kandil_code 2.1.1

//! Vision-First Code Understanding
//!
//! Implements image processing capabilities to interpret wireframes,
//! architecture diagrams, and UI mocks to generate executable code

use anyhow::Result;
use image::{DynamicImage, GenericImageView};
use serde::{Deserialize, Serialize};
use std::path::Path;

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ImageType {
    ArchitectureDiagram,
    UIMock,
    Whiteboard,
    Screenshot,
    Unknown,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DesignSpec {
    pub content: String,
    pub image_type: ImageType,
    pub detected_elements: Vec<DetectedElement>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectedElement {
    pub element_type: String,
    pub content: String,
    pub position: (u32, u32), // x, y coordinates
    pub size: (u32, u32),     // width, height
}

pub struct VisionAdapter {
    /// Image processing capabilities
    pub image_processor: ImageProcessor,
}

impl VisionAdapter {
    pub fn new() -> Self {
        Self {
            image_processor: ImageProcessor::new(),
        }
    }

    pub async fn interpret_design(&self, image_path: &str) -> Result<DesignSpec> {
        // Load and analyze the image
        let image = self.image_processor.load_image(image_path)?;

        // Classify the image type
        let image_type = self.classify_image(&image, image_path).await?;

        match image_type {
            ImageType::ArchitectureDiagram => self.extract_components(image_path).await,
            ImageType::UIMock => self.generate_html_css(image_path).await,
            ImageType::Whiteboard => self.parse_sketch(image_path).await,
            ImageType::Screenshot => self.interpret_screenshot(image_path).await,
            ImageType::Unknown => {
                // If unknown, try to generate code based on visual elements
                self.generate_code_from_visual_elements(image_path).await
            }
        }
    }

    async fn classify_image(&self, image: &DynamicImage, _image_path: &str) -> Result<ImageType> {
        // This would use actual image classification in a real implementation
        // For now, we'll use a simple heuristic based on common visual patterns

        let (width, height) = image.dimensions();
        let aspect_ratio = width as f32 / height as f32;

        // Check for common indicators
        let has_boxes_and_arrows = self.has_boxes_and_arrows(image)?;
        let has_ui_elements = self.has_ui_elements(image)?;
        let has_handwriting = self.has_handwriting(image)?;

        if has_boxes_and_arrows {
            Ok(ImageType::ArchitectureDiagram)
        } else if has_ui_elements {
            Ok(ImageType::UIMock)
        } else if has_handwriting {
            Ok(ImageType::Whiteboard)
        } else {
            Ok(ImageType::Unknown)
        }
    }

    async fn extract_components(&self, image_path: &str) -> Result<DesignSpec> {
        // In a real implementation, this would use computer vision to identify
        // and extract architectural components from diagrams
        // For now, we'll simulate the extraction

        let detected_elements = vec![
            DetectedElement {
                element_type: "database".to_string(),
                content: "User Database".to_string(),
                position: (100, 100),
                size: (80, 40),
            },
            DetectedElement {
                element_type: "service".to_string(),
                content: "Auth Service".to_string(),
                position: (250, 100),
                size: (80, 40),
            },
            DetectedElement {
                element_type: "service".to_string(),
                content: "API Gateway".to_string(),
                position: (400, 100),
                size: (80, 40),
            },
        ];

        let spec = DesignSpec {
            content: format!("Architecture diagram interpreted from: {}", image_path),
            image_type: ImageType::ArchitectureDiagram,
            detected_elements,
        };

        Ok(spec)
    }

    async fn generate_html_css(&self, image_path: &str) -> Result<DesignSpec> {
        // In a real implementation, this would use computer vision to identify
        // UI elements and generate appropriate HTML/CSS code
        // For now, we'll simulate the generation

        let html_template = r#"
<!DOCTYPE html>
<html>
<head>
    <title>Generated UI</title>
    <style>
        body { font-family: Arial, sans-serif; }
        .container { max-width: 800px; margin: 0 auto; }
    </style>
</head>
<body>
    <div class="container">
        <h1>Generated from UI mock: {}</h1>
        <p>UI elements would be generated based on visual analysis</p>
    </div>
</body>
</html>
        "#
        .trim();

        let content = html_template.replace("{}", image_path);

        let detected_elements = vec![
            DetectedElement {
                element_type: "header".to_string(),
                content: "Header element detected".to_string(),
                position: (0, 0),
                size: (800, 60),
            },
            DetectedElement {
                element_type: "content".to_string(),
                content: "Main content area".to_string(),
                position: (0, 60),
                size: (800, 400),
            },
        ];

        let spec = DesignSpec {
            content,
            image_type: ImageType::UIMock,
            detected_elements,
        };

        Ok(spec)
    }

    async fn parse_sketch(&self, image_path: &str) -> Result<DesignSpec> {
        // In a real implementation, this would use OCR and sketch analysis
        // to understand handwritten diagrams or sketches
        // For now, we'll simulate the parsing

        let detected_elements = vec![
            DetectedElement {
                element_type: "text".to_string(),
                content: "Login page".to_string(),
                position: (50, 50),
                size: (100, 20),
            },
            DetectedElement {
                element_type: "input".to_string(),
                content: "Username field".to_string(),
                position: (50, 100),
                size: (200, 30),
            },
            DetectedElement {
                element_type: "input".to_string(),
                content: "Password field".to_string(),
                position: (50, 150),
                size: (200, 30),
            },
            DetectedElement {
                element_type: "button".to_string(),
                content: "Login button".to_string(),
                position: (50, 200),
                size: (80, 30),
            },
        ];

        let spec = DesignSpec {
            content: format!("Whiteboard sketch interpreted from: {}", image_path),
            image_type: ImageType::Whiteboard,
            detected_elements,
        };

        Ok(spec)
    }

    async fn interpret_screenshot(&self, image_path: &str) -> Result<DesignSpec> {
        // In a real implementation, this would analyze a screenshot
        // to understand the UI elements and potentially reverse engineer code
        // For now, we'll simulate the interpretation

        let detected_elements = vec![
            DetectedElement {
                element_type: "window".to_string(),
                content: "Application window".to_string(),
                position: (0, 0),
                size: (800, 600),
            },
            DetectedElement {
                element_type: "toolbar".to_string(),
                content: "Menu bar".to_string(),
                position: (0, 0),
                size: (800, 30),
            },
        ];

        let spec = DesignSpec {
            content: format!("Screenshot interpreted from: {}", image_path),
            image_type: ImageType::Screenshot,
            detected_elements,
        };

        Ok(spec)
    }

    async fn generate_code_from_visual_elements(&self, image_path: &str) -> Result<DesignSpec> {
        // Fallback for unknown image types - try to generate code based on visual elements
        let detected_elements = vec![DetectedElement {
            element_type: "visual_element".to_string(),
            content: "Visual element detected".to_string(),
            position: (0, 0),
            size: (100, 100),
        }];

        let spec = DesignSpec {
            content: format!("Visual elements detected in: {}", image_path),
            image_type: ImageType::Unknown,
            detected_elements,
        };

        Ok(spec)
    }

    fn has_boxes_and_arrows(&self, _image: &DynamicImage) -> Result<bool> {
        // In a real implementation, this would analyze the image for
        // box-like shapes and arrow-like connections
        // For now, we'll return a placeholder
        Ok(false)
    }

    fn has_ui_elements(&self, _image: &DynamicImage) -> Result<bool> {
        // In a real implementation, this would detect UI elements like
        // buttons, text fields, etc.
        // For now, we'll return a placeholder
        Ok(false)
    }

    fn has_handwriting(&self, _image: &DynamicImage) -> Result<bool> {
        // In a real implementation, this would detect handwritten text
        // For now, we'll return a placeholder
        Ok(false)
    }
}

pub struct ImageProcessor;

impl ImageProcessor {
    pub fn new() -> Self {
        Self
    }

    pub fn load_image(&self, image_path: &str) -> Result<DynamicImage> {
        let path = Path::new(image_path);

        if !path.exists() {
            return Err(anyhow::anyhow!("Image file does not exist: {}", image_path));
        }

        let img = image::open(path)?;
        Ok(img)
    }

    pub fn resize_image(
        &self,
        img: &DynamicImage,
        max_width: u32,
        max_height: u32,
    ) -> DynamicImage {
        let (width, height) = img.dimensions();

        if width <= max_width && height <= max_height {
            return img.clone();
        }

        // Use f32::min instead of std::cmp::min for floating point values
        let scale_factor = (max_width as f32 / width as f32).min(max_height as f32 / height as f32);
        let new_width = (width as f32 * scale_factor) as u32;
        let new_height = (height as f32 * scale_factor) as u32;

        img.resize(new_width, new_height, image::imageops::Triangle)
    }

    pub fn detect_edges(&self, img: &DynamicImage) -> Result<DynamicImage> {
        // Apply edge detection filter
        // This is a simplified implementation
        let mut grayscale_img = img.grayscale();
        image::imageops::invert(&mut grayscale_img);
        Ok(grayscale_img)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_image_processor_creation() {
        let processor = ImageProcessor::new();
        assert!(true); // Just testing creation
    }

    #[test]
    fn test_design_spec_creation() {
        let spec = DesignSpec {
            content: "Test spec".to_string(),
            image_type: ImageType::ArchitectureDiagram,
            detected_elements: vec![],
        };

        assert_eq!(spec.content, "Test spec");
        assert_eq!(spec.image_type, ImageType::ArchitectureDiagram);
        assert!(spec.detected_elements.is_empty());
    }
}