kandil_code 0.1.0

//! IDE extension prototype
//! 
//! Prototype for IDE integration with Kandil Code features

use anyhow::Result;
use serde::{Deserialize, Serialize};
use crate::core::adapters::ai::KandilAI;
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IdeExtension {
    pub name: String,
    pub version: String,
    pub supported_ide: Vec<String>, // e.g., "VSCode", "IntelliJ", "Vim"
    pub features: Vec<ExtensionFeature>,
    pub ai_client: Arc<KandilAI>,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ExtensionFeature {
    InlineChat,
    CodeGeneration,
    Refactoring,
    CodeReview,
    Testing,
    Documentation,
    DebuggingAssistant,
    Deployment,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExtensionContext {
    pub file_path: String,
    pub language: String,
    pub selected_code: String,
    pub cursor_position: (u32, u32),
    pub workspace_root: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CodeSuggestion {
    pub id: String,
    pub title: String,
    pub description: String,
    pub code: String,
    pub range: (u32, u32), // Start and end line
    pub confidence: f32,
    pub category: SuggestionCategory,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SuggestionCategory {
    Optimization,
    Security,
    Performance,
    Style,
    BugFix,
    Enhancement,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InlineComment {
    pub line_number: u32,
    pub message: String,
    pub severity: String, // "error", "warning", "info"
    pub code_fix: Option<String>,
}

impl IdeExtension {
    pub fn new(ai_client: Arc<KandilAI>) -> Self {
        Self {
            name: "Kandil Code Extension".to_string(),
            version: "0.1.0".to_string(),
            supported_ide: vec![
                "VSCode".to_string(),
                "IntelliJ".to_string(),
                "Vim".to_string(),
                "Emacs".to_string(),
            ],
            features: vec![
                ExtensionFeature::InlineChat,
                ExtensionFeature::CodeGeneration,
                ExtensionFeature::Refactoring,
                ExtensionFeature::CodeReview,
                ExtensionFeature::Testing,
                ExtensionFeature::Documentation,
            ],
            ai_client,
        }
    }

    pub async fn get_code_suggestions(&self, context: &ExtensionContext) -> Result<Vec<CodeSuggestion>> {
        let prompt = format!(
            r#"Analyze this code for improvements:
            Language: {}
            
            Code:
            {}
            
            Provide specific, actionable suggestions for optimization, security, performance, or style improvements.
            "#,
            context.language, context.selected_code
        );

        let suggestions_text = self.ai_client.chat(&prompt).await?;
        
        // In a real implementation, this would parse the structured response
        // For simulation, return basic suggestions
        Ok(vec![
            CodeSuggestion {
                id: "sugg-001".to_string(),
                title: "Performance Optimization".to_string(),
                description: "Consider caching this computation".to_string(),
                code: "let cached_result = expensive_computation();".to_string(),
                range: (context.cursor_position.0, context.cursor_position.0 + 1),
                confidence: 0.85,
                category: SuggestionCategory::Performance,
            }
        ])
    }

    pub async fn generate_documentation(&self, code: &str, language: &str) -> Result<String> {
        let prompt = format!(
            r#"Generate documentation for this {} code:
            
            {}
            
            Include:
            - Function/class descriptions
            - Parameter documentation
            - Return value documentation
            - Example usage
            - Important notes
            "#,
            language, code
        );

        self.ai_client.chat(&prompt).await
    }

    pub async fn get_refactoring_options(&self, code: &str, language: &str) -> Result<Vec<String>> {
        let prompt = format!(
            r#"Suggest refactoring options for this {} code:
            
            {}
            
            Focus on: readability, maintainability, and best practices.
            "#,
            language, code
        );

        let response = self.ai_client.chat(&prompt).await?;
        
        // In a real implementation, this would parse multiple options
        Ok(vec![response])
    }

    pub async fn run_inline_code_review(&self, code: &str, language: &str) -> Result<Vec<InlineComment>> {
        let prompt = format!(
            r#"Perform inline code review for this {} code:
            
            {}
            
            Identify issues and suggest fixes. Return in format: line_number, issue, severity, fix.
            "#,
            language, code
        );

        let review = self.ai_client.chat(&prompt).await?;
        
        // For simulation, return basic comment
        Ok(vec![
            InlineComment {
                line_number: 1,
                message: review.chars().take(100).collect::<String>(),
                severity: "info".to_string(),
                code_fix: Some("Consider adding error handling".to_string()),
            }
        ])
    }

    pub async fn get_test_suggestions(&self, code: &str, language: &str) -> Result<String> {
        let prompt = format!(
            r#"Generate test cases for this {} code:
            
            {}
            
            Include unit tests, integration tests, and edge case tests.
            "#,
            language, code
        );

        self.ai_client.chat(&prompt).await
    }

    pub async fn explain_code(&self, code: &str, language: &str) -> Result<String> {
        let prompt = format!(
            r#"Explain this {} code in simple terms:
            
            {}
            
            Include the purpose, how it works, and any important details.
            "#,
            language, code
        );

        self.ai_client.chat(&prompt).await
    }

    pub fn get_supported_features(&self) -> &Vec<ExtensionFeature> {
        &self.features
    }

    pub fn is_feature_available(&self, feature: &ExtensionFeature) -> bool {
        self.features.contains(feature)
    }
}