1pub mod bridge_status;
2pub mod gain_score;
3pub mod model_pricing;
4pub mod task_classifier;
5
6use serde::{Deserialize, Serialize};
7
8use crate::core::a2a::cost_attribution::CostStore;
9use crate::core::gain::gain_score::GainScore;
10use crate::core::gain::model_pricing::{ModelPricing, ModelQuote};
11use crate::core::gain::task_classifier::{TaskCategory, TaskClassifier};
12use crate::core::heatmap::HeatMap;
13use crate::core::stats::StatsStore;
14
15#[derive(Clone)]
16pub struct GainEngine {
17 pub stats: StatsStore,
18 pub costs: CostStore,
19 pub heatmap: HeatMap,
20 pub pricing: ModelPricing,
21 pub events: Vec<crate::core::events::LeanCtxEvent>,
22 pub session: Option<crate::core::session::SessionState>,
23}
24
25#[derive(Debug, Clone, Serialize, Deserialize)]
26pub struct GainSummary {
27 pub model: ModelQuote,
28 pub total_commands: u64,
29 pub input_tokens: u64,
30 pub output_tokens: u64,
31 pub tokens_saved: u64,
32 pub gain_rate_pct: f64,
33 #[serde(default)]
40 pub injected_overhead_tokens_per_turn: u64,
41 #[serde(default)]
46 pub turns: u64,
47 #[serde(default)]
50 pub injected_overhead_total_tokens: u64,
51 #[serde(default)]
55 pub net_tokens_saved: i64,
56 pub avoided_usd: f64,
57 pub energy_wh: f64,
59 pub co2_grams: f64,
61 pub tool_spend_usd: f64,
62 pub roi: Option<f64>,
63 pub score: GainScore,
64 #[serde(skip_serializing_if = "Option::is_none", default)]
65 pub daemon_hint: Option<String>,
66}
67
68#[derive(Debug, Clone, Serialize, Deserialize)]
69pub struct TaskGainRow {
70 pub category: TaskCategory,
71 pub commands: u64,
72 pub tokens_saved: u64,
73 pub tool_calls: u64,
74 pub tool_spend_usd: f64,
75}
76
77#[derive(Debug, Clone, Serialize, Deserialize)]
78pub struct FileGainRow {
79 pub path: String,
80 pub access_count: u32,
81 pub tokens_saved: u64,
82 pub compression_pct: f32,
83}
84
85impl GainEngine {
86 pub fn load() -> Self {
87 Self {
88 stats: crate::core::stats::load(),
89 costs: crate::core::a2a::cost_attribution::CostStore::load(),
90 heatmap: crate::core::heatmap::HeatMap::load(),
91 pricing: ModelPricing::load(),
92 events: crate::core::events::load_events_from_file(500),
93 session: crate::core::session::SessionState::load_latest(),
94 }
95 }
96
97 pub fn summary(&self, model: Option<&str>) -> GainSummary {
98 let quote = self.pricing.quote(model);
99 let tokens_saved = self
100 .stats
101 .total_input_tokens
102 .saturating_sub(self.stats.total_output_tokens);
103 let gain_rate_pct = if self.stats.total_input_tokens > 0 {
104 tokens_saved as f64 / self.stats.total_input_tokens as f64 * 100.0
105 } else {
106 0.0
107 };
108 let avoided_usd = quote.cost.estimate_usd(tokens_saved, 0, 0, 0);
109 let tool_spend_usd = self.costs.total_cost().max(0.0);
110 let roi = if tool_spend_usd > 0.0 {
111 Some(avoided_usd / tool_spend_usd)
112 } else {
113 None
114 };
115 let score = GainScore::compute(&self.stats, &self.costs, &self.pricing, model);
116 #[cfg(unix)]
117 let daemon_hint = if crate::daemon::is_daemon_running() {
118 None
119 } else {
120 Some(
121 "daemon not running — stats tracked locally (lean-ctx serve -d for full tracking)"
122 .to_string(),
123 )
124 };
125 #[cfg(not(unix))]
126 let daemon_hint: Option<String> = None;
127 let injected_overhead_tokens_per_turn =
128 crate::core::context_overhead::ContextOverhead::cached().total_tokens() as u64;
129 let turns = crate::core::context_overhead::observed_turns();
134 let (injected_overhead_total_tokens, net_tokens_saved) =
135 crate::core::context_overhead::net_of_injection(
136 tokens_saved,
137 injected_overhead_tokens_per_turn,
138 turns,
139 );
140 GainSummary {
141 model: quote,
142 total_commands: self.stats.total_commands,
143 input_tokens: self.stats.total_input_tokens,
144 output_tokens: self.stats.total_output_tokens,
145 tokens_saved,
146 gain_rate_pct,
147 injected_overhead_tokens_per_turn,
148 turns,
149 injected_overhead_total_tokens,
150 net_tokens_saved,
151 avoided_usd,
152 energy_wh: crate::core::energy::wh_for_tokens(tokens_saved),
153 co2_grams: crate::core::energy::co2_grams_for_tokens(tokens_saved),
154 tool_spend_usd,
155 roi,
156 score,
157 daemon_hint,
158 }
159 }
160
161 pub fn gain_score(&self, model: Option<&str>) -> GainScore {
162 GainScore::compute(&self.stats, &self.costs, &self.pricing, model)
163 }
164
165 pub fn task_breakdown(&self) -> Vec<TaskGainRow> {
166 use std::collections::HashMap;
167
168 let mut by_cat: HashMap<TaskCategory, TaskGainRow> = HashMap::new();
169
170 for (cmd_key, st) in &self.stats.commands {
171 let cat = TaskClassifier::classify_command_key(cmd_key);
172 let row = by_cat.entry(cat).or_insert(TaskGainRow {
173 category: cat,
174 commands: 0,
175 tokens_saved: 0,
176 tool_calls: 0,
177 tool_spend_usd: 0.0,
178 });
179 row.commands += st.count;
180 row.tokens_saved += st.input_tokens.saturating_sub(st.output_tokens);
181 }
182
183 for (tool, tc) in &self.costs.tools {
184 let cat = TaskClassifier::classify_tool(tool);
185 let row = by_cat.entry(cat).or_insert(TaskGainRow {
186 category: cat,
187 commands: 0,
188 tokens_saved: 0,
189 tool_calls: 0,
190 tool_spend_usd: 0.0,
191 });
192 row.tool_calls += tc.total_calls;
193 row.tool_spend_usd += tc.cost_usd;
194 }
195
196 let mut out: Vec<TaskGainRow> = by_cat.into_values().collect();
197 out.sort_by_key(|x| std::cmp::Reverse(x.tokens_saved));
198 out
199 }
200
201 pub fn heatmap_gains(&self, limit: usize) -> Vec<FileGainRow> {
202 let mut items: Vec<_> = self.heatmap.entries.values().collect();
203 items.sort_by_key(|x| std::cmp::Reverse(x.total_tokens_saved));
204 items.truncate(limit);
205 items
206 .into_iter()
207 .map(|e| FileGainRow {
208 path: e.path.clone(),
209 access_count: e.access_count,
210 tokens_saved: e.total_tokens_saved,
211 compression_pct: e.avg_compression_ratio * 100.0,
212 })
213 .collect()
214 }
215}