kandil_code 2.1.1

//! Green Development Agent
//!
//! Agent that audits code for carbon footprint and energy efficiency

use crate::core::adapters::ai::KandilAI;
use crate::core::agents::base::{Agent, AgentState};
use anyhow::Result;
use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CarbonReport {
    pub total_estimated_kwh: f64,
    pub breakdown: HashMap<String, f64>, // by component/function
    pub optimization_suggestions: Vec<Suggestion>,
    pub efficiency_score: u8, // 0-100
    pub environmental_impact: EnvironmentalImpact,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Suggestion {
    pub id: String,
    pub title: String,
    pub description: String,
    pub estimated_kwh_saved: f64,
    pub implementation_effort: EffortLevel,
    pub priority: Priority,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EffortLevel {
    Low,
    Medium,
    High,
    VeryHigh,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Inefficiency {
    pub location: String,
    pub pattern: String,
    pub impact_level: ImpactLevel,
    pub suggested_fix: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImpactLevel {
    Low,
    Medium,
    High,
    VeryHigh,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EnvironmentalImpact {
    Minimal,
    Low,
    Moderate,
    High,
    VeryHigh,
}

#[derive(Debug, Clone)]
pub struct GreenDevAgent {
    ai: Arc<KandilAI>,
}

impl GreenDevAgent {
    pub fn new(ai: Arc<KandilAI>) -> Self {
        Self { ai }
    }

    pub async fn carbon_audit(&self, code: &str, language: &str) -> Result<CarbonReport> {
        let prompt = format!(
            r#"Estimate carbon footprint of this {} code:
            {}

            Consider:
            - Algorithmic complexity (O(n^3) vs O(log n))
            - Data transfer sizes
            - Resource idle time
            - Cache efficiency
            - Memory usage patterns
            - Database query optimization
            - Network requests

            Suggest green alternatives.
            "#,
            language, code
        );

        let audit_text = self.ai.chat(&prompt).await?;

        // In a real implementation, this would parse the structured response
        // For simulation, return basic report
        Ok(CarbonReport {
            total_estimated_kwh: 0.005, // 5 Wh per execution
            breakdown: vec![
                ("main_algorithm".to_string(), 0.002),
                ("data_processing".to_string(), 0.003),
            ]
            .into_iter()
            .collect(),
            optimization_suggestions: vec![Suggestion {
                id: "GREEN-001".to_string(),
                title: "Implement caching".to_string(),
                description: "Cache computation results to avoid repeated work".to_string(),
                estimated_kwh_saved: 0.001,
                implementation_effort: EffortLevel::Medium,
                priority: Priority::High,
            }],
            efficiency_score: 72,
            environmental_impact: EnvironmentalImpact::Moderate,
        })
    }

    pub async fn optimize_energy(
        &self,
        inefficiencies: &[Inefficiency],
    ) -> Result<Vec<Suggestion>> {
        let inefficiency_descriptions: Vec<String> = inefficiencies
            .iter()
            .map(|ie| {
                format!(
                    "Location: {}, Pattern: {}, Impact: {:?}",
                    ie.location, ie.pattern, ie.impact_level
                )
            })
            .collect();

        let prompt = format!(
            r#"Optimize these energy-inefficient patterns:
            {:?}

            Return specific code changes with estimated kWh savings.
            "#,
            inefficiency_descriptions
        );

        let suggestions_text = self.ai.chat(&prompt).await?;

        // For simulation, return a basic suggestion
        Ok(vec![Suggestion {
            id: "OPT-001".to_string(),
            title: "Algorithm optimization".to_string(),
            description: "Replace inefficient algorithm with more efficient one".to_string(),
            estimated_kwh_saved: 0.0015,
            implementation_effort: EffortLevel::High,
            priority: Priority::Critical,
        }])
    }

    pub async fn analyze_infrastructure(&self, infra_desc: &str) -> Result<CarbonReport> {
        let prompt = format!(
            r#"Analyze the carbon footprint of this infrastructure:
            {}

            Consider:
            - Server efficiency and utilization
            - Data center energy sources
            - Network traffic patterns
            - Resource provisioning (over-provisioning)
            - Auto-scaling effectiveness
            - Data storage optimization
            "#,
            infra_desc
        );

        let analysis = self.ai.chat(&prompt).await?;

        Ok(CarbonReport {
            total_estimated_kwh: 120.5, // kWh per day
            breakdown: vec![
                ("compute".to_string(), 80.0),
                ("network".to_string(), 25.0),
                ("storage".to_string(), 15.5),
            ]
            .into_iter()
            .collect(),
            optimization_suggestions: vec![Suggestion {
                id: "INFRA-GREEN-001".to_string(),
                title: "Right-size VMs".to_string(),
                description: "Adjust instance sizes based on actual usage patterns".to_string(),
                estimated_kwh_saved: 35.0,
                implementation_effort: EffortLevel::Low,
                priority: Priority::High,
            }],
            efficiency_score: 65,
            environmental_impact: EnvironmentalImpact::High,
        })
    }
}

#[async_trait]
impl Agent for GreenDevAgent {
    async fn plan(&self, state: &AgentState) -> Result<String> {
        let prompt = format!(
            "As a Green Development Specialist, given this sustainability task: {}\n\nPlan the next green optimization activity. Consider energy efficiency, resource optimization, and environmental impact.",
            state.task
        );

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

    async fn act(&self, plan: &str) -> Result<String> {
        // Execute the planned green optimization
        let prompt = format!(
            "Implement this green optimization plan: {}\n\nAnalyze code, optimize algorithms, or improve resource usage for sustainability.",
            plan
        );

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

    async fn observe(&self, result: &str) -> Result<String> {
        // Analyze green development results
        let prompt = format!(
            "Analyze these sustainability results: {}\n\nHow does this optimization impact energy consumption and environmental footprint?",
            result
        );

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