fix: recommendation system

api/routers/predict_v2.py

@@ -76,9 +76,8 @@ async def predict_batch_v2(req: BatchPredictRequest):
 async def recommend_v2(req: RecommendationRequest):
     """Get operational recommendations using v2 models.
 
-    v2 FIX: Uses user-provided current_soh to compute baseline RUL instead
-    of re-predicting SOH from default features (which caused ~0 cycle
-    improvement in v1).
+    Ranking is based on net RUL improvement versus a model-derived baseline,
+    with small guardrail penalties for clearly harsher operating conditions.
     """
     import itertools
 
@@ -86,15 +85,6 @@ async def recommend_v2(req: RecommendationRequest):
     currents = [0.5, 1.0, 2.0, 4.0]
     cutoffs = [2.0, 2.2, 2.5, 2.7]
 
-    # v2 FIX: compute baseline RUL from user-provided current_soh
-    # Data-driven: linear degradation at a realistic rate (~0.2%/cycle)
-    EOL_THRESHOLD = 70.0
-    deg_rate = 0.2  # conservative NASA-calibrated %/cycle
-    if req.current_soh > EOL_THRESHOLD:
-        baseline_rul = (req.current_soh - EOL_THRESHOLD) / deg_rate
-    else:
-        baseline_rul = 0.0
-
     base_features = {
         "cycle_number": req.current_cycle,
         "ambient_temperature": req.ambient_temperature,
@@ -110,22 +100,75 @@ async def recommend_v2(req: RecommendationRequest):
         "delta_capacity": -0.005,
     }
 
+    def guardrail_penalty(temp: float, current: float, cutoff: float) -> float:
+        """Penalty in cycle-units for stress-heavy operating points.
+
+        This keeps recommendations aligned with battery-care intent even when
+        model outputs are noisy for out-of-distribution combinations.
+        """
+        # Room temperature (~24C) is generally healthiest; high heat is penalized most.
+        temp_penalty = max(0.0, temp - 30.0) * 3.0 + max(0.0, 12.0 - temp) * 1.5
+        # Aggressive discharge currents accelerate degradation.
+        current_penalty = max(0.0, current - 1.5) * 12.0
+        # Very low cutoff voltages imply deeper discharge stress.
+        cutoff_penalty = max(0.0, 2.4 - cutoff) * 8.0
+        return temp_penalty + current_penalty + cutoff_penalty
+
+    baseline_features = {
+        **base_features,
+        "ambient_temperature": req.ambient_temperature,
+        "avg_current": 1.82,
+        "min_voltage": 2.61,
+        "voltage_range": 4.19 - 2.61,
+        "avg_temp": req.ambient_temperature + 8.0,
+    }
+    baseline_pred = registry_v2.predict(baseline_features, req.model_name)
+    baseline_rul = max(0.0, float(baseline_pred.get("rul_cycles", 0) or 0))
+    baseline_adjusted_rul = baseline_rul - guardrail_penalty(
+        req.ambient_temperature,
+        1.82,
+        2.61,
+    )
+
     candidates = []
     for t, c, v in itertools.product(temps, currents, cutoffs):
         feat = {**base_features, "ambient_temperature": t, "avg_current": c,
                 "min_voltage": v, "voltage_range": 4.19 - v,
                 "avg_temp": t + 8.0}
         result = registry_v2.predict(feat, req.model_name)
-        rul = result.get("rul_cycles", 0) or 0
-        candidates.append((rul, t, c, v, result["soh_pct"]))
-
-    candidates.sort(reverse=True)
+        rul = max(0.0, float(result.get("rul_cycles", 0) or 0))
+        adjusted_rul = rul - guardrail_penalty(t, c, v)
+        improvement = adjusted_rul - baseline_adjusted_rul
+        candidates.append({
+            "raw_rul": rul,
+            "adjusted_rul": adjusted_rul,
+            "improvement": improvement,
+            "temp": t,
+            "current": c,
+            "cutoff": v,
+        })
+
+    candidates.sort(
+        key=lambda x: (
+            x["improvement"] > 0,
+            x["improvement"],
+            x["adjusted_rul"],
+            -abs(x["temp"] - 24.0),
+            -x["current"],
+        ),
+        reverse=True,
+    )
     top = candidates[: req.top_k]
 
     recs = []
-    for rank, (rul, t, c, v, soh) in enumerate(top, 1):
-        improvement = rul - baseline_rul
+    for rank, rec in enumerate(top, 1):
+        rul = rec["raw_rul"]
+        t = rec["temp"]
+        c = rec["current"]
+        v = rec["cutoff"]
+        improvement = rec["improvement"]
         pct = (improvement / baseline_rul * 100) if baseline_rul > 0 else 0
+        impact = "improves" if improvement > 0 else "does not improve"
         recs.append(SingleRecommendation(
             rank=rank,
             ambient_temperature=t,
@@ -134,7 +177,10 @@ async def recommend_v2(req: RecommendationRequest):
             predicted_rul=rul,
             rul_improvement=improvement,
             rul_improvement_pct=round(pct, 1),
-            explanation=f"Operate at {t}°C, {c}A, cutoff {v}V for ~{rul:.0f} cycles RUL",
+            explanation=(
+                f"Operate at {t}°C, {c}A, cutoff {v}V for ~{rul:.0f} cycles RUL; "
+                f"this {impact} lifespan by {improvement:+.0f} cycles vs your baseline."
+            ),
         ))
 
     return RecommendationResponse(

api/routers/predict_v3.py

@@ -73,20 +73,17 @@ async def predict_batch_v3(req: BatchPredictRequest):
 # ── Recommendations (v3) ─────────────────────────────────────────────────────
 @router.post("/recommend", response_model=RecommendationResponse)
 async def recommend_v3(req: RecommendationRequest):
-    """Get operational recommendations using v3 models."""
+    """Get operational recommendations using v3 models.
+
+    Ranking is based on net RUL improvement versus a model-derived baseline,
+    with small guardrail penalties for clearly harsher operating conditions.
+    """
     import itertools
 
     temps = [4.0, 24.0, 43.0]
     currents = [0.5, 1.0, 2.0, 4.0]
     cutoffs = [2.0, 2.2, 2.5, 2.7]
 
-    EOL_THRESHOLD = 70.0
-    deg_rate = 0.2
-    if req.current_soh > EOL_THRESHOLD:
-        baseline_rul = (req.current_soh - EOL_THRESHOLD) / deg_rate
-    else:
-        baseline_rul = 0.0
-
     base_features = {
         "cycle_number": req.current_cycle,
         "ambient_temperature": req.ambient_temperature,
@@ -102,22 +99,72 @@ async def recommend_v3(req: RecommendationRequest):
         "delta_capacity": -0.005,
     }
 
+    def guardrail_penalty(temp: float, current: float, cutoff: float) -> float:
+        """Penalty in cycle-units for stress-heavy operating points.
+
+        This keeps recommendations aligned with battery-care intent even when
+        model outputs are noisy for out-of-distribution combinations.
+        """
+        temp_penalty = max(0.0, temp - 30.0) * 3.0 + max(0.0, 12.0 - temp) * 1.5
+        current_penalty = max(0.0, current - 1.5) * 12.0
+        cutoff_penalty = max(0.0, 2.4 - cutoff) * 8.0
+        return temp_penalty + current_penalty + cutoff_penalty
+
+    baseline_features = {
+        **base_features,
+        "ambient_temperature": req.ambient_temperature,
+        "avg_current": 1.82,
+        "min_voltage": 2.61,
+        "voltage_range": 4.19 - 2.61,
+        "avg_temp": req.ambient_temperature + 8.0,
+    }
+    baseline_pred = registry_v3.predict(baseline_features, req.model_name)
+    baseline_rul = max(0.0, float(baseline_pred.get("rul_cycles", 0) or 0))
+    baseline_adjusted_rul = baseline_rul - guardrail_penalty(
+        req.ambient_temperature,
+        1.82,
+        2.61,
+    )
+
     candidates = []
     for t, c, v in itertools.product(temps, currents, cutoffs):
         feat = {**base_features, "ambient_temperature": t, "avg_current": c,
                 "min_voltage": v, "voltage_range": 4.19 - v,
                 "avg_temp": t + 8.0}
         result = registry_v3.predict(feat, req.model_name)
-        rul = result.get("rul_cycles", 0) or 0
-        candidates.append((rul, t, c, v, result["soh_pct"]))
-
-    candidates.sort(reverse=True)
+        rul = max(0.0, float(result.get("rul_cycles", 0) or 0))
+        adjusted_rul = rul - guardrail_penalty(t, c, v)
+        improvement = adjusted_rul - baseline_adjusted_rul
+        candidates.append({
+            "raw_rul": rul,
+            "adjusted_rul": adjusted_rul,
+            "improvement": improvement,
+            "temp": t,
+            "current": c,
+            "cutoff": v,
+        })
+
+    candidates.sort(
+        key=lambda x: (
+            x["improvement"] > 0,
+            x["improvement"],
+            x["adjusted_rul"],
+            -abs(x["temp"] - 24.0),
+            -x["current"],
+        ),
+        reverse=True,
+    )
     top = candidates[: req.top_k]
 
     recs = []
-    for rank, (rul, t, c, v, soh) in enumerate(top, 1):
-        improvement = rul - baseline_rul
+    for rank, rec in enumerate(top, 1):
+        rul = rec["raw_rul"]
+        t = rec["temp"]
+        c = rec["current"]
+        v = rec["cutoff"]
+        improvement = rec["improvement"]
         pct = (improvement / baseline_rul * 100) if baseline_rul > 0 else 0
+        impact = "improves" if improvement > 0 else "does not improve"
         recs.append(SingleRecommendation(
             rank=rank,
             ambient_temperature=t,
@@ -126,7 +173,10 @@ async def recommend_v3(req: RecommendationRequest):
             predicted_rul=rul,
             rul_improvement=improvement,
             rul_improvement_pct=round(pct, 1),
-            explanation=f"Operate at {t}°C, {c}A, cutoff {v}V for ~{rul:.0f} cycles RUL",
+            explanation=(
+                f"Operate at {t}°C, {c}A, cutoff {v}V for ~{rul:.0f} cycles RUL; "
+                f"this {impact} lifespan by {improvement:+.0f} cycles vs your baseline."
+            ),
         ))
 
     return RecommendationResponse(

frontend/src/App.tsx

@@ -81,7 +81,12 @@ export default function App() {
         {activeTab === "simulation" && <SimulationPanel />}
         {activeTab === "predict" && <PredictionForm apiVersion={apiVersion} />}
         {activeTab === "graphs" && <GraphPanel />}
-        {activeTab === "recommend" && <RecommendationPanel apiVersion={apiVersion} />}
+        {activeTab === "recommend" && (
+          <RecommendationPanel
+            apiVersion={apiVersion}
+            onApiVersionChange={handleVersionChange}
+          />
+        )}
         {activeTab === "metrics" && <MetricsPanel apiVersion={apiVersion} />}
         {activeTab === "paper" && <ResearchPaper />}
       </main>

frontend/src/components/RecommendationPanel.tsx

@@ -12,7 +12,7 @@ import {
   Trophy, Award, Medal, BarChart2, GitCompare, RefreshCcw, ChevronDown,
   ChevronUp, Info, AlertTriangle, CheckCircle2, Sliders,
 } from "lucide-react";
-import { fetchRecommendations, fetchModels, RecommendationResponse, ModelInfo } from "../api";
+import { fetchRecommendations, fetchModels, RecommendationResponse, ModelInfo, setApiVersion } from "../api";
 
 const CHART_COLORS = [
   "#22c55e", "#3b82f6", "#f59e0b", "#ef4444", "#8b5cf6",
@@ -51,6 +51,7 @@ function SliderInput({ label, value, min, max, step, unit, onChange }: {
 
 type Props = {
   apiVersion: "v1" | "v2" | "v3";
+  onApiVersionChange?: (v: "v1" | "v2" | "v3") => void;
 };
 
 function Battery3D({ currentSoh, projectedSoh }: { currentSoh: number; projectedSoh: number }) {
@@ -87,7 +88,8 @@ function Battery3D({ currentSoh, projectedSoh }: { currentSoh: number; projected
   );
 }
 
-export default function RecommendationPanel({ apiVersion }: Props) {
+export default function RecommendationPanel({ apiVersion, onApiVersionChange }: Props) {
+  const [selectedApiVersion, setSelectedApiVersion] = useState<"v1" | "v2" | "v3">(apiVersion);
   const [batteryId, setBatteryId] = useState("B0005");
   const [currentCycle, setCurrentCycle] = useState(100);
   const [currentSoh, setCurrentSoh] = useState(85);
@@ -102,12 +104,30 @@ export default function RecommendationPanel({ apiVersion }: Props) {
   const [selectedRank, setSelectedRank] = useState<number>(1);
   const [chartTab, setChartTab] = useState<"rul" | "params" | "radar">("rul");
 
+  useEffect(() => {
+    setSelectedApiVersion(apiVersion);
+  }, [apiVersion]);
+
   // Fetch available loaded models for selector
   useEffect(() => {
+    setApiVersion(selectedApiVersion);
     fetchModels()
-      .then((m) => setModels(m.filter((mo) => mo.loaded)))
+      .then((m) => {
+        const loaded = m.filter((mo) => mo.loaded);
+        setModels(loaded);
+        setSelectedModel((prev) => (prev && loaded.some((mo) => mo.name === prev) ? prev : null));
+      })
       .catch(() => setModels([]));
-  }, [apiVersion]);
+  }, [selectedApiVersion]);
+
+  const handleVersionChange = (v: "v1" | "v2" | "v3") => {
+    setSelectedApiVersion(v);
+    setApiVersion(v);
+    onApiVersionChange?.(v);
+    setSelectedModel(null);
+    setResult(null);
+    setError(null);
+  };
 
   const handleSubmit = async () => {
     setLoading(true);
@@ -208,22 +228,41 @@ export default function RecommendationPanel({ apiVersion }: Props) {
               </div>
             </div>
             {/* Model selector */}
-            <div>
-              <label className="block text-xs text-gray-400 mb-1">Model</label>
-              <select
-                value={selectedModel ?? ""}
-                onChange={(e) => setSelectedModel(e.target.value || null)}
-                className="w-full bg-gray-800 border border-gray-700 rounded-lg px-3 py-2 text-sm text-white"
-              >
-                <option value="">Default (best available)</option>
-                {models.map((m) => (
-                  <option key={m.name} value={m.name}>
-                    {m.display_name ?? m.name}
-                    {m.r2 != null ? ` (R²=${m.r2.toFixed(3)})` : ""}
-                  </option>
-                ))}
-              </select>
+            <div className="grid grid-cols-1 md:grid-cols-2 gap-3">
+              <div>
+                <label className="block text-xs text-gray-400 mb-1">Version</label>
+                <select
+                  value={selectedApiVersion}
+                  onChange={(e) => handleVersionChange(e.target.value as "v1" | "v2" | "v3")}
+                  className="w-full bg-gray-800 border border-gray-700 rounded-lg px-3 py-2 text-sm text-white"
+                >
+                  <option value="v1">v1</option>
+                  <option value="v2">v2</option>
+                  <option value="v3">v3</option>
+                </select>
+              </div>
+              <div>
+                <label className="block text-xs text-gray-400 mb-1">Model</label>
+                <select
+                  value={selectedModel ?? ""}
+                  onChange={(e) => setSelectedModel(e.target.value || null)}
+                  className="w-full bg-gray-800 border border-gray-700 rounded-lg px-3 py-2 text-sm text-white"
+                >
+                  <option value="">Default (best available)</option>
+                  {models.map((m) => (
+                    <option key={m.name} value={m.name}>
+                      {m.display_name ?? m.name}
+                      {m.r2 != null ? ` (R²=${m.r2.toFixed(3)})` : ""}
+                    </option>
+                  ))}
+                </select>
+              </div>
             </div>
+            {models.length === 0 && (
+              <p className="text-xs text-amber-400">
+                No loaded models found for {selectedApiVersion}. Using default endpoint behavior.
+              </p>
+            )}
           </div>
           {/* Sliders */}
           <div className="space-y-4">