty(&parses).unwrap());
        }
        Commands::Generate { lemma, tags } => {
            let gen = morphology::TurkishGenerator::new();
            let tag_refs: Vec<&str> = tags.iter().map(|s| s.as_str()).collect();
            if let Some(surface) = gen.generate(&lemma, &tag_refs) {
                println!("Generated surface: {surface}");
            } else {
                println!("Failed to generate surface form.");
            }
        }
        Commands::Deasciify { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let res = normalization::TurkishDeasciifier::deasciify(&input);
            println!("{res}");
        }
        Commands::Asciify { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let res = normalization::TurkishAsciifier::asciify(&input);
            println!("{res}");
        }
        Commands::Normalize { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let res = normalization::TurkishInformalNormalizer::normalize_text(&input);
            println!("{res}");
        }
        Commands::Spellcheck { word } => {
            let checker = normalization::TurkishSpellChecker::new();
            let suggestions = checker.suggest(&word, 2, 5);
            println!("{}", serde_json::to_string_pretty(&suggestions).unwrap());
        }
        Commands::Parse { sentence, file } => {
            let input = match resolve_input_text(sentence, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let tokens = tokenization::TurkishTokenizer::tokenize_words(&input);
            let parser = parser::TurkishDependencyParser::new();
            let tree = parser.parse(&tokens);
            println!("{}", tree.to_conllu());
        }
        Commands::Readability { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let report = readability::analyze_readability(&input);
            println!("{}", serde_json::to_string_pretty(&report).unwrap());
        }
        Commands::Syllabify { word } => {
            let syllables = phonology::TurkishSyllabifier::syllabify(&word);
            let hyphenated = phonology::TurkishSyllabifier::hyphenate(&word, "-");
            println!(
                "Syllables: {:?} (Count: {}, Hyphenated: {})",
                syllables,
                syllables.len(),
                hyphenated
            );
        }
        Commands::Number { num } => {
            let words = normalization::TurkishNumberConverter::number_to_words(num);
            let ordinal = normalization::TurkishNumberConverter::ordinal_to_words(num);
            println!("Cardinal: {words}\nOrdinal:  {ordinal}");
        }
        Commands::Ner { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let entities = ner::TurkishNER::extract_entities(&input);
            println!("{}", serde_json::to_string_pretty(&entities).unwrap());
        }
        Commands::Keywords { text, file, top_k } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let extractor = analysis::TurkishKeywordExtractor::new();
            let kw = extractor.extract_keywords(&input, top_k);
            println!("{}", serde_json::to_string_pretty(&kw).unwrap());
        }
        Commands::Summarize {
            text,
            file,
            sentences,
        } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let summarizer = analysis::TurkishSummarizer::new();
            let summary = summarizer.summarize(&input, sentences);
            for (idx, sent) in summary.iter().enumerate() {
                println!("{}. {}", idx + 1, sent);
            }
        }
        Commands::Stem { word } => {
            let stemmer = morphology::TurkishStemmer::new();
            println!("Stem: {}", stemmer.stem(&word));
        }
        Commands::AiAudit { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let auditor = style::TurkishStyleAuditor::new();
            let report = auditor.audit(&input);
            println!("{}", serde_json::to_string_pretty(&report).unwrap());
        }
        Commands::HumanizePrompt {
            text,
            file,
            register,
        } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let prompt = style::TurkishHumanizer::generate_prompt(&input, &register);
            println!("{prompt}");
        }
        Commands::Embed { text, file } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let embeddings = embeddings::TurkishEmbeddings::new();
            let vec = embeddings.embed(&input);
            println!("{}", serde_json::to_string(&vec).unwrap());
        }
        Commands::Similarity { text_a, text_b } => {
            let embeddings = embeddings::TurkishEmbeddings::new();
            let score = embeddings.similarity(&text_a, &text_b);
            println!("{score:.4}");
        }
        Commands::Chunk {
            text,
            file,
            strategy,
            chunk_size,
            overlap,
            min_chunk_size,
            threshold,
            mode,
            json,
        } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };

            let thresh_mode = match mode.to_lowercase().as_str() {
                "similarity" | "sim" => {
                    chunking::ThresholdMode::Similarity(threshold.unwrap_or(0.32))
                }
                "percentile" | "perc" => {
                    chunking::ThresholdMode::Percentile(threshold.unwrap_or(0.75))
                }
                "stdev" | "std" => {
                    chunking::ThresholdMode::StandardDeviation(threshold.unwrap_or(0.80))
                }
                "iqr" => chunking::ThresholdMode::Interquartile(threshold.unwrap_or(1.0)),
                "auto" => chunking::ThresholdMode::Auto,
                _ => chunking::ThresholdMode::Percentile(threshold.unwrap_or(0.75)),
            };

            let chunks = match strategy.to_lowercase().as_str() {
                "sentence" => {
                    let chunker = chunking::SentenceChunker::new(chunk_size, overlap, 1);
                    chunker.chunk(&input)
                }
                "sdpm" => {
                    let chunker = chunking::SDPMChunker::new(
                        chunk_size,
                        thresh_mode,
                        threshold.unwrap_or(0.32),
                    )
                    .with_min_chunk_size(min_chunk_size);
                    chunker.chunk(&input)
                }
                _ => {
                    let chunker = chunking::SemanticChunker::new(chunk_size, thresh_mode)
                        .with_min_chunk_size(min_chunk_size);
                    chunker.chunk(&input)
                }
            };

            if json {
                println!("{}", serde_json::to_string_pretty(&chunks).unwrap());
            } else {
                println!("Generated {} chunk(s):\n", chunks.len());
                for (i, chunk) in chunks.iter().enumerate() {
                    println!(
                        "--- Chunk #{} (tokens: {}, chars: {}-{}) ---",
                        i + 1,
                        chunk.token_count,
                        chunk.start_index,
                        chunk.end_index
                    );
                    println!("{}\n", chunk.text);
                }
            }
        }
        Commands::PiiMask {
            text,
            file,
            mode,
            json,
        } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let engine = pii::TurkishPiiEngine::new();
            let pii_mode = match mode.to_lowercase().as_str() {
                "surrogate" => pii::PiiMode::SyntheticSurrogate,
                "tag" => pii::PiiMode::Tag,
                "anonymize" => pii::PiiMode::Anonymize,
                _ => pii::PiiMode::Placeholder,
            };
            let result = engine.mask(&input, pii_mode);
            if json {
                println!("{}", serde_json::to_string_pretty(&result).unwrap());
            } else {
                println!("{}", result.masked_text);
            }
        }
        Commands::PiiRestore {
            text,
            file,
            mapping,
        } => {
            let input = match resolve_input_text(text, file) {
                Ok(t) => t,
                Err(e) => {
                    eprintln!("Error: {e}");
                    std::process::exit(1);
                }
            };
            let map_str = match std::fs::read_to_string(&mapping) {
                Ok(s) => s,
                Err(e) => {
                    eprintln!("Failed to read mapping file: {e}");
                    std::process::exit(1);
                }
            };
            let map: std::collections::HashMap<String, String> =
                match serde_json::from_str(&map_str) {
                    Ok(m) => m,
                    Err(e) => {
                        eprintln!("Failed to parse mapping JSON: {e}");
                        std::process::exit(1);
                    }
                };
            let engine = pii::TurkishPiiEngine::new();
            let restored = engine.restore_with_mapping(&input, &map);
            println!("{}", restored);
        }
    }
}