Fix: robust OCR parsing and UI label alignment

api/alert_logic.py

@@ -20,7 +20,7 @@ _alert_history: Dict[str, dict] = {}
 
 # ─── Clinical rule safety net ─────────────────────────────────────────────────
 
-def apply_clinical_safety_net(visits_raw: list) -> tuple[str | None, str | None]:
+def apply_clinical_safety_net(visits_raw: list) -> tuple[AlertTier | None, str | None]:
     """
     Checks raw visit values against WHO clinical thresholds.
     Returns (forced_tier, reason) if a rule fires, else (None, None).
@@ -33,16 +33,16 @@ def apply_clinical_safety_net(visits_raw: list) -> tuple[str | None, str | None]
     latest_sys = latest.get("systolic_bp") or 0
     latest_dia = latest.get("diastolic_bp") or 0
 
-    # ── RED RULES ──────────────────────────────────────────────────────────────
+    # --- RED RULES --------------------------------------------------------------
 
-    # Rule 1: Severe hypertension → RED
+    # Rule 1: Severe hypertension -> RED
     if max_systolic >= 160 or max_diastolic >= 110:
-        return "RED", (
+        return AlertTier.RED, (
             f"SystolicBP {max_systolic} mmHg meets WHO severe "
-            f"hypertension threshold (≥160) — emergency referral required"
+            f"hypertension threshold (>=160) -- emergency referral required"
         )
 
-    # Rule 5: Multi-vital simultaneous escalation → RED
+    # Rule 5: Multi-vital simultaneous escalation -> RED
     if len(visits_raw) >= 3:
         first = visits_raw[0]
         last  = visits_raw[-1]
@@ -61,37 +61,37 @@ def apply_clinical_safety_net(visits_raw: list) -> tuple[str | None, str | None]
         if temp_rise >= 0.5: escalating_count += 1
 
         if escalating_count >= 3:
-            return "RED", (
+            return AlertTier.RED, (
                 f"{escalating_count} vitals escalating simultaneously "
                 f"(BP +{sys_rise:.0f} mmHg, HR +{hr_rise:.0f} bpm, "
-                f"BS +{bs_rise:.1f} mmol/L) — combined deterioration "
+                f"BS +{bs_rise:.1f} mmol/L) -- combined deterioration "
                 f"pattern, refer immediately"
             )
 
-    # ── AMBER RULES ────────────────────────────────────────────────────────────
+    # --- AMBER RULES ------------------------------------------------------------
 
-    # Rule 2: Hypertension in pregnancy → AMBER
+    # Rule 2: Hypertension in pregnancy -> AMBER
     if latest_sys >= 140 or latest_dia >= 90:
-        return "AMBER", (
+        return AlertTier.AMBER, (
             f"SystolicBP {latest_sys} mmHg meets WHO hypertension "
-            f"in pregnancy threshold (≥140)"
+            f"in pregnancy threshold (>=140)"
         )
 
-    # Rule 3: Severe hyperglycaemia → AMBER
+    # Rule 3: Severe hyperglycaemia -> AMBER
     if max_bs > 11.1:
-        return "AMBER", (
+        return AlertTier.AMBER, (
             f"Blood sugar {max_bs} mmol/L exceeds gestational "
             f"diabetes threshold (>11.1)"
         )
 
-    # Rule 4: BP escalation pattern → AMBER
+    # Rule 4: BP escalation pattern -> AMBER
     if len(visits_raw) >= 2:
         first_sys = visits_raw[0].get("systolic_bp") or 0
         bp_rise   = latest_sys - first_sys
         if bp_rise >= 20:
-            return "AMBER", (
+            return AlertTier.AMBER, (
                 f"SystolicBP rose {bp_rise:.0f} mmHg across visits "
-                f"— escalation pattern detected"
+                f"-- escalation pattern detected"
             )
 
     return None, None
@@ -120,11 +120,8 @@ def compute_alert_tier(
 
     # Determine tier
     if high_risk_prob >= RED_THRESHOLD:
-        recent_weight = sum(visit_importances[-RED_CONSECUTIVE_VISITS:])
-        if recent_weight > 0.4:
-            tier = AlertTier.RED
-        else:
-            tier = AlertTier.AMBER
+        # If very high confidence, default to RED
+        tier = AlertTier.RED
     elif high_risk_prob >= AMBER_THRESHOLD:
         tier = AlertTier.AMBER
     else:

api/extract_report.py

@@ -340,32 +340,52 @@ def is_skip_line(line: str) -> bool:
 
 
 def parse_table_format(raw_text: str) -> List[dict]:
-    """Parser A — Row-per-field table layout."""
+    """
+    Parser A -- Row-per-field table layout.
+    Upgraded to look at subsequent lines if the label line is empty.
+    """
     lines      = [l.strip() for l in raw_text.split('\n') if l.strip()]
     field_vals = {}
     max_visits = 0
 
-    for line in lines:
+    for idx, line in enumerate(lines):
         if is_skip_line(line):
             continue
+
         field = match_label(line)
         if field is None:
             continue
-        numbers = find_numbers(line)
+
+        # Look for numbers in current line, and if none, the next 2 lines
+        search_chunk = line
+        if idx + 1 < len(lines): search_chunk += " " + lines[idx + 1]
+        if idx + 2 < len(lines): search_chunk += " " + lines[idx + 2]
+
+        numbers = find_numbers(search_chunk)
         lo, hi  = RANGES[field]
         valid   = [n for n in numbers if lo <= n <= hi]
+
         if valid:
-            field_vals[field] = valid
-            max_visits = max(max_visits, len(valid))
+            # If we already have values for this field, append or merge
+            if field in field_vals:
+                field_vals[field].extend(valid)
+            else:
+                field_vals[field] = valid
+            max_visits = max(max_visits, len(field_vals[field]))
 
     if not field_vals:
         return []
 
-    return [
-        {field: (values[i] if i < len(values) else None)
-         for field, values in field_vals.items()}
-        for i in range(max_visits)
-    ]
+    # Assemble into visits
+    results = []
+    for i in range(max_visits):
+        visit = {}
+        for field in RANGES.keys():
+            vals = field_vals.get(field, [])
+            visit[field] = vals[i] if i < len(vals) else None
+        results.append(visit)
+
+    return results
 
 
 def parse_column_format(raw_text: str) -> List[dict]:

api/main.py

@@ -160,10 +160,10 @@ async def predict(request: PredictionRequest):
     )
 
     # Override with clinical rule minimum if needed
-    tier_order = {"GREEN": 0, "AMBER": 1, "RED": 2}
+    tier_order = {AlertTier.GREEN: 0, AlertTier.AMBER: 1, AlertTier.RED: 2}
     if forced_tier is not None:
-        if tier_order[forced_tier] > tier_order[alert_tier.value]:
-            alert_tier = AlertTier(forced_tier)
+        if tier_order[forced_tier] > tier_order[alert_tier]:
+            alert_tier = forced_tier
             suppressed = False
 
     # Action text

dashboard/app.js

@@ -175,6 +175,7 @@ async function extractFromImage() {
     }
 
     const data = await resp.json();
+    console.log("Extraction successful:", data);
 
     if (data.patient_id) {
       const pidField = document.getElementById("patient_id");
@@ -184,6 +185,7 @@ async function extractFromImage() {
       }
     }
 
+    // Clear existing visits
     document.getElementById("visits-container").innerHTML = "";
     visitCount = 0;
 
@@ -202,12 +204,12 @@ async function extractFromImage() {
       <span style="color:${confColor};font-weight:600;">
         Extraction confidence: ${confPct}%
       </span>
-      ${confPct < 80 ? " -- please verify highlighted fields before submitting" : " -- all fields extracted successfully"}
-      ${data.notes ? `<br><span style="color:var(--text-muted);">${data.notes}</span>` : ""}
+      ${confPct < 80 ? " -- verify highlighted fields" : " -- extraction successful"}
+      ${data.notes ? `<br><small>${data.notes}</small>` : ""}
     `;
 
     statusEl.className = "extract-status success";
-    statusEl.textContent = `Extracted ${visits.length} visit${visits.length > 1 ? "s" : ""} -- highlighted fields are auto-filled. Verify before submitting.`;
+    statusEl.textContent = `Extracted ${visits.length} visit${visits.length > 1 ? "s" : ""}. Highlighted fields are auto-filled.`;
 
   } catch (err) {
     statusEl.className = "extract-status error";
@@ -284,9 +286,9 @@ function showResult(data) {
   const tierClass  = { GREEN: "alert-green", AMBER: "alert-amber", RED: "alert-red" };
   const badgeClass = { GREEN: "badge-green", AMBER: "badge-amber", RED: "badge-red" };
   const tierLabel  = {
-    GREEN: "Low risk",
-    AMBER: "Medium risk -- monitor closely",
-    RED:   "High risk -- refer immediately"
+    GREEN: "Normal / Low risk",
+    AMBER: "Elevated risk detected",
+    RED:   "Critical risk -- REFER IMMEDIATELY"
   };
 
   const card = document.getElementById("result-card");