trustformers 0.1.1

//! Interactive CLI Demo
#![allow(clippy::all)]
#![allow(unused_variables)]
//!
//! This example provides an interactive command-line interface for exploring
//! TrustformeRS capabilities in real-time. Users can try different models,
//! tasks, and configurations interactively.

use clap::{Parser, Subcommand};
use colored::*;
use std::io::{self, Write};
use trustformers::{pipeline::PipelineChain as PipelineConfig, Result};

#[derive(Parser)]
#[command(name = "trustformers-interactive")]
#[command(about = "Interactive TrustformeRS Demo CLI")]
struct Cli {
    #[command(subcommand)]
    command: Option<Commands>,

    #[arg(long, help = "Enable debug mode")]
    debug: bool,

    #[arg(long, help = "GPU device to use")]
    device: Option<String>,
}

#[derive(Subcommand)]
enum Commands {
    /// Interactive mode - explore different tasks
    Interactive,
    /// Quick demo of all capabilities
    Demo,
    /// Benchmark different models
    Benchmark,
    /// Compare models side-by-side
    Compare,
}

struct InteractiveCLI {
    current_task: String,
    current_model: String,
    #[allow(dead_code)]
    config: PipelineConfig,
}

impl InteractiveCLI {
    fn new() -> Self {
        Self {
            current_task: "text-classification".to_string(),
            current_model: "distilbert-base-uncased-finetuned-sst-2-english".to_string(),
            config: PipelineConfig::new(),
        }
    }

    async fn run(&mut self) -> Result<()> {
        self.print_welcome();

        loop {
            self.print_status();
            self.print_menu();

            let choice = self.get_input("Enter your choice")?;

            match choice.trim() {
                "1" => self.select_task().await?,
                "2" => self.select_model().await?,
                "3" => self.configure_pipeline().await?,
                "4" => self.run_inference().await?,
                "5" => self.batch_processing().await?,
                "6" => self.streaming_demo().await?,
                "7" => self.performance_test().await?,
                "8" => self.compare_models().await?,
                "9" => self.save_session().await?,
                "10" => self.load_session().await?,
                "h" | "help" => self.print_help(),
                "q" | "quit" | "exit" => {
                    println!("{}", "Goodbye! Thanks for trying TrustformeRS! 👋".green());
                    break;
                },
                _ => println!("{}", "Invalid choice. Type 'h' for help.".red()),
            }

            println!(); // Add spacing
        }

        Ok(())
    }

    fn print_welcome(&self) {
        println!(
            "{}",
            "🚀 Welcome to TrustformeRS Interactive Demo!".bold().cyan()
        );
        println!("{}", "=========================================".cyan());
        println!("Explore the power of Rust-based machine learning!");
        println!("Type 'h' for help or 'q' to quit at any time.\n");
    }

    fn print_status(&self) {
        println!("{}", "📊 Current Configuration:".bold().yellow());
        println!("  Task: {}", self.current_task.green());
        println!("  Model: {}", self.current_model.green());
        println!("  Device: {}", "auto".green());
        println!();
    }

    fn print_menu(&self) {
        println!("{}", "🎯 Available Actions:".bold().blue());
        println!("  1️⃣  Select Task");
        println!("  2️⃣  Select Model");
        println!("  3️⃣  Configure Pipeline");
        println!("  4️⃣  Run Inference");
        println!("  5️⃣  Batch Processing");
        println!("  6️⃣  Streaming Demo");
        println!("  7️⃣  Performance Test");
        println!("  8️⃣  Compare Models");
        println!("  9️⃣  Save Session");
        println!("  🔟 Load Session");
        println!("  ❓ {} - Show help", "h".cyan());
        println!("  🚪 {} - Exit program", "q".cyan());
        println!();
    }

    fn print_help(&self) {
        println!("{}", "🔧 TrustformeRS Interactive CLI Help".bold().cyan());
        println!("{}", "===================================".cyan());
        println!();
        println!("{}", "Available Tasks:".bold());
        println!("  • text-classification - Classify text sentiment, topics, etc.");
        println!("  • text-generation - Generate text continuations");
        println!("  • question-answering - Answer questions based on context");
        println!("  • summarization - Summarize long texts");
        println!("  • translation - Translate between languages");
        println!("  • fill-mask - Fill in masked tokens");
        println!();
        println!("{}", "Tips:".bold());
        println!("  • Start with text-classification for a quick demo");
        println!("  • Use batch processing for multiple inputs");
        println!("  • Try streaming for real-time processing");
        println!("  • Compare models to see performance differences");
        println!("  • Save sessions to preserve your configurations");
        println!();
        println!("{}", "Example Workflow:".bold());
        println!("  1. Select a task (e.g., text-classification)");
        println!("  2. Choose a model (or use the default)");
        println!("  3. Run inference with your text");
        println!("  4. Try batch processing with multiple inputs");
        println!("  5. Compare with other models");
        println!();
    }

    async fn select_task(&mut self) -> Result<()> {
        println!("{}", "📋 Available Tasks:".bold().yellow());
        let tasks = vec![
            (
                "text-classification",
                "Classify text sentiment, topics, etc.",
            ),
            ("text-generation", "Generate text continuations"),
            ("question-answering", "Answer questions based on context"),
            ("summarization", "Summarize long texts"),
            ("translation", "Translate between languages"),
            ("fill-mask", "Fill in masked tokens"),
        ];

        for (i, (task, description)) in tasks.iter().enumerate() {
            println!("  {}. {} - {}", i + 1, task.green(), description);
        }

        let choice = self.get_input("Select task number (1-6)")?;

        if let Ok(index) = choice.trim().parse::<usize>() {
            if index > 0 && index <= tasks.len() {
                self.current_task = tasks[index - 1].0.to_string();
                println!("✅ Selected task: {}", self.current_task.green());

                // Suggest appropriate models for the task
                self.suggest_models().await?;
            } else {
                println!("{}", "Invalid task number!".red());
            }
        } else {
            println!("{}", "Please enter a valid number!".red());
        }

        Ok(())
    }

    async fn suggest_models(&self) -> Result<()> {
        println!("{}", "💡 Suggested models for this task:".bold().blue());

        let models = match self.current_task.as_str() {
            "text-classification" => vec![
                "distilbert-base-uncased-finetuned-sst-2-english",
                "cardiffnlp/twitter-roberta-base-sentiment-latest",
                "microsoft/DialoGPT-medium",
            ],
            "text-generation" => vec!["gpt2", "gpt2-medium", "microsoft/DialoGPT-medium"],
            "question-answering" => vec![
                "distilbert-base-cased-distilled-squad",
                "deepset/roberta-base-squad2",
                "microsoft/DialoGPT-medium",
            ],
            "summarization" => vec![
                "facebook/bart-large-cnn",
                "t5-small",
                "microsoft/DialoGPT-medium",
            ],
            _ => vec!["distilbert-base-uncased"],
        };

        for (i, model) in models.iter().enumerate() {
            println!("  {}. {}", i + 1, model.cyan());
        }

        println!("\n💡 Tip: You can select one of these in the next step!");
        Ok(())
    }

    async fn select_model(&mut self) -> Result<()> {
        println!("{}", "🤖 Model Selection".bold().yellow());
        println!("Current model: {}", self.current_model.green());
        println!();

        let new_model = self.get_input("Enter model name (or press Enter to keep current)")?;

        if !new_model.trim().is_empty() {
            println!("🔄 Loading model: {}...", new_model.cyan());

            // Simulate model loading (in real implementation, you'd validate the model)
            self.current_model = new_model.trim().to_string();
            println!("✅ Model loaded successfully!");
        }

        Ok(())
    }

    async fn configure_pipeline(&mut self) -> Result<()> {
        println!("{}", "⚙️  Pipeline Configuration".bold().yellow());

        match self.current_task.as_str() {
            "text-generation" => self.configure_generation().await?,
            "text-classification" => self.configure_classification().await?,
            _ => {
                println!("Using default configuration for {}", self.current_task);
            },
        }

        Ok(())
    }

    async fn configure_generation(&mut self) -> Result<()> {
        println!("🎛️  Text Generation Settings:");

        let max_length = self.get_input("Max length (default: 50)")?;
        let temperature = self.get_input("Temperature (default: 1.0)")?;
        let top_k = self.get_input("Top K (default: 50)")?;
        let top_p = self.get_input("Top P (default: 1.0)")?;

        println!("✅ Generation configuration updated!");

        // In a real implementation, you would update the actual config
        println!("Settings applied:");
        println!("  Max length: {}", max_length.trim());
        println!("  Temperature: {}", temperature.trim());
        println!("  Top K: {}", top_k.trim());
        println!("  Top P: {}", top_p.trim());

        Ok(())
    }

    async fn configure_classification(&mut self) -> Result<()> {
        println!("🎛️  Text Classification Settings:");

        let return_all_scores = self.get_input("Return all scores? (y/n, default: n)")?;

        println!("✅ Classification configuration updated!");
        println!("Settings applied:");
        println!("  Return all scores: {}", return_all_scores.trim());

        Ok(())
    }

    async fn run_inference(&mut self) -> Result<()> {
        println!("{}", "🔍 Run Inference".bold().yellow());
        println!("Task: {}", self.current_task.green());
        println!("Model: {}", self.current_model.green());
        println!();

        match self.current_task.as_str() {
            "text-classification" => self.run_classification().await?,
            "text-generation" => self.run_generation().await?,
            "question-answering" => self.run_qa().await?,
            "summarization" => self.run_summarization().await?,
            _ => {
                println!("{}", "Task not yet implemented in demo mode".yellow());
                return Ok(());
            },
        }

        Ok(())
    }

    async fn run_classification(&mut self) -> Result<()> {
        let text = self.get_input("Enter text to classify")?;

        if text.trim().is_empty() {
            println!("{}", "Please enter some text!".red());
            return Ok(());
        }

        println!("🔄 Classifying text...");

        // Simulate classification (in real implementation, use actual pipeline)
        let mock_results = vec![("POSITIVE", 0.8945), ("NEGATIVE", 0.1055)];

        println!("{}", "📊 Results:".bold().green());
        for (label, score) in mock_results {
            let bar_length = (score * 20.0) as usize;
            let bar = "█".repeat(bar_length);
            println!("  {}: {:.3} {}", label.cyan(), score, bar.green());
        }

        Ok(())
    }

    async fn run_generation(&mut self) -> Result<()> {
        let prompt = self.get_input("Enter text prompt")?;

        if prompt.trim().is_empty() {
            println!("{}", "Please enter a prompt!".red());
            return Ok(());
        }

        println!("🔄 Generating text...");

        // Simulate text generation
        let mock_continuation = format!(
            "{} is an amazing technology that continues to revolutionize how we interact with computers. \
            The potential applications are limitless, from creative writing to technical documentation.",
            prompt.trim()
        );

        println!("{}", "📝 Generated Text:".bold().green());
        println!("{}", format!("\"{}\"", mock_continuation).italic());

        Ok(())
    }

    async fn run_qa(&mut self) -> Result<()> {
        let context = self.get_input("Enter context text")?;

        if context.trim().is_empty() {
            println!("{}", "Please enter context text!".red());
            return Ok(());
        }

        let question = self.get_input("Enter your question")?;

        if question.trim().is_empty() {
            println!("{}", "Please enter a question!".red());
            return Ok(());
        }

        println!("🔄 Finding answer...");

        // Simulate QA
        let mock_answer = "TrustformeRS";
        let mock_score = 0.95;

        println!("{}", "💡 Answer:".bold().green());
        println!("  Answer: {}", mock_answer.cyan());
        println!("  Confidence: {:.3}", mock_score);

        Ok(())
    }

    async fn run_summarization(&mut self) -> Result<()> {
        let text = self.get_input("Enter text to summarize")?;

        if text.trim().is_empty() {
            println!("{}", "Please enter text to summarize!".red());
            return Ok(());
        }

        println!("🔄 Generating summary...");

        // Simulate summarization
        let mock_summary = "This text discusses the key points and main ideas in a concise format.";

        println!("{}", "📄 Summary:".bold().green());
        println!("{}", format!("\"{}\"", mock_summary).italic());

        Ok(())
    }

    async fn batch_processing(&mut self) -> Result<()> {
        println!("{}", "📦 Batch Processing Demo".bold().yellow());
        println!("Enter multiple inputs (one per line). Type 'DONE' when finished:");

        let mut inputs = Vec::new();

        loop {
            let input = self.get_input(&format!("Input #{}", inputs.len() + 1))?;

            if input.trim().to_uppercase() == "DONE" {
                break;
            }

            if !input.trim().is_empty() {
                inputs.push(input.trim().to_string());
            }
        }

        if inputs.is_empty() {
            println!("{}", "No inputs provided!".yellow());
            return Ok(());
        }

        println!("🔄 Processing {} inputs...", inputs.len());

        // Simulate batch processing
        for (i, input) in inputs.iter().enumerate() {
            println!(
                "  {}. \"{}\" -> {}",
                i + 1,
                input,
                "POSITIVE (0.875)".green()
            );
        }

        println!("✅ Batch processing completed!");

        Ok(())
    }

    async fn streaming_demo(&mut self) -> Result<()> {
        println!("{}", "🌊 Streaming Processing Demo".bold().yellow());

        if self.current_task != "text-generation" {
            println!(
                "{}",
                "Streaming is best demonstrated with text generation.".yellow()
            );
            println!("Switching to text-generation for this demo...");
            self.current_task = "text-generation".to_string();
        }

        let prompt = self.get_input("Enter prompt for streaming generation")?;

        if prompt.trim().is_empty() {
            println!("{}", "Please enter a prompt!".red());
            return Ok(());
        }

        println!("🔄 Streaming text generation...");
        println!("Generated text will appear word by word:\n");

        // Simulate streaming generation
        let words = vec![
            "The",
            "future",
            "of",
            "artificial",
            "intelligence",
            "looks",
            "incredibly",
            "promising",
            "with",
            "advances",
            "in",
            "machine",
            "learning",
            "and",
            "natural",
            "language",
            "processing",
            "continuing",
            "to",
            "accelerate.",
        ];

        print!("{}: ", prompt.trim().cyan());
        let _ = io::stdout().flush(); // Ignore flush errors in streaming output

        for word in words {
            print!("{} ", word);
            let _ = io::stdout().flush(); // Ignore flush errors in streaming output
            std::thread::sleep(std::time::Duration::from_millis(200));
        }

        println!("\n\n✅ Streaming complete!");

        Ok(())
    }

    async fn performance_test(&mut self) -> Result<()> {
        println!("{}", "⚡ Performance Test".bold().yellow());

        let iterations = self.get_input("Number of iterations (default: 10)")?;
        let iterations: usize = iterations.trim().parse().unwrap_or(10);

        println!("🔄 Running {} iterations...", iterations);

        let start = std::time::Instant::now();

        // Simulate performance test
        for i in 1..=iterations {
            print!(
                "\r  Progress: {}/{} ({:.1}%)",
                i,
                iterations,
                (i as f64 / iterations as f64) * 100.0
            );
            let _ = io::stdout().flush(); // Ignore flush errors in progress display
            std::thread::sleep(std::time::Duration::from_millis(50));
        }

        let duration = start.elapsed();

        println!("\n\n📊 Performance Results:");
        println!("  Total time: {:.3}s", duration.as_secs_f64());
        println!(
            "  Average per iteration: {:.3}ms",
            duration.as_millis() as f64 / iterations as f64
        );
        println!(
            "  Throughput: {:.1} iterations/sec",
            iterations as f64 / duration.as_secs_f64()
        );

        Ok(())
    }

    async fn compare_models(&mut self) -> Result<()> {
        println!("{}", "🔬 Model Comparison".bold().yellow());

        let model1 = self.get_input("Enter first model name")?;
        let model2 = self.get_input("Enter second model name")?;
        let test_input = self.get_input("Enter test input")?;

        if test_input.trim().is_empty() {
            println!("{}", "Please enter test input!".red());
            return Ok(());
        }

        println!("🔄 Comparing models...");

        // Simulate model comparison
        println!("{}", "📊 Comparison Results:".bold().green());
        println!();

        println!("Model 1: {}", model1.cyan());
        println!("  Result: POSITIVE (0.892)");
        println!("  Time: 45ms");
        println!("  Memory: 1.2GB");
        println!();

        println!("Model 2: {}", model2.cyan());
        println!("  Result: POSITIVE (0.876)");
        println!("  Time: 38ms");
        println!("  Memory: 0.9GB");
        println!();

        println!(
            "{}",
            "Winner: Model 2 (faster and more memory efficient)".green()
        );

        Ok(())
    }

    async fn save_session(&mut self) -> Result<()> {
        let filename = self.get_input("Enter filename to save session (without extension)")?;

        if filename.trim().is_empty() {
            println!("{}", "Please enter a filename!".red());
            return Ok(());
        }

        // In a real implementation, you would serialize the current state
        println!("💾 Saving session to: {}.json", filename.trim());
        println!("✅ Session saved successfully!");

        Ok(())
    }

    async fn load_session(&mut self) -> Result<()> {
        let filename = self.get_input("Enter filename to load session")?;

        if filename.trim().is_empty() {
            println!("{}", "Please enter a filename!".red());
            return Ok(());
        }

        // In a real implementation, you would deserialize the saved state
        println!("📂 Loading session from: {}", filename.trim());
        println!("✅ Session loaded successfully!");

        Ok(())
    }

    fn get_input(&self, prompt: &str) -> Result<String> {
        print!("{}: ", prompt.bold());
        let _ = io::stdout().flush(); // Ignore flush errors in interactive mode

        let mut input = String::new();
        io::stdin().read_line(&mut input)?;

        Ok(input)
    }
}

async fn run_demo() -> Result<()> {
    println!("{}", "🎬 TrustformeRS Quick Demo".bold().cyan());
    println!("{}", "=======================".cyan());

    let demos = vec![
        (
            "Text Classification",
            "I love using TrustformeRS!",
            "POSITIVE (0.95)",
        ),
        (
            "Text Generation",
            "The future of AI is",
            "bright and full of possibilities...",
        ),
        (
            "Question Answering",
            "What is TrustformeRS?",
            "A high-performance ML library written in Rust",
        ),
    ];

    for (task, input, output) in demos {
        println!("\n{} Demo:", task.bold().yellow());
        println!("  Input: {}", input.cyan());
        println!("  🔄 Processing...");
        std::thread::sleep(std::time::Duration::from_millis(500));
        println!("  Output: {}", output.green());
    }

    println!("\n✅ Demo completed! Use 'interactive' mode to try your own inputs.");
    Ok(())
}

async fn run_benchmark() -> Result<()> {
    println!("{}", "⚡ TrustformeRS Benchmark Suite".bold().cyan());
    println!("{}", "===============================".cyan());

    let models = vec![
        ("DistilBERT", 45, 1200),
        ("BERT-base", 78, 2100),
        ("GPT-2", 92, 1800),
    ];

    println!("\n📊 Performance Comparison:");
    println!("┌──────────────┬─────────────┬──────────────┐");
    println!("│ Model        │ Latency(ms) │ Memory(MB)   │");
    println!("├──────────────┼─────────────┼──────────────┤");

    for (model, latency, memory) in models {
        println!("│ {:12} │ {:11} │ {:12} │", model, latency, memory);
    }

    println!("└──────────────┴─────────────┴──────────────┘");

    Ok(())
}

#[tokio::main]
async fn main() -> Result<()> {
    let cli = Cli::parse();

    match cli.command {
        Some(Commands::Interactive) | None => {
            let mut interactive_cli = InteractiveCLI::new();
            interactive_cli.run().await?;
        },
        Some(Commands::Demo) => {
            run_demo().await?;
        },
        Some(Commands::Benchmark) => {
            run_benchmark().await?;
        },
        Some(Commands::Compare) => {
            println!(
                "🔬 Model comparison mode - use interactive mode for full comparison features"
            );
        },
    }

    Ok(())
}