//! 👁️ GPT-4 Vision Processing
//!
//! This example demonstrates GPT-4 Vision capabilities including:
//! - Image analysis and description
//! - Vision prompt optimization
//! - Detail level control
//! - Cost optimization strategies
//! - Multi-modal interactions
//! - Practical vision use cases
//!
//! Before running, set your API key:
//! ```bash
//! export OPENAI_API_KEY="your-openai-key"
//! ```
//!
//! Usage:
//! ```bash
//! cargo run --example openai_vision_processing
//! ```

use siumai::prelude::*;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("👁️ GPT-4 Vision Processing Demo\n");

    // Get API key
    let api_key = std::env::var("OPENAI_API_KEY").unwrap_or_else(|_| {
        println!("⚠️  OPENAI_API_KEY not set, using demo key");
        "demo-key".to_string()
    });

    println!("🔍 Demonstrating GPT-4 Vision Capabilities:");
    println!("   1. Basic Image Analysis");
    println!("   2. Detailed Image Description");
    println!("   3. Object Detection and Counting");
    println!("   4. Text Extraction (OCR)");
    println!("   5. Image Comparison");
    println!("   6. Cost Optimization Strategies\n");

    // Demo 1: Basic Image Analysis
    println!("📸 1. Basic Image Analysis");
    demo_basic_image_analysis(&api_key).await?;
    println!();

    // Demo 2: Detailed Description
    println!("🔍 2. Detailed Image Description");
    demo_detailed_description(&api_key).await?;
    println!();

    // Demo 3: Object Detection
    println!("🎯 3. Object Detection and Counting");
    demo_object_detection(&api_key).await?;
    println!();

    // Demo 4: Text Extraction
    println!("📝 4. Text Extraction (OCR)");
    demo_text_extraction(&api_key).await?;
    println!();

    // Demo 5: Image Comparison
    println!("⚖️ 5. Image Comparison");
    demo_image_comparison(&api_key).await?;
    println!();

    // Demo 6: Cost Optimization
    println!("💰 6. Cost Optimization Strategies");
    demo_cost_optimization(&api_key).await?;

    println!("\n✅ GPT-4 Vision Processing demo completed!");
    Ok(())
}

/// Demo basic image analysis
async fn demo_basic_image_analysis(api_key: &str) -> Result<(), Box<dyn std::error::Error>> {
    println!("   Setting up GPT-4 Vision for basic analysis...");

    let ai = Siumai::builder()
        .openai()
        .api_key(api_key)
        .model("gpt-4o-mini") // Note: In real usage, use "gpt-4-vision-preview"
        .temperature(0.3)
        .max_tokens(300)
        .build()
        .await?;

    // Simulate image analysis (in real usage, you'd include image data)
    let messages = vec![
        ChatMessage::system(
            "You are an expert image analyst. Describe what you see in images \
            with focus on key objects, people, settings, and overall composition.",
        )
        .build(),
        ChatMessage::user(
            "Analyze this image: [Image would be included here in real usage] \
            For this demo, imagine you're looking at a photo of a modern office \
            workspace with a laptop, coffee cup, notebook, and plants on a desk \
            near a window with natural light.",
        )
        .build(),
    ];

    println!("   Analyzing image content...");

    match ai.chat(messages).await {
        Ok(response) => {
            if let Some(text) = response.text() {
                println!("   📊 Analysis Result:");
                println!("   {text}");
            }
        }
        Err(e) => println!("   ❌ Error: {e}"),
    }

    Ok(())
}

/// Demo detailed image description
async fn demo_detailed_description(api_key: &str) -> Result<(), Box<dyn std::error::Error>> {
    println!("   Creating detailed image description...");

    let ai = Siumai::builder()
        .openai()
        .api_key(api_key)
        .model("gpt-4o-mini")
        .temperature(0.2)
        .max_tokens(500)
        .build()
        .await?;

    let messages = vec![
        ChatMessage::system(
            "You are a professional image describer. Provide detailed, \
            comprehensive descriptions including colors, textures, lighting, \
            composition, mood, and any text visible in the image.",
        )
        .build(),
        ChatMessage::user(
            "Provide a detailed description of this image: [Image placeholder] \
            For this demo, imagine a sunset landscape photo showing mountains \
            silhouetted against an orange and purple sky, with a lake in the \
            foreground reflecting the colors.",
        )
        .build(),
    ];

    println!("   Generating detailed description...");

    match ai.chat(messages).await {
        Ok(response) => {
            if let Some(text) = response.text() {
                println!("   📝 Detailed Description:");
                println!("   {text}");
            }
        }
        Err(e) => println!("   ❌ Error: {e}"),
    }

    Ok(())
}

/// Demo object detection and counting
async fn demo_object_detection(api_key: &str) -> Result<(), Box<dyn std::error::Error>> {
    println!("   Performing object detection and counting...");

    let ai = Siumai::builder()
        .openai()
        .api_key(api_key)
        .model("gpt-4o-mini")
        .temperature(0.1)
        .max_tokens(400)
        .build()
        .await?;

    let messages = vec![
        ChatMessage::system(
            "You are an object detection specialist. Count and identify \
            all objects in the image. Provide a structured list with \
            object names and quantities.",
        )
        .build(),
        ChatMessage::user(
            "Count and identify all objects in this image: [Image placeholder] \
            For this demo, imagine a kitchen scene with 3 apples, 2 bananas, \
            1 cutting board, 1 knife, 2 bowls, and 1 coffee maker on the counter.",
        )
        .build(),
    ];

    println!("   Detecting and counting objects...");

    match ai.chat(messages).await {
        Ok(response) => {
            if let Some(text) = response.text() {
                println!("   🎯 Object Detection Results:");
                println!("   {text}");
            }
        }
        Err(e) => println!("   ❌ Error: {e}"),
    }

    Ok(())
}

/// Demo text extraction (OCR)
async fn demo_text_extraction(api_key: &str) -> Result<(), Box<dyn std::error::Error>> {
    println!("   Extracting text from image...");

    let ai = Siumai::builder()
        .openai()
        .api_key(api_key)
        .model("gpt-4o-mini")
        .temperature(0.0)
        .max_tokens(300)
        .build()
        .await?;

    let messages = vec![
        ChatMessage::system(
            "You are an OCR specialist. Extract all visible text from images \
            accurately, maintaining formatting and structure where possible.",
        )
        .build(),
        ChatMessage::user(
            "Extract all text from this image: [Image placeholder] \
            For this demo, imagine a business card with: \
            'John Smith, Software Engineer, TechCorp Inc., \
            john.smith@techcorp.com, (555) 123-4567'",
        )
        .build(),
    ];

    println!("   Performing OCR extraction...");

    match ai.chat(messages).await {
        Ok(response) => {
            if let Some(text) = response.text() {
                println!("   📝 Extracted Text:");
                println!("   {text}");
            }
        }
        Err(e) => println!("   ❌ Error: {e}"),
    }

    Ok(())
}

/// Demo image comparison
async fn demo_image_comparison(api_key: &str) -> Result<(), Box<dyn std::error::Error>> {
    println!("   Comparing multiple images...");

    let ai = Siumai::builder()
        .openai()
        .api_key(api_key)
        .model("gpt-4o-mini")
        .temperature(0.2)
        .max_tokens(400)
        .build()
        .await?;

    let messages = vec![
        ChatMessage::system(
            "You are an image comparison expert. Compare images and identify \
            similarities, differences, and notable changes between them.",
        )
        .build(),
        ChatMessage::user(
            "Compare these two images: [Images placeholder] \
            For this demo, imagine comparing two photos of the same room - \
            one before and one after renovation. The before shows old furniture \
            and carpet, the after shows modern furniture and hardwood floors.",
        )
        .build(),
    ];

    println!("   Analyzing image differences...");

    match ai.chat(messages).await {
        Ok(response) => {
            if let Some(text) = response.text() {
                println!("   ⚖️ Comparison Results:");
                println!("   {text}");
            }
        }
        Err(e) => println!("   ❌ Error: {e}"),
    }

    Ok(())
}

/// Demo cost optimization strategies
async fn demo_cost_optimization(api_key: &str) -> Result<(), Box<dyn std::error::Error>> {
    println!("   Demonstrating cost optimization strategies...");

    // Strategy 1: Low detail for simple tasks
    println!("   Strategy 1: Low detail mode for simple analysis");
    let ai_low = Siumai::builder()
        .openai()
        .api_key(api_key)
        .model("gpt-4o-mini")
        .temperature(0.3)
        .max_tokens(150) // Reduced tokens
        .build()
        .await?;

    let simple_messages = vec![
        ChatMessage::user(
            "Is this image primarily indoors or outdoors? [Image placeholder] \
            For demo: outdoor mountain landscape",
        )
        .build(),
    ];

    match ai_low.chat(simple_messages).await {
        Ok(response) => {
            if let Some(text) = response.text() {
                println!("   💰 Low-cost analysis: {}", text.trim());
            }
        }
        Err(e) => println!("   ❌ Error: {e}"),
    }

    // Strategy 2: Batch processing
    println!("   Strategy 2: Batch processing multiple questions");
    let batch_messages = vec![
        ChatMessage::user(
            "For this image [placeholder], answer these questions in order: \
            1. What is the main subject? \
            2. What colors dominate? \
            3. Is it day or night? \
            4. Indoor or outdoor? \
            For demo: sunset beach scene with people walking",
        )
        .build(),
    ];

    match ai_low.chat(batch_messages).await {
        Ok(response) => {
            if let Some(text) = response.text() {
                println!("   📦 Batch analysis:");
                for (i, line) in text.lines().take(4).enumerate() {
                    println!("      {}. {}", i + 1, line.trim());
                }
            }
        }
        Err(e) => println!("   ❌ Error: {e}"),
    }

    // Strategy 3: Preprocessing recommendations
    println!("   Strategy 3: Image preprocessing recommendations");
    println!("   💡 Cost Optimization Tips:");
    println!("      • Resize images to optimal dimensions (max 2048x2048)");
    println!("      • Use JPEG format for photographs");
    println!("      • Compress images while maintaining quality");
    println!("      • Batch multiple questions in single request");
    println!("      • Use low detail mode for simple tasks");
    println!("      • Cache results for repeated analyses");

    Ok(())
}

/// 🎯 Key GPT-4 Vision Features Summary:
///
/// Core Capabilities:
/// - Image understanding and description
/// - Object detection and counting
/// - Text extraction (OCR)
/// - Scene analysis and composition
/// - Multi-modal conversations
///
/// Advanced Features:
/// - Image comparison and analysis
/// - Detail level control (low/high)
/// - Custom vision prompts
/// - Structured output formats
/// - Batch processing support
///
/// Use Cases:
/// - Content moderation
/// - Accessibility descriptions
/// - Document processing
/// - Quality control
/// - Educational tools
/// - Creative assistance
///
/// Cost Optimization:
/// - Image preprocessing
/// - Detail level selection
/// - Batch question processing
/// - Result caching
/// - Optimal image formats
///
/// Best Practices:
/// - Clear, specific prompts
/// - Appropriate detail levels
/// - Efficient image formats
/// - Structured questioning
/// - Error handling
///
/// Production Considerations:
/// - Rate limiting
/// - Image size limits
/// - Cost monitoring
/// - Quality validation
/// - Privacy compliance
///
/// Next Steps:
/// - Implement image preprocessing
/// - Add result caching
/// - Create vision workflows
/// - Optimize for specific use cases
const fn _documentation() {}
