Merge pull request #60 from AdaWorldAPI/claude/charming-johnson-ufstpw

feat(fma): /fma-body organ search + zoom + clinical NARS reasoning

Author: AdaWorldAPI

cockpit/public/fma_body.mesh.nodes.json

@@ -0,0 +1,323 @@
+//! Clinical NARS reasoning — the FMA `/fma-body` viewer's "reason about this organ"
+//! panel, backed by the REAL q2 NARS (`lance_graph_planner::nars::truth::TruthValue::
+//! deduction`, the same canonical algebra `graph_engine::nars_deduction` uses).
+//!
+//! A scenario {organ, condition, medication, labs} is compiled into a small clinical
+//! graph of `(subject --rel--> object, NarsTruth)` statements drawn from a hand-authored
+//! rule KB (condition→effects, drug→properties, (effect×property)→risk, lab→effect). NARS
+//! 2-hop deduction `A→B, B→C ⊢ A→C` then chains e.g. `acetaminophen → hepatically_metabolized
+//! → drug_accumulation_toxicity ⊢ acetaminophen → drug_accumulation_toxicity` with a derived
+//! truth value. Returns the inferences (frequency/confidence) + a plain-language summary.
+//!
+//! DEMO ONLY — a NARS reasoning illustration over a tiny rule set, NOT medical advice. The
+//! frontend shows that disclaimer in-view.
+
+use lance_graph_contract::exploration::NarsTruth;
+use lance_graph_planner::nars::truth::TruthValue;
+use serde::Serialize;
+
+/// One clinical statement: `subject --relation--> object` carrying a NARS truth value.
+#[derive(Clone)]
+struct Stmt {
+    s: String,
+    rel: String,
+    o: String,
+    truth: NarsTruth,
+}
+
+/// A derived (or asserted) clinical inference, wire-serialized with `truth_f`/`truth_c`
+/// to match the cockpit's existing NARS JSON convention (see graph_engine).
+#[derive(Clone)]
+pub struct CliInference {
+    s: String,
+    rel: String,
+    o: String,
+    kind: &'static str, // "Asserted" | "Deduction"
+    truth: NarsTruth,
+    via: Vec<String>,
+}
+
+impl Serialize for CliInference {
+    fn serialize<S: serde::Serializer>(&self, ser: S) -> Result<S::Ok, S::Error> {
+        use serde::ser::SerializeStruct;
+        let mut st = ser.serialize_struct("CliInference", 7)?;
+        st.serialize_field("source", &self.s)?;
+        st.serialize_field("relation", &self.rel)?;
+        st.serialize_field("target", &self.o)?;
+        st.serialize_field("inference_type", &self.kind)?;
+        st.serialize_field("truth_f", &self.truth.frequency)?;
+        st.serialize_field("truth_c", &self.truth.confidence)?;
+        st.serialize_field("via", &self.via)?;
+        st.end()
+    }
+}
+
+/// Canonical NARS deduction `A→B, B→C ⊢ A→C` via the planner's `TruthValue::deduction`
+/// (`f = f1·f2, c = f1·f2·c1·c2`) — identical bridge to `graph_engine::nars_deduction`.
+fn nars_deduction(ab: &NarsTruth, bc: &NarsTruth) -> NarsTruth {
+    let r = TruthValue::new(ab.frequency, ab.confidence)
+        .deduction(&TruthValue::new(bc.frequency, bc.confidence));
+    NarsTruth::new(r.frequency, r.confidence)
+}
+
+fn norm(s: &str) -> String {
+    s.trim().to_lowercase().replace([' ', '-'], "_")
+}
+
+// ── the rule KB (compact clinical demo) ───────────────────────────────────────────────
+// condition → physiological effects it induces.
+fn condition_effects(cond: &str) -> &'static [(&'static str, f32, f32)] {
+    match cond {
+        "cirrhosis" => &[
+            ("impaired_hepatic_clearance", 0.9, 0.85),
+            ("coagulopathy", 0.75, 0.8),
+            ("portal_hypertension", 0.7, 0.75),
+        ],
+        "hepatitis" => &[
+            ("hepatic_inflammation", 0.85, 0.8),
+            ("impaired_hepatic_clearance", 0.6, 0.7),
+        ],
+        "ckd" | "renal_failure" | "chronic_kidney_disease" => {
+            &[("impaired_renal_clearance", 0.9, 0.85)]
+        }
+        "heart_failure" => &[
+            ("renal_hypoperfusion", 0.7, 0.75),
+            ("fluid_overload", 0.8, 0.8),
+        ],
+        _ => &[],
+    }
+}
+// medication → pharmacologic properties.
+fn drug_properties(drug: &str) -> &'static [(&'static str, f32, f32)] {
+    match drug {
+        "acetaminophen" | "paracetamol" => &[
+            ("hepatically_metabolized", 0.95, 0.9),
+            ("hepatotoxic_in_overdose", 0.7, 0.8),
+        ],
+        "ibuprofen" | "naproxen" | "nsaid" => &[
+            ("renally_cleared", 0.8, 0.85),
+            ("gi_bleed_propensity", 0.65, 0.75),
+            ("nephrotoxic", 0.6, 0.7),
+        ],
+        "warfarin" => &[
+            ("hepatically_metabolized", 0.9, 0.85),
+            ("anticoagulant", 0.95, 0.9),
+        ],
+        "metformin" => &[
+            ("renally_cleared", 0.9, 0.9),
+            ("lactic_acidosis_if_accumulated", 0.6, 0.75),
+        ],
+        _ => &[],
+    }
+}
+// (active effect × drug property) → the risk it produces. property→risk is the edge NARS
+// chains through (drug → property → risk).
+fn risk_rule(effect: &str, property: &str) -> Option<(&'static str, f32, f32)> {
+    match (effect, property) {
+        ("impaired_hepatic_clearance", "hepatically_metabolized") => {
+            Some(("drug_accumulation_toxicity", 0.85, 0.8))
+        }
+        ("impaired_renal_clearance", "renally_cleared") => {
+            Some(("drug_accumulation_toxicity", 0.85, 0.8))
+        }
+        ("coagulopathy", "anticoagulant") => Some(("major_bleeding_risk", 0.9, 0.85)),
+        ("coagulopathy", "gi_bleed_propensity") => Some(("gi_hemorrhage_risk", 0.85, 0.8)),
+        ("portal_hypertension", "gi_bleed_propensity") => Some(("variceal_bleed_risk", 0.8, 0.8)),
+        ("renal_hypoperfusion", "nephrotoxic") => Some(("acute_kidney_injury_risk", 0.85, 0.8)),
+        ("impaired_renal_clearance", "lactic_acidosis_if_accumulated") => {
+            Some(("lactic_acidosis_risk", 0.8, 0.8))
+        }
+        _ => None,
+    }
+}
+// lab (name, abnormal flag) → the effect it asserts/reinforces.
+fn lab_effect(name: &str, flag: &str) -> Option<(&'static str, f32, f32)> {
+    match (name, flag) {
+        ("inr", "high") => Some(("coagulopathy", 0.85, 0.85)),
+        ("bilirubin", "high") => Some(("impaired_hepatic_clearance", 0.8, 0.8)),
+        ("albumin", "low") => Some(("hepatic_synthetic_dysfunction", 0.7, 0.75)),
+        ("creatinine", "high") => Some(("impaired_renal_clearance", 0.88, 0.85)),
+        ("egfr", "low") => Some(("impaired_renal_clearance", 0.88, 0.85)),
+        ("platelets", "low") => Some(("coagulopathy", 0.6, 0.7)),
+        _ => None,
+    }
+}
+
+#[derive(serde::Deserialize)]
+pub struct LabValue {
+    pub name: String,
+    #[serde(default)]
+    pub flag: String, // "high" | "low" | "normal"
+}
+
+#[derive(serde::Deserialize)]
+pub struct ClinicalScenario {
+    #[serde(default)]
+    pub organ: String,
+    #[serde(default)]
+    pub condition: String,
+    #[serde(default)]
+    pub medication: String,
+    #[serde(default)]
+    pub labs: Vec<LabValue>,
+}
+
+/// Compile the scenario → clinical statements, run 2-hop NARS deduction, return inferences.
+fn reason(sc: &ClinicalScenario) -> (Vec<CliInference>, Vec<String>) {
+    let (organ, cond, drug) = (norm(&sc.organ), norm(&sc.condition), norm(&sc.medication));
+    let mut stmts: Vec<Stmt> = Vec::new();
+    let mut effects: Vec<String> = Vec::new();
+    let push = |v: &mut Vec<Stmt>, s: &str, rel: &str, o: &str, f: f32, c: f32| {
+        v.push(Stmt {
+            s: s.into(),
+            rel: rel.into(),
+            o: o.into(),
+            truth: NarsTruth::new(f, c),
+        });
+    };
+
+    // organ --has--> condition
+    if !organ.is_empty() && !cond.is_empty() {
+        push(&mut stmts, &organ, "has", &cond, 1.0, 0.9);
+    }
+    // condition --induces--> effect
+    for &(e, f, c) in condition_effects(&cond) {
+        push(&mut stmts, &cond, "induces", e, f, c);
+        effects.push(e.to_string());
+    }
+    // lab --indicates--> effect (reinforces / asserts)
+    for lab in &sc.labs {
+        let (ln, lf) = (norm(&lab.name), norm(&lab.flag));
+        if let Some((e, f, c)) = lab_effect(&ln, &lf) {
+            push(&mut stmts, &format!("{ln}_{lf}"), "indicates", e, f, c);
+            if !effects.contains(&e.to_string()) {
+                effects.push(e.to_string());
+            }
+        }
+    }
+    // medication --is--> property ; property --confers--> risk (only for ACTIVE effects)
+    for &(p, f, c) in drug_properties(&drug) {
+        if !drug.is_empty() {
+            push(&mut stmts, &drug, "is", p, f, c);
+        }
+        for e in &effects {
+            if let Some((risk, rf, rc)) = risk_rule(e, p) {
+                push(&mut stmts, p, "confers", risk, rf, rc);
+            }
+        }
+    }
+
+    // assert + deduce
+    let mut out: Vec<CliInference> = stmts
+        .iter()
+        .map(|s| CliInference {
+            s: s.s.clone(),
+            rel: s.rel.clone(),
+            o: s.o.clone(),
+            kind: "Asserted",
+            truth: s.truth,
+            via: vec![],
+        })
+        .collect();
+
+    // 2-hop deduction A→B, B→C ⊢ A→C
+    let existing: std::collections::HashSet<(String, String)> =
+        stmts.iter().map(|s| (s.s.clone(), s.o.clone())).collect();
+    for ab in &stmts {
+        for bc in &stmts {
+            if ab.o == bc.s && ab.s != bc.o && !existing.contains(&(ab.s.clone(), bc.o.clone())) {
+                let t = nars_deduction(&ab.truth, &bc.truth);
+                if t.confidence >= 0.1 {
+                    out.push(CliInference {
+                        s: ab.s.clone(),
+                        rel: bc.rel.clone(),
+                        o: bc.o.clone(),
+                        kind: "Deduction",
+                        truth: t,
+                        via: vec![ab.o.clone()],
+                    });
+                }
+            }
+        }
+    }
+
+    // plain-language summary: strongest derived risks (expectation = c·(f−0.5)+0.5).
+    let mut derived: Vec<&CliInference> = out
+        .iter()
+        .filter(|i| i.kind == "Deduction" && (i.o.contains("risk") || i.o.contains("toxicity")))
+        .collect();
+    derived.sort_by(|a, b| {
+        let ex = |i: &CliInference| i.truth.confidence * (i.truth.frequency - 0.5) + 0.5;
+        ex(b)
+            .partial_cmp(&ex(a))
+            .unwrap_or(std::cmp::Ordering::Equal)
+    });
+    let mut summary: Vec<String> = derived
+        .iter()
+        .take(4)
+        .map(|i| {
+            let pretty = |s: &str| s.replace('_', " ");
+            format!(
+                "{} → {} (f={:.2}, c={:.2}, via {})",
+                pretty(&i.s),
+                pretty(&i.o),
+                i.truth.frequency,
+                i.truth.confidence,
+                i.via.join(", ")
+            )
+        })
+        .collect();
+    if summary.is_empty() {
+        summary.push("No risk chain derived from the rule KB for this scenario.".into());
+    }
+    (out, summary)
+}
+
+/// `POST /api/clinical/reason` — body `{organ, condition, medication, labs:[{name,flag}]}`.
+pub async fn clinical_reason_handler(
+    axum::Json(sc): axum::Json<ClinicalScenario>,
+) -> axum::Json<serde_json::Value> {
+    let (inferences, summary) = reason(&sc);
+    let asserted = inferences.iter().filter(|i| i.kind == "Asserted").count();
+    let derived = inferences.len() - asserted;
+    axum::Json(serde_json::json!({
+        "organ": sc.organ,
+        "condition": sc.condition,
+        "medication": sc.medication,
+        "asserted": asserted,
+        "derived": derived,
+        "inferences": inferences,
+        "summary": summary,
+        "engine": "lance_graph_planner::nars::truth::TruthValue::deduction (f=f1·f2, c=f1·f2·c1·c2)",
+        "disclaimer": "NARS reasoning DEMO over a small rule KB — not medical advice.",
+    }))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    #[test]
+    fn cirrhosis_acetaminophen_chains_to_accumulation() {
+        let sc = ClinicalScenario {
+            organ: "liver".into(),
+            condition: "cirrhosis".into(),
+            medication: "acetaminophen".into(),
+            labs: vec![LabValue {
+                name: "inr".into(),
+                flag: "high".into(),
+            }],
+        };
+        let (inf, summary) = reason(&sc);
+        // deduction must surface acetaminophen → drug_accumulation_toxicity via hepatically_metabolized
+        assert!(
+            inf.iter().any(|i| i.s == "acetaminophen"
+                && i.o == "drug_accumulation_toxicity"
+                && i.kind == "Deduction"),
+            "expected acetaminophen→drug_accumulation_toxicity; got {:?}",
+            inf.iter()
+                .map(|i| format!("{}→{}", i.s, i.o))
+                .collect::<Vec<_>>()
+        );
+        assert!(!summary.is_empty());
+    }
+}

cockpit/src/FmaBody.tsx

@@ -29,6 +29,7 @@ use tower_http::cors::CorsLayer;
 
 mod openai;
 mod graph_engine;
+mod clinical;
 mod osint_gotham;
 mod scene_player;
 mod shader_stream;
@@ -168,6 +169,8 @@ async fn main() {
         .route("/api/graph/snapshot", get(graph_engine::graph_snapshot_handler))
         .route("/api/graph/infer", post(graph_engine::nars_infer_handler))
         .route("/api/graph/health", get(graph_engine::graph_health_handler))
+        // Clinical NARS reasoning for the /fma-body organ panel (real TruthValue::deduction)
+        .route("/api/clinical/reason", post(clinical::clinical_reason_handler))
         // OSINT domain (classid 0x0700): the harvest as a CANON family-basin graph
         // (round→anchor basins, GUID-v2 tail), displayed via the OGAR ClassView.
         .route("/api/graph/osint", get(osint_gotham::osint_graph_handler))