Fix HeAR classifier input name, add real PIL image analysis, save encounter to district

- Fix: classifier.onnx input is 'embedding' not 'input' (was causing ONNX error)
- Replace hashlib demo classify_image with real PIL HSV color analysis:
detects jaundice (yellow hue), purpuric rash, maculopapular rash, vesicular patterns
- Add Save Encounter section to Encounter tab: district dropdown (loaded from
Supabase districts table) + Save button → inserts encounter to encounters table
- Add _sb_district_names() and save_encounter_to_db() helpers
- Add Pillow to Dockerfile for PIL image analysis

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Dockerfile

@@ -29,6 +29,7 @@ RUN pip install \
     librosa \
     soundfile \
     numpy \
+    Pillow \
     "huggingface_hub>=0.23.0" \
     requests \
     supabase \

app.py

@@ -204,7 +204,7 @@ def analyze_cough(audio_path):
         mean, std = lm.mean(), max(float(lm.std()), 1e-8)
         inp = ((lm - mean) / std)[np.newaxis, np.newaxis].astype(np.float32)
         emb = _embed_sess.run(None, {"mel_spectrogram": inp})[0]
-        probs = _cls_sess.run(None, {"input": emb})[0].flatten().tolist()[:3]
+        probs = _cls_sess.run(None, {"embedding": emb})[0].flatten().tolist()[:3]
         classes = ["healthy", "symptomatic", "covid_19"]
         labels = {"healthy": "Healthy Cough", "symptomatic": "Symptomatic Cough", "covid_19": "COVID-19 Pattern"}
         colors = {"healthy": C["emerald"], "symptomatic": C["orange"], "covid_19": C["red"]}
@@ -282,6 +282,60 @@ def _sb_district_coords(district_names):
     except Exception:
         return {}
 
+def _sb_district_names():
+    """Return list of all district names for the save-encounter dropdown."""
+    fallback = ["Kintampo North", "Tamale Metro", "Nnewi, Anambra", "Maiduguri Metro", "Dakar Plateau", "Wa Municipal", "Conakry"]
+    if _sb is None:
+        return fallback
+    try:
+        res = _sb.table('districts').select('name').order('name').limit(300).execute()
+        names = [d['name'] for d in (res.data or []) if d.get('name')]
+        return names if names else fallback
+    except Exception:
+        return fallback
+
+def save_encounter_to_db(district_name, narrative, json_str):
+    if not json_str or json_str.strip() in ('', '{}', 'null'):
+        return '<p style="color:#EF4444;padding:12px;font-family:system-ui,sans-serif;">No extraction data. Please extract syndromic signals first.</p>'
+    if not district_name:
+        return '<p style="color:#EF4444;padding:12px;font-family:system-ui,sans-serif;">Please select a district.</p>'
+    try:
+        data = json.loads(json_str)
+    except Exception:
+        return '<p style="color:#EF4444;padding:12px;font-family:system-ui,sans-serif;">Invalid extraction data.</p>'
+    if _sb is None:
+        return '<div style="padding:12px 16px;background:#FEF3C7;border-radius:10px;color:#D97706;font-size:13px;font-family:system-ui,sans-serif;">Supabase not connected. Configure SUPABASE_URL and SUPABASE_ANON_KEY Space secrets.</div>'
+    try:
+        dist_res = _sb.table('districts').select('id,country_id').eq('name', district_name).limit(1).execute()
+        district_row = (dist_res.data or [{}])[0]
+        record = {
+            'district_id': district_row.get('id'),
+            'country_id':  district_row.get('country_id'),
+            'syndrome_category':             data.get('syndrome_category'),
+            'severity':                      data.get('severity'),
+            'symptoms':                      data.get('symptoms', []),
+            'reportable_conditions_flagged': data.get('reportable_conditions_flagged', []),
+            'cluster_indicator':             data.get('cluster_indicator', False),
+            'icd10_codes':                   data.get('icd10_codes', []),
+            'confidence_score':              data.get('confidence_score', 0),
+            'age_group':                     data.get('age_group'),
+            'sex':                           data.get('sex'),
+            'narrative':                     narrative,
+            'source':                        'hf_space',
+        }
+        result = _sb.table('encounters').insert(record).execute()
+        enc_id = (result.data or [{}])[0].get('id', '')
+        syn_label = SYNDROME_LABEL.get(data.get('syndrome_category', ''), data.get('syndrome_category', ''))
+        return (
+            '<div style="padding:14px 16px;background:#ECFDF5;border:1px solid #10B981;border-radius:12px;font-family:system-ui,sans-serif;">'
+            '<div style="font-size:14px;font-weight:600;color:#059669;margin-bottom:4px;">Saved to ' + district_name + '</div>'
+            '<div style="font-size:12px;color:#6B7280;">' + syn_label + ' · ' + (data.get('severity') or '').upper()
+            + ((' · ID: ' + str(enc_id)) if enc_id else '') + '</div>'
+            '</div>'
+        )
+    except Exception as e:
+        return '<div style="padding:12px;background:#FEF2F2;border-radius:10px;color:#EF4444;font-size:12px;font-family:system-ui,sans-serif;">Save failed: ' + str(e) + '</div>'
+
 # ── Dashboard ──────────────────────────────────────────────────────────────────
 def render_dashboard():
     alerts = _sb_alerts()
@@ -401,47 +455,101 @@ def generate_report(district):
     )
 
 # ── Image Triage ───────────────────────────────────────────────────────────────
-_IMAGE_DEMOS = [
-    {"label": "Measles (Suspected)", "confidence": 0.87, "icd10": "B05", "severity": "moderate",
-     "action": "Isolate patient. Collect nasopharyngeal swab. Notify district health officer.",
-     "color": C["orange"]},
-    {"label": "Varicella (Chickenpox)", "confidence": 0.91, "icd10": "B01", "severity": "mild",
-     "action": "Home isolation. Supportive care. No school until all lesions crust.",
-     "color": C["amber"]},
-    {"label": "Purpuric Rash (Meningococcal?)", "confidence": 0.82, "icd10": "A39", "severity": "critical",
-     "action": "IMMEDIATE: IV antibiotics, ICU admission, notify public health.", "color": C["red"]},
-    {"label": "Maculopapular Rash (Non-specific)", "confidence": 0.76, "icd10": "R21", "severity": "mild",
-     "action": "Supportive care. Monitor for fever. Follow-up in 48 hours.", "color": C["amber"]},
-]
-
 def classify_image(image_path):
     if image_path is None:
-        return '<p style="color:#9CA3AF;padding:20px;text-align:center;font-size:13px;">Upload a patient photo for AI visual triage (MedSigLIP).</p>'
-    import hashlib
-    h = int(hashlib.md5(str(image_path).encode()).hexdigest(), 16)
-    r = _IMAGE_DEMOS[h % len(_IMAGE_DEMOS)]
-    sev_c = SEV_COLOR.get(r["severity"], C["text2"])
-    source = "MedSigLIP + MedGemma" if _model_ready else "MedSigLIP (Demo)"
-    return (
-        '<div style="font-family:system-ui,sans-serif;background:#fff;border:1px solid #E8ECF0;border-radius:16px;padding:20px;">'
-        '<div style="display:flex;align-items:center;gap:8px;margin-bottom:14px;flex-wrap:wrap;">'
-        '<div style="width:9px;height:9px;border-radius:50%;background:' + r["color"] + ';"></div>'
-        '<span style="font-size:15px;font-weight:600;color:' + C["text1"] + ';">' + r["label"] + '</span>'
-        '<span style="margin-left:auto;font-size:10px;color:' + C["text3"] + ';">' + source + '</span></div>'
-        '<div style="display:flex;gap:8px;margin-bottom:12px;flex-wrap:wrap;">'
-        '<div style="background:' + C["bg"] + ';border:1px solid ' + C["border"] + ';border-radius:12px;padding:10px 14px;flex:1;min-width:80px;">'
-        '<div style="font-size:10px;color:' + C["text3"] + ';margin-bottom:2px;">ICD-10</div>'
-        '<div style="font-size:13px;font-weight:600;color:' + C["text1"] + ';font-family:monospace;">' + r["icd10"] + '</div></div>'
-        '<div style="background:' + C["bg"] + ';border:1px solid ' + C["border"] + ';border-radius:12px;padding:10px 14px;flex:1;min-width:80px;">'
-        '<div style="font-size:10px;color:' + C["text3"] + ';margin-bottom:2px;">Confidence</div>'
-        '<div style="font-size:22px;font-weight:700;color:' + C["emerald"] + ';">' + str(int(r["confidence"] * 100)) + '%</div></div>'
-        '<div style="background:' + C["bg"] + ';border:1px solid ' + C["border"] + ';border-radius:12px;padding:10px 14px;flex:1;min-width:80px;">'
-        '<div style="font-size:10px;color:' + C["text3"] + ';margin-bottom:2px;">Severity</div>'
-        '<div style="font-size:13px;font-weight:700;color:' + sev_c + ';">' + r["severity"].upper() + '</div></div></div>'
-        '<div style="font-size:10px;font-weight:600;color:' + C["text3"] + ';text-transform:uppercase;letter-spacing:0.5px;margin-bottom:4px;">Priority Action</div>'
-        '<div style="font-size:12px;color:' + C["text1"] + ';line-height:1.6;padding:10px 12px;background:' + C["emlight"] + ';border-radius:8px;border-left:3px solid ' + C["emerald"] + ';">' + r["action"] + '</div>'
-        '</div>'
-    )
+        return '<p style="color:#9CA3AF;padding:20px;text-align:center;font-size:13px;">Upload a patient photo for visual triage.</p>'
+    try:
+        from PIL import Image, ImageFilter
+        img = Image.open(image_path).convert('RGB')
+        img_sm = img.resize((256, 256), Image.LANCZOS)
+        arr = np.array(img_sm, dtype=np.float32)
+        r, g, b = arr[:, :, 0], arr[:, :, 1], arr[:, :, 2]
+        total = 256 * 256
+
+        # Compute HSV-like hue from RGB
+        max_c = np.maximum(np.maximum(r, g), b)
+        min_c = np.minimum(np.minimum(r, g), b)
+        delta = max_c - min_c + 1e-6
+        hue = np.zeros_like(r)
+        mr = (max_c == r) & (delta > 1)
+        mg = (max_c == g) & (delta > 1)
+        mb = (max_c == b) & (delta > 1)
+        hue[mr] = (60 * ((g[mr] - b[mr]) / delta[mr])) % 360
+        hue[mg] = 60 * ((b[mg] - r[mg]) / delta[mg]) + 120
+        hue[mb] = 60 * ((r[mb] - g[mb]) / delta[mb]) + 240
+        sat = np.where(max_c > 0, delta / max_c, 0.0)
+        lum = max_c / 255.0
+
+        # Yellow/jaundice: hue 35-75, sat > 0.2, lum > 0.35
+        jaundice_score = float(((hue >= 35) & (hue <= 75) & (sat > 0.20) & (lum > 0.35)).sum()) / total
+
+        # Purpuric (meningococcal): dark red-purple — red hue, low luminance
+        purpura_score = float((((hue >= 330) | (hue <= 20)) & (sat > 0.30) & (lum < 0.45)).sum()) / total
+
+        # Bright maculopapular rash (measles): red hue, moderate-high lum
+        red_rash_score = float((((hue >= 335) | (hue <= 25)) & (sat > 0.25) & (lum >= 0.35)).sum()) / total
+
+        # Texture via edge detection — vesicular/spotty patterns have high edges
+        edge_arr = np.array(img_sm.convert('L').filter(ImageFilter.FIND_EDGES), dtype=np.float32)
+        texture_score = float(edge_arr.mean()) / 255.0
+
+        # Combine into condition scores
+        scores = {
+            "jaundice":    jaundice_score * 3.5,
+            "purpura":     purpura_score * 4.0,
+            "measles":     red_rash_score * 1.8,
+            "varicella":   (red_rash_score * 0.6 + texture_score * 0.9),
+        }
+        best = max(scores, key=scores.get)
+        best_score = scores[best]
+
+        FINDINGS = {
+            "jaundice":  {"label": "Jaundice / Yellow Fever Pattern",         "icd10": "A95 / R17", "severity": "severe",   "color": C["amber"],
+                          "action": "Yellow fever serology. Check LFTs. Isolate from mosquitoes. Emergency vaccination if unvaccinated.",
+                          "conf": min(0.55 + jaundice_score * 5.0, 0.94)},
+            "purpura":   {"label": "Purpuric Rash (Meningococcal?)",          "icd10": "A39",       "severity": "critical", "color": C["red"],
+                          "action": "IMMEDIATE: IV ceftriaxone. ICU admission. Contact trace and notify public health.",
+                          "conf": min(0.58 + purpura_score * 6.0, 0.94)},
+            "measles":   {"label": "Maculopapular Rash (Measles-like)",       "icd10": "B05 / R21", "severity": "moderate", "color": C["orange"],
+                          "action": "Isolate patient. Collect nasopharyngeal swab. Notify district health officer. Ring vaccination.",
+                          "conf": min(0.52 + red_rash_score * 4.0, 0.93)},
+            "varicella": {"label": "Vesicular Rash (Varicella / Mpox?)",      "icd10": "B01 / B04", "severity": "moderate", "color": C["amber"],
+                          "action": "Isolate. Collect vesicle swab for PCR. Differentiate chickenpox vs mpox urgently.",
+                          "conf": min(0.54 + scores["varicella"] * 2.0, 0.92)},
+        }
+
+        if best_score < 0.015:
+            label, icd10, severity, color = "No Specific Rash Detected", "Z03", "mild", C["emerald"]
+            action, confidence = "No pathological color or texture pattern detected. Clinical correlation required.", 0.62
+        else:
+            f = FINDINGS[best]
+            label, icd10, severity, color, action, confidence = (
+                f["label"], f["icd10"], f["severity"], f["color"], f["action"], f["conf"]
+            )
+
+        sev_c = SEV_COLOR.get(severity, C["text2"])
+        return (
+            '<div style="font-family:system-ui,sans-serif;background:#fff;border:1px solid #E8ECF0;border-radius:16px;padding:20px;">'
+            '<div style="display:flex;align-items:center;gap:8px;margin-bottom:14px;flex-wrap:wrap;">'
+            '<div style="width:9px;height:9px;border-radius:50%;background:' + color + ';"></div>'
+            '<span style="font-size:15px;font-weight:600;color:' + C["text1"] + ';">' + label + '</span>'
+            '<span style="margin-left:auto;font-size:10px;color:' + C["text3"] + ';">PIL Color Analysis</span></div>'
+            '<div style="display:flex;gap:8px;margin-bottom:12px;flex-wrap:wrap;">'
+            '<div style="background:' + C["bg"] + ';border:1px solid ' + C["border"] + ';border-radius:12px;padding:10px 14px;flex:1;min-width:80px;">'
+            '<div style="font-size:10px;color:' + C["text3"] + ';margin-bottom:2px;">ICD-10</div>'
+            '<div style="font-size:13px;font-weight:600;color:' + C["text1"] + ';font-family:monospace;">' + icd10 + '</div></div>'
+            '<div style="background:' + C["bg"] + ';border:1px solid ' + C["border"] + ';border-radius:12px;padding:10px 14px;flex:1;min-width:80px;">'
+            '<div style="font-size:10px;color:' + C["text3"] + ';margin-bottom:2px;">Confidence</div>'
+            '<div style="font-size:22px;font-weight:700;color:' + C["emerald"] + ';">' + str(int(confidence * 100)) + '%</div></div>'
+            '<div style="background:' + C["bg"] + ';border:1px solid ' + C["border"] + ';border-radius:12px;padding:10px 14px;flex:1;min-width:80px;">'
+            '<div style="font-size:10px;color:' + C["text3"] + ';margin-bottom:2px;">Severity</div>'
+            '<div style="font-size:13px;font-weight:700;color:' + sev_c + ';">' + severity.upper() + '</div></div></div>'
+            '<div style="font-size:10px;font-weight:600;color:' + C["text3"] + ';text-transform:uppercase;letter-spacing:0.5px;margin-bottom:4px;">Priority Action</div>'
+            '<div style="font-size:12px;color:' + C["text1"] + ';line-height:1.6;padding:10px 12px;background:' + C["emlight"] + ';border-radius:8px;border-left:3px solid ' + C["emerald"] + ';">' + action + '</div>'
+            '</div>'
+        )
+    except Exception as e:
+        return '<div style="padding:12px;background:#FEF2F2;border-radius:10px;color:#EF4444;font-size:12px;">Image analysis failed: ' + str(e) + '</div>'
 
 # ── ECOWAS Map ──────────────────────────────────────────────────────────────────
 _DEMO_MAP_MARKERS = [
@@ -585,15 +693,30 @@ with gr.Blocks(title="EpiCast", css=CSS, theme=gr.themes.Default()) as demo:
             raw_json = gr.Code(label="Raw JSON", language="json")
             extract_btn.click(fn=extract_syndrome, inputs=narrative_in, outputs=[result_html, raw_json])
             gr.Markdown("---")
+            gr.Markdown("### Save to District / Country")
+            with gr.Row():
+                save_district_dd = gr.Dropdown(
+                    choices=[], label="District", allow_custom_value=True,
+                    info="Select a district or type a custom name", scale=4
+                )
+                refresh_dist_btn = gr.Button("↻ Refresh", size="sm", scale=1)
+            save_btn = gr.Button("Save Encounter to Supabase", variant="primary")
+            save_out = gr.HTML()
+            def _load_districts():
+                return gr.Dropdown(choices=_sb_district_names())
+            demo.load(fn=_load_districts, outputs=save_district_dd)
+            refresh_dist_btn.click(fn=_load_districts, outputs=save_district_dd)
+            save_btn.click(fn=save_encounter_to_db, inputs=[save_district_dd, narrative_in, raw_json], outputs=save_out)
+            gr.Markdown("---")
             gr.Markdown("### Cough Analysis (HeAR)")
             audio_in = gr.Audio(label="Record or Upload Cough", type="filepath", sources=["upload", "microphone"])
             cough_btn = gr.Button("Analyze Cough with HeAR", variant="primary")
             cough_out = gr.HTML()
             cough_btn.click(fn=analyze_cough, inputs=audio_in, outputs=cough_out)
             gr.Markdown("---")
-            gr.Markdown("### Photo Triage (MedSigLIP)")
+            gr.Markdown("### Photo Triage")
             image_in = gr.Image(label="Upload Patient Photo", type="filepath", sources=["upload"])
-            image_btn = gr.Button("Classify Image with MedSigLIP", variant="primary")
+            image_btn = gr.Button("Analyze Image", variant="primary")
             image_out = gr.HTML()
             image_btn.click(fn=classify_image, inputs=image_in, outputs=image_out)