Upload app.py with huggingface_hub

app.py

@@ -9,805 +9,154 @@ from groq import Groq
 from PIL import Image
 from datetime import datetime
 from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.utils import EntryNotFoundError
 
 GROQ_KEY = os.environ.get("GROQ_API_KEY", "")
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
 HISTORY_REPO = "Saicharan21/cardiolab-chat-history"
-PAPERS_DB_REPO = "Saicharan21/cardiolab-papers-db"
-CARDIOLAB_MODEL = "Saicharan21/CardioLab-AI-Model"
 
-CHAT_MODELS = {
-    "CardioLab Fine-tuned (SJSU)": "cardiolab",
-    "Llama 3.3 70B (Best)": "llama-3.3-70b-versatile",
-    "Llama 3.1 8B (Fast)": "llama-3.1-8b-instant",
-    "Llama 4 Scout (New)": "meta-llama/llama-4-scout-17b-16e-instruct",
-    "Llama 4 Maverick": "meta-llama/llama-4-maverick-17b-128e-instruct",
-}
-
-KNOWHOW = (
-    "MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min cardiac output 80-120mmHg. "
-    "TGT: Arduino Uno Stepper Motor 150mL blood sampled at 0 20 40 60 minutes. "
-    "NORMAL RANGES: TAT below 8 ng/mL. PF1.2 below 2.0 nmol/L. Free hemoglobin below 20 mg/L. Platelets above 150. "
-    "HIGH RISK: TAT above 15. PF1.2 above 3.0. Hemoglobin above 50. Platelets below 100. "
-    "uPAD: Jaffe reaction creatinine picric acid orange-red. Normal 0.6-1.2 mg/dL. CKD above 1.5. "
-    "Stage2 1.5-3.0. Stage3-4 3.0-6.0. Stage5 above 6.0. "
+KNOWHOW = ("MCL: Sylgard 184 PDMS 10:1 ratio 48hr cure green laser PIV 70bpm 5L/min. "
+    "TGT: Arduino Uno Stepper Motor 150mL blood sampled at 0 20 40 60min measures TAT PF1.2 hemolysis platelets. "
+    "uPAD: Jaffe reaction creatinine plus picric acid gives orange-red color normal 0.6-1.2 mg/dL CKD above 1.5. "
     "MHV: 27mm SJM Regent bileaflet also trileaflet monoleaflet pediatric. "
-    "PIV: green laser 532nm. Normal velocity 0.5-2.0 m/s. Normal shear below 5 Pa. Risk above 10 Pa. "
-    "Equipment: Heska Element HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno."
-)
+    "Equipment: Heska HT5 hematology analyzer time-resolved PIV Tygon tubing Arduino Uno.")
 
 CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;900&family=Space+Grotesk:wght@400;500;700&display=swap');
-
-body {
-    background: #050a14 !important;
-    font-family: Inter, sans-serif !important;
-}
-
-body::before {
-    content: "";
-    position: fixed;
-    top: 0; left: 0; width: 100%; height: 100%;
-    background:
-        radial-gradient(ellipse 80% 60% at 20% 10%, rgba(193,18,31,0.12) 0%, transparent 60%),
-        radial-gradient(ellipse 60% 80% at 80% 90%, rgba(0,87,168,0.10) 0%, transparent 60%);
-    pointer-events: none;
-    z-index: 0;
-    animation: bgShift 12s ease-in-out infinite alternate;
-}
-
-@keyframes bgShift { 0%{opacity:1;transform:scale(1)} 100%{opacity:0.7;transform:scale(1.05)} }
-@keyframes msgIn { from{opacity:0;transform:translateY(10px)} to{opacity:1;transform:translateY(0)} }
-@keyframes pulse { 0%,100%{opacity:1;transform:scale(1)} 50%{opacity:0.5;transform:scale(1.3)} }
-
-.gradio-container {
-    background: transparent !important;
-    max-width: 1600px !important;
-    margin: 0 auto !important;
-}
-
-.tab-nav {
-    background: rgba(255,255,255,0.03) !important;
-    backdrop-filter: blur(20px) !important;
-    border: 1px solid rgba(255,255,255,0.08) !important;
-    border-radius: 16px !important;
-    padding: 6px !important;
-    margin: 10px 0 !important;
-    display: flex !important;
-    flex-wrap: wrap !important;
-    gap: 4px !important;
-}
-
-.tab-nav button {
-    background: transparent !important;
-    color: rgba(255,255,255,0.5) !important;
-    border: none !important;
-    border-radius: 10px !important;
-    padding: 8px 14px !important;
-    font-weight: 500 !important;
-    font-size: 0.78em !important;
-    white-space: nowrap !important;
-    transition: all 0.25s ease !important;
-}
-
-.tab-nav button:hover {
-    background: rgba(255,255,255,0.08) !important;
-    color: rgba(255,255,255,0.9) !important;
-    transform: translateY(-1px) !important;
-}
-
-.tab-nav button.selected {
-    background: linear-gradient(135deg, #c1121f, #e63946) !important;
-    color: white !important;
-    font-weight: 700 !important;
-    box-shadow: 0 4px 20px rgba(193,18,31,0.4) !important;
-    transform: translateY(-1px) !important;
-}
-
-.message.user {
-    background: linear-gradient(135deg, rgba(193,18,31,0.2), rgba(230,57,70,0.15)) !important;
-    border: 1px solid rgba(193,18,31,0.3) !important;
-    color: rgba(255,255,255,0.95) !important;
-    border-radius: 18px 18px 4px 18px !important;
-    padding: 14px 18px !important;
-    backdrop-filter: blur(10px) !important;
-    animation: msgIn 0.3s ease !important;
-}
-
-.message.bot {
-    background: rgba(255,255,255,0.05) !important;
-    border: 1px solid rgba(255,255,255,0.1) !important;
-    color: rgba(255,255,255,0.9) !important;
-    border-radius: 18px 18px 18px 4px !important;
-    padding: 14px 18px !important;
-    backdrop-filter: blur(10px) !important;
-    border-left: 3px solid #c1121f !important;
-    animation: msgIn 0.3s ease !important;
-}
-
-.chatbot {
-    background: rgba(255,255,255,0.02) !important;
-    border: 1px solid rgba(255,255,255,0.08) !important;
-    border-radius: 20px !important;
-    backdrop-filter: blur(20px) !important;
-}
-
-textarea, input[type=text], input[type=number] {
-    background: rgba(255,255,255,0.06) !important;
-    color: rgba(255,255,255,0.9) !important;
-    border: 1px solid rgba(255,255,255,0.12) !important;
-    border-radius: 14px !important;
-    transition: all 0.25s ease !important;
-    backdrop-filter: blur(10px) !important;
-}
-
-textarea:focus, input:focus {
-    border-color: rgba(193,18,31,0.6) !important;
-    box-shadow: 0 0 0 3px rgba(193,18,31,0.15) !important;
-    outline: none !important;
-    background: rgba(255,255,255,0.08) !important;
-}
-
-textarea::placeholder { color: rgba(255,255,255,0.3) !important; }
-
-button.primary {
-    background: linear-gradient(135deg, #c1121f 0%, #e63946 100%) !important;
-    color: white !important;
-    border: none !important;
-    border-radius: 12px !important;
-    font-weight: 700 !important;
-    box-shadow: 0 4px 20px rgba(193,18,31,0.35) !important;
-    transition: all 0.2s ease !important;
-}
-
-button.primary:hover {
-    transform: translateY(-2px) !important;
-    box-shadow: 0 8px 30px rgba(193,18,31,0.5) !important;
-}
-
-button.secondary {
-    background: rgba(255,255,255,0.07) !important;
-    color: rgba(255,255,255,0.7) !important;
-    border: 1px solid rgba(255,255,255,0.15) !important;
-    border-radius: 12px !important;
-    transition: all 0.2s ease !important;
-    backdrop-filter: blur(10px) !important;
-}
-
-button.secondary:hover {
-    background: rgba(255,255,255,0.12) !important;
-    color: white !important;
-}
-
-label span {
-    color: rgba(255,255,255,0.55) !important;
-    font-weight: 500 !important;
-    font-size: 0.78em !important;
-    letter-spacing: 0.06em !important;
-    text-transform: uppercase !important;
-}
-
-.block, .panel {
-    background: rgba(255,255,255,0.03) !important;
-    border: 1px solid rgba(255,255,255,0.07) !important;
-    border-radius: 20px !important;
-    backdrop-filter: blur(20px) !important;
-}
-
-.prose, .md { color: rgba(255,255,255,0.8) !important; }
-
-::-webkit-scrollbar { width: 5px; }
-/* FIX DROPDOWNS - z-index critical */
-.multiselect, .dropdown-arrow, .wrap-inner, select {
-    z-index: 9999 !important;
-    position: relative !important;
-}
-
-ul.options {
-    z-index: 99999 !important;
-    position: absolute !important;
-    background: #1a1a2e !important;
-    border: 1px solid rgba(193,18,31,0.4) !important;
-    border-radius: 10px !important;
-    color: white !important;
-}
-
-ul.options li {
-    background: #1a1a2e !important;
-    color: rgba(255,255,255,0.85) !important;
-    padding: 8px 12px !important;
-}
-
-ul.options li:hover {
-    background: rgba(193,18,31,0.3) !important;
-}
-
-/* Fix body::before blocking clicks */
-body::before {
-    pointer-events: none !important;
-    z-index: -1 !important;
-}
-
-/* Fix dropdown container */
-.gradio-dropdown {
-    z-index: 1000 !important;
-    position: relative !important;
-}
-
-/* Fix session dropdown */
-.gradio-dropdown ul {
-    z-index: 99999 !important;
-    background: #0d1117 !important;
-    border: 1px solid rgba(255,255,255,0.15) !important;
-    border-radius: 10px !important;
-}
-
-/* Fix all interactive elements z-index */
-button, input, select, textarea, .gr-dropdown {
-    position: relative !important;
-    z-index: 100 !important;
-}
-
-::-webkit-scrollbar-track { background: transparent; }
-::-webkit-scrollbar-thumb { background: rgba(255,255,255,0.15); border-radius: 10px; }
-::-webkit-scrollbar-thumb:hover { background: rgba(193,18,31,0.5); }
-
-img { border-radius: 14px !important; border: 1px solid rgba(255,255,255,0.08) !important; }
-/* GRADIO 6.x OVERRIDES */
-.svelte-1gfkn6j, .svelte-1ipelgc, .svelte-1occ011 {
-    background: rgba(255,255,255,0.06) !important;
-    color: rgba(255,255,255,0.9) !important;
-    border-color: rgba(255,255,255,0.12) !important;
-}
-
-/* Fix all containers */
-.container, .wrap, .form {
-    background: transparent !important;
-    border-color: rgba(255,255,255,0.08) !important;
-}
-
-/* Fix dropdowns */
-.multiselect, .dropdown, ul.options {
-    background: #0d1117 !important;
-    color: rgba(255,255,255,0.9) !important;
-    border-color: rgba(255,255,255,0.15) !important;
-}
-
-ul.options li {
-    background: #0d1117 !important;
-    color: rgba(255,255,255,0.85) !important;
-}
-
-ul.options li:hover {
-    background: rgba(193,18,31,0.2) !important;
-}
-
-/* Fix radio buttons */
-.radio-group label, .radio-group span {
-    color: rgba(255,255,255,0.8) !important;
-}
-
-/* Fix all textboxes */
-.scroll-hide, .overflow-y-auto {
-    background: rgba(255,255,255,0.05) !important;
-    color: rgba(255,255,255,0.9) !important;
-}
-
-/* Fix number inputs */
-input[type=number] {
-    background: rgba(255,255,255,0.06) !important;
-    color: rgba(255,255,255,0.9) !important;
-    border: 1px solid rgba(255,255,255,0.12) !important;
-}
-
-/* Fix file upload */
-.file-preview, .upload-button {
-    background: rgba(255,255,255,0.04) !important;
-    color: rgba(255,255,255,0.7) !important;
-    border-color: rgba(255,255,255,0.15) !important;
-}
-
-/* Fix all white backgrounds */
-.bg-white, .bg-gray-50, .bg-gray-100 {
-    background: rgba(255,255,255,0.04) !important;
-}
-
-/* Gradio panel backgrounds */
-div.gradio-group, div.gradio-row, div.gradio-column {
-    background: transparent !important;
-}
-
-/* Fix image containers */
-.image-container, .preview-container {
-    background: rgba(255,255,255,0.04) !important;
-    border: 1px solid rgba(255,255,255,0.08) !important;
-    border-radius: 14px !important;
-}
-
-/* Fix audio component */
-.audio-container, .waveform-container {
-    background: rgba(255,255,255,0.04) !important;
-    border-radius: 14px !important;
-}
-
-/* Fix markdown */
-.prose p, .prose h1, .prose h2, .prose h3 {
-    color: rgba(255,255,255,0.85) !important;
-}
-
-/* Fix all borders */
-.border, .border-gray-200, .border-gray-300 {
-    border-color: rgba(255,255,255,0.08) !important;
-}
-
-/* GRADIO 6.x OVERRIDES */
-.svelte-1gfkn6j, .svelte-1ipelgc, .svelte-1occ011 {
-    background: rgba(255,255,255,0.06) !important;
-    color: rgba(255,255,255,0.9) !important;
-    border-color: rgba(255,255,255,0.12) !important;
-}
-
-/* Fix all containers */
-.container, .wrap, .form {
-    background: transparent !important;
-    border-color: rgba(255,255,255,0.08) !important;
-}
-
-/* Fix dropdowns */
-.multiselect, .dropdown, ul.options {
-    background: #0d1117 !important;
-    color: rgba(255,255,255,0.9) !important;
-    border-color: rgba(255,255,255,0.15) !important;
-}
-
-ul.options li {
-    background: #0d1117 !important;
-    color: rgba(255,255,255,0.85) !important;
-}
-
-ul.options li:hover {
-    background: rgba(193,18,31,0.2) !important;
-}
-
-/* Fix radio buttons */
-.radio-group label, .radio-group span {
-    color: rgba(255,255,255,0.8) !important;
-}
-
-/* Fix all textboxes */
-.scroll-hide, .overflow-y-auto {
-    background: rgba(255,255,255,0.05) !important;
-    color: rgba(255,255,255,0.9) !important;
-}
-
-/* Fix number inputs */
-input[type=number] {
-    background: rgba(255,255,255,0.06) !important;
-    color: rgba(255,255,255,0.9) !important;
-    border: 1px solid rgba(255,255,255,0.12) !important;
-}
-
-/* Fix file upload */
-.file-preview, .upload-button {
-    background: rgba(255,255,255,0.04) !important;
-    color: rgba(255,255,255,0.7) !important;
-    border-color: rgba(255,255,255,0.15) !important;
-}
-
-/* Fix all white backgrounds */
-.bg-white, .bg-gray-50, .bg-gray-100 {
-    background: rgba(255,255,255,0.04) !important;
-}
-
-/* Gradio panel backgrounds */
-div.gradio-group, div.gradio-row, div.gradio-column {
-    background: transparent !important;
-}
-
-/* Fix image containers */
-.image-container, .preview-container {
-    background: rgba(255,255,255,0.04) !important;
-    border: 1px solid rgba(255,255,255,0.08) !important;
-    border-radius: 14px !important;
-}
-
-/* Fix audio component */
-.audio-container, .waveform-container {
-    background: rgba(255,255,255,0.04) !important;
-    border-radius: 14px !important;
-}
-
-/* Fix markdown */
-.prose p, .prose h1, .prose h2, .prose h3 {
-    color: rgba(255,255,255,0.85) !important;
-}
-
-/* Fix all borders */
-.border, .border-gray-200, .border-gray-300 {
-    border-color: rgba(255,255,255,0.08) !important;
-}
-
+body, .gradio-container { background: #f0f4f8 !important; }
+.tab-nav { background: #ffffff !important; border-bottom: 2px solid #e2e8f0 !important; padding: 4px 5px 0 5px !important; display: flex !important; flex-wrap: wrap !important; gap: 3px !important; }
+.tab-nav button { background: #f7fafc !important; color: #2d3748 !important; border: 1px solid #e2e8f0 !important; border-radius: 6px 6px 0 0 !important; padding: 8px 10px !important; font-weight: 600 !important; font-size: 0.8em !important; white-space: nowrap !important; }
+.tab-nav button:hover { background: #ebf4ff !important; color: #1a237e !important; }
+.tab-nav button.selected { background: linear-gradient(135deg, #e63946, #c1121f) !important; color: #ffffff !important; font-weight: 700 !important; }
+button.primary { background: linear-gradient(135deg, #e63946 0%, #c1121f 100%) !important; color: white !important; border: none !important; border-radius: 8px !important; font-weight: 700 !important; }
+button.secondary { background: #edf2f7 !important; color: #4a5568 !important; border: 1px solid #cbd5e0 !important; border-radius: 8px !important; }
+textarea, input[type=number], input[type=text] { background: #f7fafc !important; color: #1a202c !important; border: 1px solid #cbd5e0 !important; border-radius: 8px !important; }
+.message.user { background: linear-gradient(135deg, #e63946, #c1121f) !important; color: white !important; }
+.message.bot { background: #ebf4ff !important; color: #1a202c !important; border: 1px solid #bee3f8 !important; }
+label span { color: #2b6cb0 !important; font-weight: 600 !important; font-size: 0.85em !important; text-transform: uppercase !important; }
 """
 
-HEADER = """
-<style>
-@keyframes hb{0%,100%{transform:scale(1)}15%{transform:scale(1.15)}30%{transform:scale(1)}45%{transform:scale(1.08)}60%{transform:scale(1)}}
-@keyframes ecg{from{stroke-dashoffset:500}to{stroke-dashoffset:0}}
-</style>
-<div style="background:linear-gradient(135deg,#0f172a 0%,#1e0a0a 100%);padding:14px 28px;display:flex;align-items:center;justify-content:space-between;border-bottom:2px solid #c1121f;">
-
-  <div style="display:flex;align-items:center;gap:10px;">
-    <div style="background:rgba(0,87,168,0.2);border:1px solid rgba(0,87,168,0.3);border-radius:10px;padding:6px 10px;">
-      <div style="color:rgba(232,160,32,0.9);font-size:0.6em;font-weight:600;letter-spacing:2px;text-transform:uppercase;">SJSU</div>
-      <div style="color:white;font-size:0.65em;font-weight:600;white-space:nowrap;">Biomedical Eng.</div>
-    </div>
-  </div>
-
-  <div style="display:flex;align-items:center;gap:16px;">
-    <svg width="80" height="24" viewBox="0 0 100 24" style="opacity:0.7;">
-      <polyline points="0,12 15,12 20,4 24,20 28,2 32,18 36,12 100,12"
-        fill="none" stroke="#c1121f" stroke-width="2" stroke-linecap="round"
-        stroke-dasharray="500" style="animation:ecg 1.5s ease forwards;"/>
-    </svg>
-
-    <div style="text-align:center;">
-      <div style="font-size:1.8em;font-weight:900;letter-spacing:1px;line-height:1.1;">
-        <span style="color:white;">Cardio</span><span style="color:#c1121f;">Lab</span><span style="color:white;"> AI</span>
-      </div>
-      <div style="color:rgba(255,255,255,0.4);font-size:0.6em;letter-spacing:2px;text-transform:uppercase;margin-top:2px;">SJSU Biomedical Engineering</div>
-    </div>
-
-    <div style="animation:hb 1.4s ease infinite;">
-      <svg width="36" height="34" viewBox="0 0 100 90">
-        <defs>
-          <radialGradient id="hg4" cx="50%" cy="35%">
-            <stop offset="0%" stop-color="#e63946"/>
-            <stop offset="100%" stop-color="#7d0a11"/>
-          </radialGradient>
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-        <path d="M50 85 C50 85 5 55 5 30 C5 15 18 5 30 5 C38 5 45 9 50 15 C55 9 62 5 70 5 C82 5 95 15 95 30 C95 55 50 85 50 85Z" fill="url(#hg4)"/>
-        <polyline points="22,45 30,45 34,34 38,56 42,28 46,50 52,45 78,45" fill="none" stroke="white" stroke-width="3" stroke-linecap="round" opacity="0.9"/>
-      </svg>
-    </div>
-
-    <svg width="80" height="24" viewBox="0 0 100 24" style="opacity:0.7;transform:scaleX(-1);">
-      <polyline points="0,12 15,12 20,4 24,20 28,2 32,18 36,12 100,12"
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-        stroke-dasharray="500" style="animation:ecg 1.8s ease forwards;"/>
-    </svg>
-  </div>
-
-  <div style="display:flex;flex-direction:column;align-items:flex-end;gap:4px;">
-    <div style="display:flex;gap:6px;">
-      <span style="background:rgba(46,204,113,0.15);border:1px solid rgba(46,204,113,0.3);color:rgba(255,255,255,0.7);padding:2px 8px;border-radius:20px;font-size:0.6em;font-weight:600;">RAG ON</span>
-      <span style="background:rgba(193,18,31,0.15);border:1px solid rgba(193,18,31,0.3);color:rgba(255,255,255,0.7);padding:2px 8px;border-radius:20px;font-size:0.6em;font-weight:600;">5 MODELS</span>
-    </div>
-    <div style="color:rgba(255,255,255,0.25);font-size:0.58em;">MHV · CKD · FSI</div>
-  </div>
-
-</div>
-<div style="height:1px;background:linear-gradient(90deg,transparent,#0057a8,#c1121f,#e8a020,#c1121f,#0057a8,transparent);"></div>
-"""
+# ── PERSISTENT HISTORY FUNCTIONS ──────────────────────────────────
+def get_history_api():
+    if not HF_TOKEN: return None
+    return HfApi(token=HF_TOKEN)
 
-SIDEBAR_HTML = """
-<div style="background:linear-gradient(135deg,#fff5f5,#fef2f2);border:1px solid #fecaca;border-radius:12px;padding:12px;margin-bottom:8px;">
-  <div style="display:flex;align-items:center;gap:8px;">
-    <div style="width:6px;height:6px;background:#c1121f;border-radius:50%;"></div>
-    <span style="color:#c1121f;font-weight:700;font-size:0.8em;letter-spacing:1px;">SJSU CARDIOLAB</span>
-  </div>
-  <div style="color:#94a3b8;font-size:0.7em;margin-top:2px;">Conversations</div>
-</div>
-"""
-
-STATUS_BANNER = """
-<div style="background:#f0fdf4;border:1px solid #bbf7d0;border-radius:10px;margin:6px 0;padding:8px 16px;display:flex;align-items:center;justify-content:center;gap:16px;flex-wrap:wrap;">
-  <div style="display:flex;align-items:center;gap:6px;">
-    <div style="width:7px;height:7px;background:#22c55e;border-radius:50%;"></div>
-    <span style="color:#15803d;font-size:0.78em;font-weight:600;">RAG Active — 417 chunks from 16 SJSU papers</span>
-  </div>
-  <div style="color:#d1fae5;">|</div>
-  <div style="display:flex;align-items:center;gap:6px;">
-    <div style="width:7px;height:7px;background:#f59e0b;border-radius:50%;"></div>
-    <span style="color:#92400e;font-size:0.78em;font-weight:600;">Fine-tuned Model Loaded</span>
-  </div>
-  <div style="color:#d1fae5;">|</div>
-  <div style="display:flex;align-items:center;gap:6px;">
-    <div style="width:7px;height:7px;background:#3b82f6;border-radius:50%;"></div>
-    <span style="color:#1e40af;font-size:0.78em;font-weight:600;">5 AI Models Ready</span>
-  </div>
-</div>
-"""
-
-FOOTER_HTML = """
-<div style="text-align:center;padding:12px;border-top:1px solid #e2e8f0;background:#f8fafc;margin-top:8px;">
-  <span style="color:#94a3b8;font-size:0.72em;">
-    CardioLab AI v39 | SJSU Biomedical Engineering |
-    Inspired by <a href="https://github.com/snap-stanford/Biomni" style="color:#c1121f;text-decoration:none;">Biomni Stanford</a> |
-    <a href="https://github.com/pranatechsol/Cardio-Lab-Ai" style="color:#0057a8;text-decoration:none;">GitHub</a> |
-    Apache 2.0 | $0 Cost
-  </span>
-</div>
-"""
-
-# ── LOAD PAPERS + MODEL ────────────────────────────────────
-CHUNKS = []
-METADATA = []
-EMBEDDINGS = None
-PAPERS_LOADED = False
-EMBEDDER = None
-CARDIOLAB_TOKENIZER = None
-CARDIOLAB_LLM = None
-CARDIOLAB_MODEL_LOADED = False
-
-def load_papers():
-    global CHUNKS, METADATA, EMBEDDINGS, PAPERS_LOADED, EMBEDDER
-    try:
-        from sentence_transformers import SentenceTransformer
-        chunks_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="chunks.json", repo_type="dataset", token=HF_TOKEN)
-        meta_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="metadata.json", repo_type="dataset", token=HF_TOKEN)
-        emb_path = hf_hub_download(repo_id=PAPERS_DB_REPO, filename="embeddings.npy", repo_type="dataset", token=HF_TOKEN)
-        with open(chunks_path) as f: CHUNKS = json.load(f)
-        with open(meta_path) as f: METADATA = json.load(f)
-        EMBEDDINGS = np.load(emb_path)
-        EMBEDDER = SentenceTransformer("all-MiniLM-L6-v2")
-        PAPERS_LOADED = True
-        print("Papers loaded: " + str(len(CHUNKS)) + " chunks")
-    except Exception as e:
-        print("Paper load error: " + str(e))
-
-def load_cardiolab_model():
-    global CARDIOLAB_TOKENIZER, CARDIOLAB_LLM, CARDIOLAB_MODEL_LOADED
-    try:
-        import torch
-        from transformers import AutoModelForCausalLM, AutoTokenizer
-        print("Loading CardioLab fine-tuned model...")
-        CARDIOLAB_TOKENIZER = AutoTokenizer.from_pretrained(CARDIOLAB_MODEL, token=HF_TOKEN)
-        CARDIOLAB_TOKENIZER.pad_token = CARDIOLAB_TOKENIZER.eos_token
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        CARDIOLAB_LLM = AutoModelForCausalLM.from_pretrained(
-            CARDIOLAB_MODEL, token=HF_TOKEN,
-            torch_dtype=torch.float16 if device == "cuda" else torch.float32,
-            device_map="auto" if device == "cuda" else None,
-            low_cpu_mem_usage=True
-        )
-        CARDIOLAB_MODEL_LOADED = True
-        print("CardioLab model loaded!")
-    except Exception as e:
-        print("CardioLab model error: " + str(e))
-
-load_papers()
-load_cardiolab_model()
-
-def search_papers(query, n=4):
-    if not PAPERS_LOADED or EMBEDDINGS is None or EMBEDDER is None:
-        return "", []
-    try:
-        q_emb = EMBEDDER.encode([query])
-        norms = np.linalg.norm(EMBEDDINGS, axis=1, keepdims=True)
-        emb_norm = EMBEDDINGS / (norms + 1e-10)
-        q_norm = q_emb / (np.linalg.norm(q_emb) + 1e-10)
-        scores = (emb_norm @ q_norm.T).flatten()
-        top_idx = np.argsort(scores)[::-1][:n]
-        context = ""
-        results = []
-        seen = set()
-        for idx in top_idx:
-            chunk = CHUNKS[idx]
-            meta = METADATA[idx]
-            score = float(scores[idx])
-            if score > 0.25:
-                results.append({"chunk": chunk, "paper": meta["paper"], "score": score})
-                if meta["paper"] not in seen:
-                    context += chr(10) + "=== FROM: " + meta["paper"] + " ===" + chr(10)
-                    seen.add(meta["paper"])
-                context += chunk[:500] + chr(10)
-        return context, results
-    except Exception as e:
-        return "", []
-
-# ── SESSION MANAGEMENT ─────────────────────────────────────
 def load_all_sessions():
     if not HF_TOKEN: return {}
     try:
-        path = hf_hub_download(repo_id=HISTORY_REPO, filename="chat_history.json", repo_type="dataset", token=HF_TOKEN)
-        with open(path) as f: return json.load(f)
-    except: return {}
+        api = get_history_api()
+        path = hf_hub_download(
+            repo_id=HISTORY_REPO,
+            filename="chat_history.json",
+            repo_type="dataset",
+            token=HF_TOKEN
+        )
+        with open(path, "r") as f:
+            return json.load(f)
+    except Exception:
+        return {}
 
 def save_all_sessions(sessions):
     if not HF_TOKEN: return False
     try:
-        api2 = HfApi(token=HF_TOKEN)
-        api2.upload_file(
-            path_or_fileobj=json.dumps(sessions, indent=2).encode(),
+        api = get_history_api()
+        content = json.dumps(sessions, indent=2)
+        api.upload_file(
+            path_or_fileobj=content.encode(),
             path_in_repo="chat_history.json",
-            repo_id=HISTORY_REPO, repo_type="dataset",
-            token=HF_TOKEN, commit_message="Update"
+            repo_id=HISTORY_REPO,
+            repo_type="dataset",
+            token=HF_TOKEN,
+            commit_message="Update chat history"
         )
         return True
-    except: return False
+    except Exception as e:
+        print("Save error:", e)
+        return False
 
 def get_session_list():
-    s = load_all_sessions()
-    return list(reversed(list(s.keys()))) if s else ["No saved sessions"]
-
-def save_session(history, name):
-    if not history: return "Nothing to save", gr.update()
-    if not name or not name.strip():
-        name = "Chat " + datetime.now().strftime("%b %d %H:%M")
     sessions = load_all_sessions()
-    sessions[name] = {"messages": history, "saved_at": datetime.now().isoformat()}
-    ok = save_all_sessions(sessions)
-    choices = get_session_list()
-    return ("Saved: " + name if ok else "Save failed"), gr.update(choices=choices, value=name)
+    if not sessions:
+        return ["No saved sessions yet"]
+    return list(sessions.keys())
 
-def load_session(name):
-    if not name or "No saved" in name: return [], "Select a session"
+def load_session(session_name):
+    if not session_name or session_name == "No saved sessions yet":
+        return [], "No session loaded"
     sessions = load_all_sessions()
-    return (sessions[name]["messages"], "Loaded: " + name) if name in sessions else ([], "Not found")
-
-def delete_session(name):
-    if not name or "No saved" in name: return "Select a session", gr.update()
+    if session_name in sessions:
+        history = sessions[session_name]["messages"]
+        return history, "Loaded: " + session_name + " (" + str(len(history)) + " messages)"
+    return [], "Session not found"
+
+def save_session(history, session_name):
+    if not history:
+        return "Nothing to save — chat is empty", gr.update()
+    if not session_name.strip():
+        session_name = "Session " + datetime.now().strftime("%Y-%m-%d %H:%M")
     sessions = load_all_sessions()
-    if name in sessions:
-        del sessions[name]
+    sessions[session_name] = {
+        "messages": history,
+        "saved_at": datetime.now().isoformat(),
+        "message_count": len(history)
+    }
+    success = save_all_sessions(sessions)
+    if success:
+        return "Saved: " + session_name, gr.update(choices=get_session_list(), value=session_name)
+    return "Save failed — check HF_TOKEN in Space secrets", gr.update()
+
+def delete_session(session_name):
+    if not session_name or session_name == "No saved sessions yet":
+        return "No session selected", gr.update()
+    sessions = load_all_sessions()
+    if session_name in sessions:
+        del sessions[session_name]
         save_all_sessions(sessions)
-        choices = get_session_list()
-        return "Deleted: " + name, gr.update(choices=choices, value=choices[0] if choices else None)
-    return "Not found", gr.update()
-
-def new_chat(): return [], "", "New chat started"
+        new_list = get_session_list()
+        return "Deleted: " + session_name, gr.update(choices=new_list, value=new_list[0] if new_list else None)
+    return "Session not found", gr.update()
 
-# ── SEARCH ─────────────────────────────────────────────────
-def get_pubmed_chat(query, n=3):
+# ── CHAT FUNCTIONS ────────────────────────────────────────────────
+def get_pubmed(query, n=5):
     try:
-        r = requests.get(
-            "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
-            params={"db":"pubmed","term":query+" AND (heart valve OR hemodynamics OR microfluidic OR thrombogen OR creatinine OR CKD)","retmax":n,"retmode":"json","sort":"date","field":"tiab"},
-            timeout=10
-        )
+        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
+            params={"db":"pubmed","term":query+" AND (mechanical heart valve OR microfluidic OR CKD OR thrombogenicity)","retmax":n,"retmode":"json","sort":"date"},timeout=10)
         ids = r.json()["esearchresult"]["idlist"]
-        return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids]) if ids else ""
+        if not ids: return ""
+        return chr(10).join(["https://pubmed.ncbi.nlm.nih.gov/"+i for i in ids])
     except: return ""
 
-def expand_query_ai(query):
-    if not GROQ_KEY: return query
-    try:
-        client = Groq(api_key=GROQ_KEY)
-        resp = client.chat.completions.create(
-            model="llama-3.1-8b-instant",
-            messages=[
-                {"role":"system","content":"Biomedical PubMed expert. Convert to MeSH terms for heart valves hemodynamics PIV thrombogenicity FSI microfluidics CKD. Return ONLY terms."},
-                {"role":"user","content":"Optimize: " + query}
-            ],
-            max_tokens=80
-        )
-        return resp.choices[0].message.content.strip() or query
-    except: return query
-
-def quick_search(query, search_model="Llama 3.3 70B (Best)"):
+def quick_search(query):
     if not query.strip(): return "Please enter a topic."
-    expanded = expand_query_ai(query)
-    results = []
-    try:
-        forced = expanded + " AND (heart valve OR hemodynamics OR microfluidic OR thrombogen OR creatinine OR PIV OR CKD)"
-        r = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi",
-            params={"db":"pubmed","term":forced,"retmax":8,"retmode":"json","sort":"date","field":"tiab"},timeout=12)
-        ids = r.json()["esearchresult"]["idlist"]
-        if ids:
-            r2 = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi",
-                params={"db":"pubmed","id":",".join(ids),"retmode":"xml","rettype":"abstract"},timeout=12)
-            import xml.etree.ElementTree as ET
-            root = ET.fromstring(r2.content)
-            for article in root.findall(".//PubmedArticle"):
-                try:
-                    title = article.find(".//ArticleTitle").text or "No title"
-                    pmid = article.find(".//PMID").text or ""
-                    year_el = article.find(".//PubDate/Year")
-                    year = year_el.text if year_el is not None else ""
-                    results.append({"source":"PubMed","title":str(title),"year":year,"url":"https://pubmed.ncbi.nlm.nih.gov/"+pmid})
-                except: continue
-    except: pass
+    pubmed = get_pubmed(query, n=8)
     try:
         r = requests.get("https://api.semanticscholar.org/graph/v1/paper/search",
-            params={"query":expanded,"limit":6,"fields":"title,year,url,citationCount"},timeout=12)
-        for p in r.json().get("data",[]):
-            year = p.get("year",0) or 0
-            if int(year) >= 2015:
-                results.append({"source":"Scholar","title":p.get("title",""),"year":str(year),"url":p.get("url",""),"citations":str(p.get("citationCount",0))})
-    except: pass
-    out = "QUERY: " + query + chr(10) + "AI EXPANDED: " + expanded + chr(10) + "="*45 + chr(10) + chr(10)
-    groups = {"PubMed":[],"Scholar":[]}
-    seen = set()
-    for r in results:
-        key = r["title"][:50].lower()
-        if key not in seen and r.get("url"):
-            seen.add(key); groups.get(r["source"],[]).append(r)
-    for source, papers in groups.items():
-        if not papers: continue
-        out += "--- " + source + " ---" + chr(10)
-        for p in papers[:8]:
-            out += p["title"][:85] + " (" + p["year"] + ")" + chr(10)
-            out += "  " + p.get("url","") + chr(10) + chr(10)
-    out += "--- SJSU ScholarWorks ---" + chr(10)
-    out += "https://scholarworks.sjsu.edu/do/search/?q=" + requests.utils.quote(query) + "&context=6781027"
-    return out
-
-# ── CHAT ───────────────────────────────────────────────────
-def answer_with_cardiolab_model(question, paper_context=""):
-    if not CARDIOLAB_MODEL_LOADED: return None
-    try:
-        import torch
-        system = "You are CardioLab AI for SJSU Biomedical Engineering."
-        if paper_context:
-            system += " Use these SJSU research papers: " + paper_context[:400]
-        prompt = "<|system|>" + system + "</s><|user|>" + question + "</s><|assistant|>"
-        inputs = CARDIOLAB_TOKENIZER(prompt, return_tensors="pt", truncation=True, max_length=512)
-        device = next(CARDIOLAB_LLM.parameters()).device
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-        with torch.no_grad():
-            outputs = CARDIOLAB_LLM.generate(
-                **inputs, max_new_tokens=200, do_sample=True,
-                temperature=0.3, pad_token_id=CARDIOLAB_TOKENIZER.eos_token_id
-            )
-        response = CARDIOLAB_TOKENIZER.decode(outputs[0], skip_special_tokens=True)
-        answer = response.split("<|assistant|>")[-1].strip() if "<|assistant|>" in response else response[-300:].strip()
-        return answer if len(answer) > 20 else None
-    except Exception as e:
-        print("CardioLab model error: " + str(e))
-        return None
+            params={"query":query+" biomedical","limit":5,"fields":"title,year,url"},timeout=10)
+        papers = r.json().get("data",[])
+        scholar = chr(10).join([p.get("title","")[:80]+" ("+str(p.get("year",""))+")"+chr(10)+"   "+p.get("url","") for p in papers if p.get("url","")])
+    except: scholar = ""
+    return "PUBMED:"+chr(10)+pubmed+chr(10)+chr(10)+"SCHOLAR:"+chr(10)+scholar
 
-def research_chat(message, history, chat_model="Llama 3.3 70B (Best)"):
-    if not message.strip(): return "", history
-    paper_context, paper_results = search_papers(message, n=4)
-    if chat_model == "CardioLab Fine-tuned (SJSU)" and CARDIOLAB_MODEL_LOADED:
-        answer = answer_with_cardiolab_model(message, paper_context)
-        if answer:
-            if paper_results:
-                unique_papers = list(dict.fromkeys([r["paper"] for r in paper_results]))
-                answer += chr(10) + chr(10) + "Sources from SJSU CardioLab papers:"
-                for p in unique_papers[:3]:
-                    answer += chr(10) + "  - " + p.replace(".pdf","").replace("_"," ")
-            pubmed = get_pubmed_chat(message, n=2)
-            if pubmed: answer += chr(10) + "PubMed: " + pubmed
-            history.append({"role":"user","content":message})
-            history.append({"role":"assistant","content":"[CardioLab Fine-tuned] " + answer})
-            return "", history
+def research_chat(message, history):
     if not GROQ_KEY:
         history.append({"role":"user","content":message})
-        history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings."})
+        history.append({"role":"assistant","content":"Error: Add GROQ_API_KEY to Space Settings Secrets."})
         return "", history
     try:
-        model_id = CHAT_MODELS.get(chat_model, "llama-3.3-70b-versatile")
         client = Groq(api_key=GROQ_KEY)
-        if paper_context:
-            system_prompt = (
-                "You are CardioLab AI for SJSU Biomedical Engineering. "
-                "Answer using SJSU CardioLab research papers below. "
-                "Always cite the paper name when using specific data." +
-                chr(10) + chr(10) + "SJSU CARDIOLAB PAPERS:" + chr(10) + paper_context +
-                chr(10) + chr(10) + "ADDITIONAL KNOWLEDGE: " + KNOWHOW
-            )
-        else:
-            system_prompt = "You are CardioLab AI for SJSU Biomedical Engineering. Expert in MHV MCL PIV TGT uPAD CKD FSI. " + KNOWHOW
-        msgs = [{"role":"system","content":system_prompt}]
+        msgs = [{"role":"system","content":"You are CardioLab AI. Expert in MHV MCL PIV TGT uPAD CKD FSI. Remember full conversation. Never invent URLs. "+KNOWHOW}]
         for item in history:
             if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]})
         msgs.append({"role":"user","content":message})
-        resp = client.chat.completions.create(model=model_id, messages=msgs, max_tokens=800)
+        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=700)
         answer = resp.choices[0].message.content
-        if paper_results:
-            unique_papers = list(dict.fromkeys([r["paper"] for r in paper_results]))
-            answer += chr(10) + chr(10) + "Sources from SJSU CardioLab papers:"
-            for p in unique_papers[:3]:
-                answer += chr(10) + "  - " + p.replace(".pdf","").replace("_"," ")
-        pubmed = get_pubmed_chat(message, n=2)
-        if pubmed: answer += chr(10) + "PubMed: " + pubmed
+        pubmed = get_pubmed(message, n=3)
+        if pubmed: answer += chr(10)+chr(10)+"PUBMED:"+chr(10)+pubmed
         history.append({"role":"user","content":message})
         history.append({"role":"assistant","content":answer})
         return "", history
     except Exception as e:
         history.append({"role":"user","content":message})
-        history.append({"role":"assistant","content":"Error: " + str(e)})
+        history.append({"role":"assistant","content":"Error: "+str(e)})
         return "", history
 
 def voice_chat(audio, history):
@@ -818,384 +167,263 @@ def voice_chat(audio, history):
         client = Groq(api_key=GROQ_KEY)
         with open(audio, "rb") as f:
             tx = client.audio.transcriptions.create(file=("audio.wav", f, "audio/wav"), model="whisper-large-v3")
-        paper_context, _ = search_papers(tx.text, n=3)
-        system = "You are CardioLab AI. " + KNOWHOW
-        if paper_context:
-            system = "You are CardioLab AI. Use these SJSU papers:" + chr(10) + paper_context + chr(10) + KNOWHOW
-        msgs = [{"role":"system","content":system}]
+        msgs = [{"role":"system","content":"You are CardioLab AI. "+KNOWHOW}]
         for item in history:
             if isinstance(item, dict): msgs.append({"role":item["role"],"content":item["content"]})
         msgs.append({"role":"user","content":tx.text})
-        resp = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=msgs, max_tokens=500)
-        history.append({"role":"user","content":"Voice: " + tx.text})
+        resp = client.chat.completions.create(model="llama-3.3-70b-versatile",messages=msgs,max_tokens=500)
+        history.append({"role":"user","content":"[Voice] "+tx.text})
         history.append({"role":"assistant","content":resp.choices[0].message.content})
         return history
     except Exception as e:
-        history.append({"role":"assistant","content":"Voice error: " + str(e)})
+        history.append({"role":"assistant","content":"Voice error: "+str(e)})
         return history
 
-# ── PHASE D ────────────────────────────────────────────────
-def generate_protocol(experiment_type, specific_params):
-    if not GROQ_KEY: return "Error: Add GROQ_API_KEY to Space Settings."
-    if not experiment_type: return "Please select an experiment type."
-    try:
-        client = Groq(api_key=GROQ_KEY)
-        paper_context, _ = search_papers(experiment_type, n=4)
-        lab_ctx = {
-            "MCL": "Sylgard 184 PDMS 10:1 ratio 48hr cure. Tygon tubing. 70bpm 5L/min 80-120mmHg.",
-            "PIV": "Green laser 532nm time-resolved. Normal velocity 0.5-2.0 m/s. Shear below 5 Pa.",
-            "Thrombogenicity": "Arduino Uno stepper motor 48V. 150mL fresh blood. Sample at 0 20 40 60 min. Heska HT5. Measures TAT PF1.2 free hemoglobin platelets. TAT normal below 8 ng/mL. PF1.2 normal below 2.0 nmol/L.",
-            "uPAD": "Whatman filter paper. Wax printer 120C. Picric acid alkaline solution. Jaffe reaction.",
-            "FSI": "COMSOL Multiphysics ALE mesh. Blood 1060 kg/m3 0.0035 Pa.s. SJM bileaflet geometry.",
-        }
-        extra = next((v for k, v in lab_ctx.items() if k.lower() in experiment_type.lower()), "")
-        system_msg = (
-            "You are CardioLab AI protocol generator for SJSU Biomedical Engineering. "
-            "Generate a COMPLETE detailed lab protocol with sections: "
-            "1. OBJECTIVE 2. MATERIALS AND EQUIPMENT with exact quantities "
-            "3. SAFETY CONSIDERATIONS 4. STEP-BY-STEP PROCEDURE numbered "
-            "5. DATA COLLECTION 6. ANALYSIS METHOD "
-            "7. EXPECTED RESULTS with CardioLab normal ranges 8. TROUBLESHOOTING "
-            "Use exact SJSU CardioLab values."
-        )
-        user_msg = "Generate complete protocol for: " + experiment_type
-        if specific_params and specific_params.strip():
-            user_msg += chr(10) + "Parameters: " + specific_params
-        if extra:
-            user_msg += chr(10) + "CardioLab context: " + extra
-        if paper_context:
-            user_msg += chr(10) + "From SJSU papers: " + paper_context[:600]
-        resp = client.chat.completions.create(
-            model="llama-3.3-70b-versatile",
-            messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}],
-            max_tokens=1200
-        )
-        return resp.choices[0].message.content
-    except Exception as e:
-        return "Error: " + str(e)
-
-def generate_report(data_description, experiment_type, results):
-    if not GROQ_KEY: return "Error: Add GROQ_API_KEY to Space Settings."
-    if not experiment_type: return "Please select a study type."
-    try:
-        client = Groq(api_key=GROQ_KEY)
-        paper_context, _ = search_papers(experiment_type, n=3)
-        system_msg = (
-            "You are CardioLab AI report writer for SJSU Biomedical Engineering. "
-            "Generate a professional research report with sections: "
-            "1. ABSTRACT 150 words 2. INTRODUCTION background and objectives "
-            "3. MATERIALS AND METHODS 4. RESULTS AND DISCUSSION "
-            "5. CONCLUSION 6. RECOMMENDATIONS 7. REFERENCES cite SJSU CardioLab papers. "
-            "Use specific values. Write in professional academic style."
-        )
-        user_msg = "Write research report for: " + experiment_type
-        if data_description and data_description.strip():
-            user_msg += chr(10) + "Description: " + data_description
-        if results and results.strip():
-            user_msg += chr(10) + "Results: " + results
-        if paper_context:
-            user_msg += chr(10) + "SJSU papers: " + paper_context[:600]
-        resp = client.chat.completions.create(
-            model="llama-3.3-70b-versatile",
-            messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}],
-            max_tokens=1500
-        )
-        return resp.choices[0].message.content
-    except Exception as e:
-        return "Error: " + str(e)
-
-def generate_hypothesis(research_area, current_findings):
-    if not GROQ_KEY: return "Error: Add GROQ_API_KEY to Space Settings."
-    if not research_area: return "Please select a research area."
-    try:
-        client = Groq(api_key=GROQ_KEY)
-        paper_context, _ = search_papers(research_area, n=3)
-        system_msg = (
-            "You are CardioLab AI research assistant for SJSU Biomedical Engineering. "
-            "Generate 3 specific testable research hypotheses. For each provide: "
-            "H0 null hypothesis, H1 alternative hypothesis, Scientific rationale, "
-            "Suggested experiment, Expected outcome and measurable metrics. "
-            "Base on SJSU CardioLab research."
-        )
-        user_msg = "Generate hypotheses for: " + research_area
-        if current_findings and current_findings.strip():
-            user_msg += chr(10) + "Current findings: " + current_findings
-        if paper_context:
-            user_msg += chr(10) + "SJSU papers: " + paper_context[:500]
-        resp = client.chat.completions.create(
-            model="llama-3.3-70b-versatile",
-            messages=[{"role":"system","content":system_msg},{"role":"user","content":user_msg}],
-            max_tokens=1000
-        )
-        return resp.choices[0].message.content
-    except Exception as e:
-        return "Error: " + str(e)
-
-# ── ANALYSIS TOOLS ─────────────────────────────────────────
+# ── ANALYSIS TOOLS ────────────────────────────────────────────────
 def analyze_upad_photo(image):
     if image is None: return None, "Upload a uPAD photo first."
     try:
         img = Image.fromarray(image) if not isinstance(image, Image.Image) else image
-        arr = np.array(img); h, w = arr.shape[:2]
-        y1, y2, x1, x2 = int(h*0.35), int(h*0.65), int(w*0.35), int(w*0.65)
-        zone = arr[y1:y2, x1:x2]
-        R = float(np.mean(zone[:,:,0]))
-        G = float(np.mean(zone[:,:,1]))
-        B = float(np.mean(zone[:,:,2]))
+        arr = np.array(img)
+        h,w = arr.shape[:2]
+        y1,y2,x1,x2 = int(h*0.35),int(h*0.65),int(w*0.35),int(w*0.65)
+        zone = arr[y1:y2,x1:x2]
+        R,G,B = float(np.mean(zone[:,:,0])),float(np.mean(zone[:,:,1])),float(np.mean(zone[:,:,2]))
         c = max(0, round(0.018*(R-B)-0.3, 2))
-        if c < 1.2: s, a = "Normal", "Monitor annually."
-        elif c < 1.5: s, a = "Borderline", "Repeat in 3 months."
-        elif c < 3.0: s, a = "Stage 2 CKD", "Consult nephrologist."
-        elif c < 6.0: s, a = "Stage 3-4 CKD", "Immediate consultation."
-        else: s, a = "Stage 5 CKD", "Emergency care."
-        ri = img.copy()
+        if c<1.2: s,a="Normal","Monitor annually."
+        elif c<1.5: s,a="Borderline","Repeat in 3 months."
+        elif c<3.0: s,a="Stage 2 CKD","Consult nephrologist."
+        elif c<6.0: s,a="Stage 3-4 CKD","Immediate consultation."
+        else: s,a="Stage 5 CKD","Emergency care needed."
+        result_img = img.copy()
         import PIL.ImageDraw as D
-        D.Draw(ri).rectangle([x1, y1, x2, y2], outline=(0,255,0), width=3)
-        result = ("uPAD ANALYSIS" + chr(10) +
-            "R:" + str(round(R,1)) + " G:" + str(round(G,1)) + " B:" + str(round(B,1)) + chr(10) +
-            "Creatinine: " + str(c) + " mg/dL" + chr(10) +
-            "Stage: " + s + chr(10) + "Action: " + a)
-        return ri, result
-    except Exception as e:
-        return None, "Error: " + str(e)
-
-def mk_chart(fn, title, bg, fg, gc, ac, pb):
-    fig2, ax = plt.subplots(figsize=(8,5))
-    fig2.patch.set_facecolor(bg); ax.set_facecolor(pb)
-    fn(ax)
-    ax.set_title(title, color=fg, fontweight="bold", fontsize=13, pad=8)
-    ax.tick_params(colors=ac, labelsize=10)
-    ax.grid(True, alpha=0.3, color=gc, linestyle="--")
-    for sp in ["top","right"]: ax.spines[sp].set_visible(False)
-    for sp in ["bottom","left"]: ax.spines[sp].set_color(gc)
-    plt.tight_layout()
-    buf = io.BytesIO()
-    plt.savefig(buf, format="png", facecolor=bg, bbox_inches="tight", dpi=130)
-    buf.seek(0)
-    res = Image.open(buf).copy()
-    plt.close()
-    return res
+        draw = D.Draw(result_img)
+        draw.rectangle([x1,y1,x2,y2], outline=(0,255,0), width=3)
+        return result_img, ("uPAD ANALYSIS"+chr(10)+"━"*22+chr(10)+
+            "R:"+str(round(R,1))+" G:"+str(round(G,1))+" B:"+str(round(B,1))+chr(10)+
+            "Orange Score: "+str(round(R-B,1))+chr(10)+"━"*22+chr(10)+
+            "CREATININE: "+str(c)+" mg/dL"+chr(10)+"CKD STAGE: "+s+chr(10)+
+            "ACTION: "+a+chr(10)+"Confirm: Heska Element HT5")
+    except Exception as e: return None, "Error: "+str(e)
 
 def analyze_piv_csv(file, theme="White"):
-    if file is None: return None, None, None, None, "Upload PIV CSV first."
+    if file is None: return None,None,None,None,"Upload a PIV CSV file first."
     try:
         df = pd.read_csv(file.name)
-        cols = [c.lower().strip() for c in df.columns]; df.columns = cols
+        cols = [c.lower().strip() for c in df.columns]
+        df.columns = cols
         num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
-        if not num_cols: return None, None, None, None, "No numeric columns."
-        bg = "#fff" if theme=="White" else "#0a1628"
+        if not num_cols: return None,None,None,None,"No numeric columns found."
+        bg = "#ffffff" if theme=="White" else "#0a1628"
         fg = "#1a202c" if theme=="White" else "white"
         gc = "#e2e8f0" if theme=="White" else "#2d4a8a"
         ac = "#4a5568" if theme=="White" else "#a8b2d8"
         pb = "#f7fafc" if theme=="White" else "#132340"
         x = np.arange(len(df))
         vc = next((c for c in cols if any(k in c for k in ["vel","speed","v_mag"])), num_cols[0] if num_cols else None)
-        sc2 = next((c for c in cols if any(k in c for k in ["shear","stress","tau","wss"])), num_cols[1] if len(num_cols)>1 else None)
+        sc = next((c for c in cols if any(k in c for k in ["shear","stress","tau","wss"])), num_cols[1] if len(num_cols)>1 else None)
         tc = next((c for c in cols if "time" in c or "frame" in c), None)
         xv = df[tc] if tc else x
+        def mk(fn, title):
+            fig2,ax = plt.subplots(figsize=(8,5))
+            fig2.patch.set_facecolor(bg); ax.set_facecolor(pb)
+            fn(ax)
+            ax.set_title(title, color=fg, fontweight="bold", fontsize=13, pad=8)
+            ax.tick_params(colors=ac, labelsize=10)
+            ax.grid(True, alpha=0.3, color=gc, linestyle="--")
+            for sp in ["top","right"]: ax.spines[sp].set_visible(False)
+            for sp in ["bottom","left"]: ax.spines[sp].set_color(gc)
+            plt.tight_layout()
+            buf2=io.BytesIO(); plt.savefig(buf2,format="png",facecolor=bg,bbox_inches="tight",dpi=130); buf2.seek(0)
+            res=Image.open(buf2).copy(); plt.close(); return res
         def pv(ax):
             if vc:
-                ax.plot(xv, df[vc], color="#c1121f", linewidth=2.5, marker="o", markersize=5)
-                ax.fill_between(xv, df[vc], alpha=0.15, color="#c1121f")
-                ax.axhline(y=2.0, color="#f59e0b", linestyle="--", linewidth=2, label="Risk 2.0 m/s")
-                ax.set_ylabel("Velocity (m/s)", color=ac)
-                ax.legend(fontsize=9, labelcolor=fg, facecolor=pb)
+                ax.plot(xv,df[vc],color="#e63946",linewidth=2.5,marker="o",markersize=5)
+                ax.fill_between(xv,df[vc],alpha=0.2,color="#e63946")
+                ax.axhline(y=2.0,color="#f59e0b",linestyle="--",linewidth=2,label="Risk: 2.0 m/s")
+                ax.set_ylabel("Velocity (m/s)",color=ac,fontsize=11)
+                ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
+                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
         def ps(ax):
-            if sc2:
+            if sc:
                 xp = xv.values if tc else x
-                ax.plot(xp, df[sc2], color="#0057a8", linewidth=2.5, marker="s", markersize=5)
-                ax.fill_between(xp, df[sc2], alpha=0.15, color="#0057a8")
-                ax.axhline(y=5, color="#f59e0b", linestyle="--", linewidth=2, label="Caution 5 Pa")
-                ax.axhline(y=10, color="#c1121f", linestyle="--", linewidth=2, label="Risk 10 Pa")
-                ax.set_ylabel("Shear (Pa)", color=ac)
-                ax.legend(fontsize=9, labelcolor=fg, facecolor=pb)
+                ax.plot(xp,df[sc],color="#4361ee",linewidth=2.5,marker="s",markersize=5)
+                ax.fill_between(xp,df[sc],alpha=0.2,color="#4361ee")
+                ax.axhline(y=5,color="#f59e0b",linestyle="--",linewidth=2,label="Caution: 5 Pa")
+                ax.axhline(y=10,color="#e63946",linestyle="--",linewidth=2,label="High risk: 10 Pa")
+                ax.set_ylabel("Shear Stress (Pa)",color=ac,fontsize=11)
+                ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
+                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
         def psc(ax):
-            if vc and sc2:
-                s3 = ax.scatter(df[vc], df[sc2], c=x, cmap="RdYlGn_r", s=90, edgecolors=fg, linewidth=0.5, zorder=5)
-                cb = plt.colorbar(s3, ax=ax, label="Time")
-                cb.ax.yaxis.label.set_color(fg); cb.ax.tick_params(colors=ac)
-                ax.axvline(x=2.0, color="#f59e0b", linestyle="--", linewidth=2)
-                ax.axhline(y=10, color="#c1121f", linestyle="--", linewidth=2)
-                ax.set_xlabel("Velocity (m/s)", color=ac); ax.set_ylabel("Shear (Pa)", color=ac)
+            if vc and sc:
+                s2 = ax.scatter(df[vc],df[sc],c=x,cmap="RdYlGn_r",s=90,edgecolors=fg,linewidth=0.5,zorder=5)
+                cb=plt.colorbar(s2,ax=ax,label="Time"); cb.ax.yaxis.label.set_color(fg); cb.ax.tick_params(colors=ac)
+                ax.axvline(x=2.0,color="#f59e0b",linestyle="--",linewidth=2,label="Vel risk")
+                ax.axhline(y=10,color="#e63946",linestyle="--",linewidth=2,label="Shear risk")
+                ax.set_xlabel("Velocity (m/s)",color=ac,fontsize=11)
+                ax.set_ylabel("Shear Stress (Pa)",color=ac,fontsize=11)
+                ax.legend(fontsize=9,labelcolor=fg,facecolor=pb)
         def psum(ax):
-            ax.axis("off"); risk = []
-            st = "CLINICAL SUMMARY" + chr(10) + "="*20 + chr(10) + chr(10)
+            ax.axis("off"); risk=[]
+            st="CLINICAL SUMMARY"+chr(10)+"━"*20+chr(10)+chr(10)
             for col in num_cols[:3]:
-                mn = round(df[col].mean(), 3); mx = round(df[col].max(), 3)
-                st += col[:14] + ":" + chr(10) + "  Mean: " + str(mn) + chr(10) + "  Max:  " + str(mx) + chr(10) + chr(10)
-                if "vel" in col and mx > 2.0: risk.append("HIGH VELOCITY")
-                if "shear" in col and mx > 10: risk.append("HIGH SHEAR")
-            bc = "#c1121f" if risk else "#2ecc71"
-            st += "="*20 + chr(10) + ("OVERALL: HIGH RISK" if risk else "OVERALL: LOW RISK")
-            ax.text(0.05, 0.97, st, transform=ax.transAxes, color=fg, fontsize=10, va="top",
-                fontfamily="monospace", bbox=dict(boxstyle="round,pad=0.8", facecolor=pb, edgecolor=bc, linewidth=2.5))
-        i1 = mk_chart(pv, "Velocity Profile", bg, fg, gc, ac, pb)
-        i2 = mk_chart(ps, "Wall Shear Stress", bg, fg, gc, ac, pb)
-        i3 = mk_chart(psc, "Velocity vs Shear", bg, fg, gc, ac, pb)
-        i4 = mk_chart(psum, "Clinical Summary", bg, fg, gc, ac, pb)
-        ai = ""
+                mn=round(df[col].mean(),3); mx=round(df[col].max(),3)
+                st+=col[:14]+":"+chr(10)+"  Mean: "+str(mn)+chr(10)+"  Max:  "+str(mx)+chr(10)+chr(10)
+                if "vel" in col and mx>2.0: risk.append("HIGH VELOCITY (>2.0 m/s)")
+                if "shear" in col and mx>10: risk.append("HIGH SHEAR (>10 Pa)")
+            st+="━"*20+chr(10)
+            if risk:
+                st+="RISK FLAGS:"+chr(10)+"".join(["  ⚠ "+r+chr(10) for r in risk])
+                st+="OVERALL: HIGH RISK"; bc="#e63946"
+            else:
+                st+="OVERALL: LOW RISK"; bc="#2ecc71"
+            ax.text(0.05,0.97,st,transform=ax.transAxes,color=fg,fontsize=10,va="top",fontfamily="monospace",
+                bbox=dict(boxstyle="round,pad=0.8",facecolor=pb,edgecolor=bc,linewidth=2.5))
+        i1=mk(pv,"Velocity Profile"); i2=mk(ps,"Wall Shear Stress")
+        i3=mk(psc,"Velocity vs Shear"); i4=mk(psum,"Clinical Summary")
+        ai=""
         if GROQ_KEY:
             try:
-                client = Groq(api_key=GROQ_KEY)
-                resp = client.chat.completions.create(
-                    model="llama-3.3-70b-versatile",
-                    messages=[{"role":"system","content":"PIV expert SJSU CardioLab."},
-                    {"role":"user","content":"PIV from 27mm SJM Regent:" + chr(10) + df.describe().to_string()[:500]}],
-                    max_tokens=250
-                )
-                ai = chr(10) + "AI: " + resp.choices[0].message.content
+                client=Groq(api_key=GROQ_KEY)
+                resp=client.chat.completions.create(model="llama-3.3-70b-versatile",
+                    messages=[{"role":"system","content":"PIV expert SJSU CardioLab. Analyze PIV stats give clinical interpretation."},
+                    {"role":"user","content":"PIV data from 27mm SJM Regent MHV 70bpm 5L/min:"+chr(10)+df.describe().to_string()[:600]}],max_tokens=300)
+                ai=chr(10)+"━"*20+chr(10)+"AI:"+chr(10)+resp.choices[0].message.content
             except: pass
-        return i1, i2, i3, i4, "PIV: " + str(len(df)) + " rows" + ai
-    except Exception as e:
-        return None, None, None, None, "Error: " + str(e)
+        return i1,i2,i3,i4,"PIV LOADED: "+str(len(df))+" rows | "+", ".join(df.columns.tolist())+ai
+    except Exception as e: return None,None,None,None,"Error: "+str(e)
 
 def analyze_tgt_csv(file, theme="White"):
-    if file is None: return None, None, None, None, "Upload TGT CSV first."
+    if file is None: return None,None,None,None,"Upload a TGT CSV file first."
     try:
         df = pd.read_csv(file.name)
-        cols = [c.lower().strip() for c in df.columns]; df.columns = cols
+        cols = [c.lower().strip() for c in df.columns]
+        df.columns = cols
         num_cols = df.select_dtypes(include=[np.number]).columns.tolist()
-        bg = "#fff" if theme=="White" else "#0a1628"
-        fg = "#1a202c" if theme=="White" else "white"
-        gc = "#e2e8f0" if theme=="White" else "#2d4a8a"
-        ac = "#4a5568" if theme=="White" else "#a8b2d8"
-        pb = "#f7fafc" if theme=="White" else "#132340"
-        tc = next((c for c in cols if "time" in c or "min" in c), None)
-        tatc = next((c for c in cols if "tat" in c), num_cols[0] if num_cols else None)
-        pfc = next((c for c in cols if "pf" in c), num_cols[1] if len(num_cols)>1 else None)
-        hc = next((c for c in cols if "hemo" in c), num_cols[2] if len(num_cols)>2 else None)
-        plc = next((c for c in cols if "platelet" in c or "plt" in c), num_cols[3] if len(num_cols)>3 else None)
-        def mk2(dc, color, yl, lim, ll, title, bar=False):
-            def fn(ax):
-                if dc and dc in df.columns:
-                    xp = df[tc].values if tc else range(len(df))
-                    yp = df[dc].values
-                    if bar:
-                        bs = ax.bar(range(len(yp)), yp, color=color, alpha=0.85, edgecolor=bg, width=0.6)
-                        for b, v in zip(bs, yp):
-                            ax.text(b.get_x()+b.get_width()/2, b.get_height()+0.5, str(round(v,1)),
-                                ha="center", va="bottom", color=fg, fontsize=10, fontweight="bold")
-                    else:
-                        ax.plot(xp, yp, color=color, linewidth=3, marker="o", markersize=8)
-                        ax.fill_between(xp, yp, alpha=0.15, color=color)
-                        for xi, yi in zip(xp, yp):
-                            ax.annotate(str(round(yi,1)), (xi, yi), textcoords="offset points",
-                                xytext=(0,10), ha="center", color=fg, fontsize=10, fontweight="bold")
-                    ax.axhline(y=lim, color="#f59e0b", linestyle="--", linewidth=2.5, label=ll)
-                    ax.legend(fontsize=10, labelcolor=fg, facecolor=pb)
-                    ax.set_ylabel(yl, color=ac)
-                    mv = round(float(np.max(yp)), 2)
-                    ax.set_title(title + chr(10) + "Max: " + str(mv) + " - " + ("HIGH" if mv>lim else "NORMAL"),
-                        color=fg, fontweight="bold", fontsize=12)
-            return mk_chart(fn, title, bg, fg, gc, ac, pb)
-        i1 = mk2(tatc, "#c1121f", "TAT (ng/mL)", 8, "Normal: 8", "TAT")
-        i2 = mk2(pfc, "#0057a8", "PF1.2", 2.0, "Normal: 2.0", "PF1.2")
-        i3 = mk2(hc, "#2ecc71", "Free Hgb (mg/L)", 20, "Normal: 20", "Free Hemoglobin", bar=True)
-        i4 = mk2(plc, "#e8a020", "Platelets", 150, "Normal>150", "Platelets")
-        ai = ""
+        bg="#ffffff" if theme=="White" else "#0a1628"
+        fg="#1a202c" if theme=="White" else "white"
+        gc="#e2e8f0" if theme=="White" else "#2d4a8a"
+        ac="#4a5568" if theme=="White" else "#a8b2d8"
+        pb="#f7fafc" if theme=="White" else "#132340"
+        tc=next((c for c in cols if "time" in c or "min" in c),None)
+        tatc=next((c for c in cols if "tat" in c),num_cols[0] if num_cols else None)
+        pfc=next((c for c in cols if "pf" in c),num_cols[1] if len(num_cols)>1 else None)
+        hc=next((c for c in cols if "hemo" in c or "hgb" in c),num_cols[2] if len(num_cols)>2 else None)
+        plc=next((c for c in cols if "platelet" in c or "plt" in c),num_cols[3] if len(num_cols)>3 else None)
+        xv=df[tc] if tc else range(len(df))
+        def mk(dc,color,yl,lim,ll,title,bar=False):
+            fig2,ax=plt.subplots(figsize=(8,5))
+            fig2.patch.set_facecolor(bg); ax.set_facecolor(pb)
+            if dc and dc in df.columns:
+                xp=df[tc].values if tc else range(len(df)); yp=df[dc].values
+                if bar:
+                    bs=ax.bar(range(len(yp)),yp,color=color,alpha=0.85,edgecolor=bg,width=0.6)
+                    for b,v in zip(bs,yp): ax.text(b.get_x()+b.get_width()/2,b.get_height()+0.5,str(round(v,1)),ha="center",va="bottom",color=fg,fontsize=10,fontweight="bold")
+                else:
+                    ax.plot(xp,yp,color=color,linewidth=3,marker="o",markersize=8)
+                    ax.fill_between(xp,yp,alpha=0.2,color=color)
+                    for xi,yi in zip(xp,yp): ax.annotate(str(round(yi,1)),(xi,yi),textcoords="offset points",xytext=(0,10),ha="center",color=fg,fontsize=10,fontweight="bold")
+                ax.axhline(y=lim,color="#f59e0b",linestyle="--",linewidth=2.5,label=ll)
+                ax.legend(fontsize=10,labelcolor=fg,facecolor=pb)
+                ax.set_ylabel(yl,color=ac,fontsize=11); ax.set_xlabel(tc or "Sample",color=ac,fontsize=11)
+                mv=round(float(np.max(yp)),2); st="HIGH" if mv>lim else "NORMAL"
+                ax.set_title(title+chr(10)+"Max: "+str(mv)+"  Status: "+st,color=fg,fontweight="bold",fontsize=12)
+            ax.tick_params(colors=ac,labelsize=10); ax.grid(True,alpha=0.3,color=gc,linestyle="--")
+            for sp in ["top","right"]: ax.spines[sp].set_visible(False)
+            for sp in ["bottom","left"]: ax.spines[sp].set_color(gc)
+            plt.tight_layout()
+            buf2=io.BytesIO(); plt.savefig(buf2,format="png",facecolor=bg,bbox_inches="tight",dpi=130); buf2.seek(0)
+            res=Image.open(buf2).copy(); plt.close(); return res
+        i1=mk(tatc,"#e63946","TAT (ng/mL)",8,"Normal: 8 ng/mL","Thrombin-Antithrombin TAT")
+        i2=mk(pfc,"#4361ee","PF1.2 (nmol/L)",2.0,"Normal: 2.0","Prothrombin Fragment PF1.2")
+        i3=mk(hc,"#2ecc71","Free Hemoglobin (mg/L)",20,"Normal: 20 mg/L","Free Hemoglobin Hemolysis",bar=True)
+        i4=mk(plc,"#e67e22","Platelet Count (10³/μL)",150,"Normal min: 150","Platelet Count")
+        ai=""
         if GROQ_KEY:
             try:
-                client = Groq(api_key=GROQ_KEY)
-                resp = client.chat.completions.create(
-                    model="llama-3.3-70b-versatile",
-                    messages=[{"role":"system","content":"Hematology expert. Thrombogenicity risk."},
-                    {"role":"user","content":"TGT:" + chr(10) + df.describe().to_string()[:500]}],
-                    max_tokens=250
-                )
-                ai = chr(10) + "AI: " + resp.choices[0].message.content
+                client=Groq(api_key=GROQ_KEY)
+                resp=client.chat.completions.create(model="llama-3.3-70b-versatile",
+                    messages=[{"role":"system","content":"Hematology expert SJSU CardioLab. Analyze TGT data give thrombogenicity risk LOW MODERATE or HIGH. Normal: TAT<8, PF1.2<2.0, Hemo<20, Plt>150."},
+                    {"role":"user","content":"TGT from 27mm SJM Regent MHV:"+chr(10)+df.describe().to_string()[:600]}],max_tokens=300)
+                ai=chr(10)+"━"*20+chr(10)+"AI:"+chr(10)+resp.choices[0].message.content
             except: pass
-        return i1, i2, i3, i4, "TGT: " + str(len(df)) + " rows" + ai
-    except Exception as e:
-        return None, None, None, None, "Error: " + str(e)
+        return i1,i2,i3,i4,"TGT LOADED: "+str(len(df))+" rows | "+", ".join(df.columns.tolist())+ai
+    except Exception as e: return None,None,None,None,"Error: "+str(e)
 
 def generate_image(prompt):
-    if not prompt.strip(): return None, "Enter description.", ""
-    if not HF_TOKEN: return None, "Add HF_TOKEN to Space secrets.", ""
+    if not prompt.strip(): return None,"Enter description.","";
+    if not HF_TOKEN: return None,"Add HF_TOKEN to Space secrets.","";
     try:
-        enhanced, desc = prompt, ""
+        enhanced,desc=prompt,""
         if GROQ_KEY:
             try:
-                client = Groq(api_key=GROQ_KEY)
-                resp = client.chat.completions.create(
-                    model="llama-3.3-70b-versatile",
+                client=Groq(api_key=GROQ_KEY)
+                resp=client.chat.completions.create(model="llama-3.3-70b-versatile",
                     messages=[{"role":"system","content":"Format: DESCRIPTION: [2 sentences] PROMPT: [detailed image prompt]"},
-                    {"role":"user","content":"Biomedical image: " + prompt}],
-                    max_tokens=200
-                )
-                full = resp.choices[0].message.content
+                    {"role":"user","content":"Biomedical image for CardioLab: "+prompt}],max_tokens=200)
+                full=resp.choices[0].message.content
                 if "DESCRIPTION:" in full and "PROMPT:" in full:
-                    desc = full.split("DESCRIPTION:")[1].split("PROMPT:")[0].strip()
-                    enhanced = full.split("PROMPT:")[1].strip()
+                    desc=full.split("DESCRIPTION:")[1].split("PROMPT:")[0].strip()
+                    enhanced=full.split("PROMPT:")[1].strip()
             except: pass
-        headers = {"Authorization": "Bearer " + HF_TOKEN, "Content-Type": "application/json"}
-        for url in [
-            "https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell",
-            "https://router.huggingface.co/hf-inference/models/stabilityai/stable-diffusion-xl-base-1.0"
-        ]:
+        headers={"Authorization":"Bearer "+HF_TOKEN,"Content-Type":"application/json"}
+        for url in ["https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell",
+                    "https://router.huggingface.co/hf-inference/models/stabilityai/stable-diffusion-xl-base-1.0"]:
             try:
-                r = requests.post(url, headers=headers,
-                    json={"inputs":enhanced,"parameters":{"num_inference_steps":8}}, timeout=60)
-                if r.status_code == 200:
-                    return Image.open(io.BytesIO(r.content)), "Generated!", desc
+                r=requests.post(url,headers=headers,json={"inputs":enhanced,"parameters":{"num_inference_steps":8}},timeout=60)
+                if r.status_code==200: return Image.open(io.BytesIO(r.content)),"Generated!",desc
             except: continue
-        return None, "Models busy.", desc
-    except Exception as e:
-        return None, "Error: " + str(e), ""
+        return None,"Models busy. Try again.",desc
+    except Exception as e: return None,"Error: "+str(e),""
 
-def piv_manual(v, s, h):
-    vr = "HIGH-stenosis" if float(v) > 2.0 else "NORMAL"
-    sr = "HIGH-thrombosis" if float(s) > 10 else "ELEVATED" if float(s) > 5 else "NORMAL"
-    return "Velocity: " + str(v) + " m/s - " + vr + chr(10) + "Shear: " + str(s) + " Pa - " + sr + chr(10) + "HR: " + str(h) + " bpm"
+def piv_manual(v,s,h):
+    vr="HIGH-stenosis" if float(v)>2.0 else "NORMAL"
+    sr="HIGH-thrombosis" if float(s)>10 else "ELEVATED" if float(s)>5 else "NORMAL"
+    return "Velocity: "+str(v)+" - "+vr+chr(10)+"Shear: "+str(s)+" - "+sr+chr(10)+"HR: "+str(h)+" bpm"
 
-def tgt_manual(t, p, h, pl, tm):
-    risk = sum([float(t)>15, float(p)>2.0, float(h)>50, float(pl)<150])
-    return ("TAT:" + str(t) + " PF1.2:" + str(p) + chr(10) +
-        "Hemo:" + str(h) + " Plt:" + str(pl) + chr(10) +
-        "RESULT: " + ("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK"))
+def tgt_manual(t,p,h,pl,tm):
+    risk=sum([float(t)>15,float(p)>2.0,float(h)>50,float(pl)<150])
+    return "TAT:"+str(t)+" PF1.2:"+str(p)+chr(10)+"Hemo:"+str(h)+" Plt:"+str(pl)+chr(10)+"Time:"+str(tm)+"min"+chr(10)+"RESULT: "+("HIGH RISK" if risk>=3 else "MODERATE" if risk>=2 else "LOW RISK")
 
-# ── BUILD UI ───────────────────────────────────────────────
-with gr.Blocks(title="CardioLab AI v39 - SJSU", css=CSS) as demo:
-    gr.HTML(HEADER)
-    gr.HTML(STATUS_BANNER)
+# ── UI ─────────────────────────────────────────────────────────────
+with gr.Blocks(title="CardioLab AI", css=CSS) as demo:
+    gr.HTML('''<div style="background:linear-gradient(135deg,#1a237e,#b71c1c);padding:20px;text-align:center;border-radius:12px 12px 0 0"><div style="font-size:2.5em;font-weight:900;color:#fff;letter-spacing:3px">CardioLab AI</div></div>''')
 
     with gr.Tabs():
 
         with gr.Tab("Chat"):
+            gr.Markdown("### Chat with memory — saves conversations like ChatGPT")
             with gr.Row():
-                with gr.Column(scale=1, min_width=200):
-                    gr.HTML(SIDEBAR_HTML)
-                    new_chat_btn = gr.Button("New Chat", variant="secondary")
-                    session_dropdown = gr.Dropdown(choices=get_session_list(), label="Saved Sessions", interactive=True, allow_custom_value=True)
-                    load_btn = gr.Button("Load Session", variant="primary")
-                    session_name_box = gr.Textbox(placeholder="Session name...", label="", lines=1, container=False)
-                    with gr.Row():
-                        save_btn = gr.Button("Save", variant="primary", scale=2)
-                        delete_btn = gr.Button("Del", variant="secondary", scale=1)
-                    session_status = gr.Textbox(label="", lines=1, interactive=False, container=False)
-                with gr.Column(scale=4):
-                    chatbot = gr.Chatbot(label="", height=460, show_label=False, container=False)
+                with gr.Column(scale=3):
+                    chatbot = gr.Chatbot(label="", height=420)
                     with gr.Row():
-                        msg_box = gr.Textbox(
-                            placeholder="Ask anything — AI searches 16 SJSU papers + PubMed...",
-                            label="", lines=2, scale=4, container=False
-                        )
-                        with gr.Column(scale=1, min_width=160):
-                            chat_model_dd = gr.Radio(
-                                choices=list(CHAT_MODELS.keys()),
-                                value="Llama 3.3 70B (Best)", label="AI Model"
-                            )
+                        msg_box = gr.Textbox(placeholder="Ask about CardioLab research...", label="", lines=2, scale=4)
+                        with gr.Column(scale=1, min_width=80):
                             send_btn = gr.Button("Send", variant="primary")
                             clear_btn = gr.Button("Clear", variant="secondary")
-            send_btn.click(research_chat, inputs=[msg_box, chatbot, chat_model_dd], outputs=[msg_box, chatbot])
-            msg_box.submit(research_chat, inputs=[msg_box, chatbot, chat_model_dd], outputs=[msg_box, chatbot])
+                with gr.Column(scale=1, min_width=200):
+                    gr.Markdown("### Saved Sessions")
+                    session_dropdown = gr.Dropdown(
+                        choices=get_session_list(),
+                        label="Load a saved session",
+                        interactive=True
+                    )
+                    load_btn = gr.Button("Load Session", variant="primary")
+                    session_status = gr.Textbox(label="Status", lines=1, interactive=False)
+                    gr.Markdown("### Save Current Chat")
+                    session_name_box = gr.Textbox(label="Session name", placeholder="e.g. TGT Research May 2026")
+                    save_btn = gr.Button("Save Chat", variant="primary")
+                    delete_btn = gr.Button("Delete Session", variant="secondary")
+
+            send_btn.click(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
+            msg_box.submit(research_chat, inputs=[msg_box, chatbot], outputs=[msg_box, chatbot])
             clear_btn.click(lambda: ([], ""), outputs=[chatbot, msg_box])
-            new_chat_btn.click(new_chat, outputs=[chatbot, msg_box, session_status])
             save_btn.click(save_session, inputs=[chatbot, session_name_box], outputs=[session_status, session_dropdown])
             load_btn.click(load_session, inputs=session_dropdown, outputs=[chatbot, session_status])
             delete_btn.click(delete_session, inputs=session_dropdown, outputs=[session_status, session_dropdown])
 
         with gr.Tab("Voice"):
-            voice_chatbot = gr.Chatbot(label="", height=360, show_label=False)
+            voice_chatbot = gr.Chatbot(label="", height=320)
             audio_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Question")
             with gr.Row():
                 voice_btn = gr.Button("Ask by Voice", variant="primary")
@@ -1204,177 +432,93 @@ with gr.Blocks(title="CardioLab AI v39 - SJSU", css=CSS) as demo:
             voice_clear.click(lambda: [], outputs=voice_chatbot)
 
         with gr.Tab("Papers"):
-            gr.Markdown("### Search PubMed + Semantic Scholar + SJSU ScholarWorks")
             with gr.Row():
-                search_input = gr.Textbox(
-                    placeholder="e.g. bileaflet mechanical heart valve thrombogenicity hemodynamics",
-                    label="Research Topic", scale=3
-                )
-                search_model_dd = gr.Dropdown(
-                    choices=list(CHAT_MODELS.keys()),
-                    value="Llama 3.3 70B (Best)", label="AI Model", scale=1
-                )
+                search_input = gr.Textbox(placeholder="e.g. mechanical heart valve thrombogenicity", label="Research Topic", scale=4)
                 search_btn = gr.Button("Search", variant="primary", scale=1)
-            search_output = gr.Textbox(label="Results", lines=22)
-            search_btn.click(quick_search, inputs=[search_input, search_model_dd], outputs=search_output)
-            search_input.submit(quick_search, inputs=[search_input, search_model_dd], outputs=search_output)
+            search_output = gr.Textbox(label="Verified Results", lines=18)
+            search_btn.click(quick_search, inputs=search_input, outputs=search_output)
+            search_input.submit(quick_search, inputs=search_input, outputs=search_output)
 
         with gr.Tab("PIV CSV"):
+            gr.Markdown("### Upload PIV CSV — 4 separate charts + AI analysis")
             with gr.Row():
-                piv_file = gr.File(label="Upload PIV CSV", file_types=[".csv"], scale=3)
+                piv_file = gr.File(label="UPLOAD PIV CSV", file_types=[".csv"], scale=3)
                 piv_theme = gr.Radio(["White","Dark"], value="White", label="Theme", scale=1)
             piv_btn = gr.Button("Analyze PIV Data", variant="primary")
-            piv_result = gr.Textbox(label="AI Analysis", lines=4)
+            piv_result = gr.Textbox(label="AI Analysis", lines=5)
             with gr.Row():
-                piv_c1 = gr.Image(label="Velocity", type="pil")
+                piv_c1 = gr.Image(label="Velocity Profile", type="pil")
                 piv_c2 = gr.Image(label="Shear Stress", type="pil")
             with gr.Row():
-                piv_c3 = gr.Image(label="Vel vs Shear", type="pil")
+                piv_c3 = gr.Image(label="Velocity vs Shear", type="pil")
                 piv_c4 = gr.Image(label="Clinical Summary", type="pil")
-            piv_btn.click(analyze_piv_csv, inputs=[piv_file, piv_theme], outputs=[piv_c1, piv_c2, piv_c3, piv_c4, piv_result])
+            piv_btn.click(analyze_piv_csv, inputs=[piv_file,piv_theme], outputs=[piv_c1,piv_c2,piv_c3,piv_c4,piv_result])
 
         with gr.Tab("TGT CSV"):
+            gr.Markdown("### Upload TGT CSV — blood biomarker charts + thrombogenicity assessment")
             with gr.Row():
-                tgt_file = gr.File(label="Upload TGT CSV", file_types=[".csv"], scale=3)
+                tgt_file = gr.File(label="UPLOAD TGT CSV", file_types=[".csv"], scale=3)
                 tgt_theme = gr.Radio(["White","Dark"], value="White", label="Theme", scale=1)
             tgt_btn = gr.Button("Analyze TGT Data", variant="primary")
-            tgt_result = gr.Textbox(label="AI Assessment", lines=4)
+            tgt_result = gr.Textbox(label="AI Assessment", lines=5)
             with gr.Row():
-                tgt_c1 = gr.Image(label="TAT", type="pil")
-                tgt_c2 = gr.Image(label="PF1.2", type="pil")
+                tgt_c1 = gr.Image(label="TAT Over Time", type="pil")
+                tgt_c2 = gr.Image(label="PF1.2 Over Time", type="pil")
             with gr.Row():
-                tgt_c3 = gr.Image(label="Hemoglobin", type="pil")
-                tgt_c4 = gr.Image(label="Platelets", type="pil")
-            tgt_btn.click(analyze_tgt_csv, inputs=[tgt_file, tgt_theme], outputs=[tgt_c1, tgt_c2, tgt_c3, tgt_c4, tgt_result])
+                tgt_c3 = gr.Image(label="Free Hemoglobin", type="pil")
+                tgt_c4 = gr.Image(label="Platelet Count", type="pil")
+            tgt_btn.click(analyze_tgt_csv, inputs=[tgt_file,tgt_theme], outputs=[tgt_c1,tgt_c2,tgt_c3,tgt_c4,tgt_result])
 
-        with gr.Tab("uPAD"):
+        with gr.Tab("uPAD Photo"):
+            gr.Markdown("### Upload uPAD Photo — Instant CKD diagnosis")
             with gr.Row():
                 with gr.Column():
-                    photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=260)
+                    photo_input = gr.Image(label="Upload uPAD Photo", type="numpy", height=280)
                     analyze_btn = gr.Button("Analyze uPAD", variant="primary")
                 with gr.Column():
-                    photo_img = gr.Image(label="Detection Zone", type="pil", height=260)
-                    photo_text = gr.Textbox(label="CKD Result", lines=8)
+                    photo_img = gr.Image(label="Detection Zone", type="pil", height=280)
+                    photo_text = gr.Textbox(label="CKD Result", lines=10)
             analyze_btn.click(analyze_upad_photo, inputs=photo_input, outputs=[photo_img, photo_text])
-            with gr.Row():
-                r = gr.Number(label="R", value=210)
-                g = gr.Number(label="G", value=140)
-                b = gr.Number(label="B", value=80)
-                out3 = gr.Textbox(label="Manual Result", lines=3)
-            gr.Button("Analyze RGB", variant="secondary").click(
-                lambda r, g, b: (
-                    "Creatinine: " + str(max(0, round(0.02*(r-b)-0.5, 2))) + " mg/dL" + chr(10) +
-                    ("Normal" if max(0, round(0.02*(r-b)-0.5, 2)) < 1.2
-                     else "Borderline" if max(0, round(0.02*(r-b)-0.5, 2)) < 1.5
-                     else "CKD")
-                ),
-                inputs=[r, g, b], outputs=out3
-            )
 
         with gr.Tab("AI Image"):
             with gr.Row():
-                img_prompt = gr.Textbox(
-                    placeholder="e.g. 27mm bileaflet mechanical heart valve cross section",
-                    label="Describe image", lines=2, scale=4
-                )
+                img_prompt = gr.Textbox(placeholder="e.g. bileaflet heart valve | uPAD device | Arduino TGT", label="Describe image", lines=2, scale=4)
                 with gr.Column(scale=1):
                     img_btn = gr.Button("Generate", variant="primary")
                     img_status = gr.Textbox(label="Status", lines=1)
             img_desc = gr.Textbox(label="AI Description", lines=2, interactive=False)
-            img_output = gr.Image(label="Generated Image", type="pil", height=400)
-            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output, img_status, img_desc])
+            img_output = gr.Image(label="Generated Image", type="pil", height=380)
+            img_btn.click(generate_image, inputs=img_prompt, outputs=[img_output,img_status,img_desc])
 
         with gr.Tab("PIV Manual"):
             with gr.Row():
                 with gr.Column():
-                    v = gr.Number(label="Max Velocity m/s", value=1.8)
-                    s = gr.Number(label="Wall Shear Pa", value=6.5)
-                    h = gr.Number(label="Heart Rate bpm", value=72)
-                    piv_out = gr.Textbox(label="Result", lines=4)
-                    gr.Button("Analyze PIV", variant="primary").click(piv_manual, inputs=[v, s, h], outputs=piv_out)
+                    v=gr.Number(label="Max Velocity m/s",value=1.8)
+                    s=gr.Number(label="Wall Shear Stress Pa",value=6.5)
+                    h=gr.Number(label="Heart Rate bpm",value=72)
+                    piv_out=gr.Textbox(label="Result",lines=4)
+                    gr.Button("Analyze PIV",variant="primary").click(piv_manual,inputs=[v,s,h],outputs=piv_out)
 
         with gr.Tab("TGT Manual"):
             with gr.Row():
                 with gr.Column():
-                    t1 = gr.Number(label="TAT ng/mL", value=18)
-                    t2 = gr.Number(label="PF1.2", value=2.5)
-                    t3 = gr.Number(label="Hemoglobin mg/L", value=60)
-                    t4 = gr.Number(label="Platelets", value=140)
-                    t5 = gr.Number(label="Time min", value=40)
-                    out2 = gr.Textbox(label="Result", lines=6)
-                    gr.Button("Analyze TGT", variant="primary").click(tgt_manual, inputs=[t1, t2, t3, t4, t5], outputs=out2)
-
-        with gr.Tab("Protocol Generator"):
-            gr.Markdown("### Generate complete lab protocols from SJSU CardioLab knowledge")
-            with gr.Row():
-                with gr.Column(scale=1):
-                    proto_type = gr.Dropdown(
-                        choices=[
-                            "MCL Setup", "PIV Experiment",
-                            "Thrombogenicity Tester Blood Clotting Test",
-                            "uPAD Fabrication", "uPAD Creatinine Test",
-                            "FSI COMSOL Simulation", "Valve Testing"
-                        ],
-                        value="Thrombogenicity Tester Blood Clotting Test",
-                        label="Experiment Type"
-                    )
-                    proto_params = gr.Textbox(
-                        placeholder="e.g. 27mm SJM valve 70bpm porcine blood",
-                        label="Specific Parameters", lines=2
-                    )
-                    proto_btn = gr.Button("Generate Protocol", variant="primary")
-                with gr.Column(scale=2):
-                    proto_output = gr.Textbox(label="Generated Protocol", lines=28)
-            proto_btn.click(generate_protocol, inputs=[proto_type, proto_params], outputs=proto_output)
-
-        with gr.Tab("Report Writer"):
-            gr.Markdown("### Generate professional research reports from your data")
+                    t1=gr.Number(label="TAT ng/mL",value=18)
+                    t2=gr.Number(label="PF1.2",value=2.5)
+                    t3=gr.Number(label="Hemoglobin mg/L",value=60)
+                    t4=gr.Number(label="Platelets",value=140)
+                    t5=gr.Number(label="Time min",value=40)
+                    out2=gr.Textbox(label="Result",lines=6)
+                    gr.Button("Analyze TGT",variant="primary").click(tgt_manual,inputs=[t1,t2,t3,t4,t5],outputs=out2)
+
+        with gr.Tab("uPAD Manual"):
             with gr.Row():
-                with gr.Column(scale=1):
-                    report_exp = gr.Dropdown(
-                        choices=[
-                            "MCL PIV Flow Analysis", "TGT Thrombogenicity Study",
-                            "uPAD CKD Detection", "FSI Simulation Study",
-                            "Heart Valve Comparison"
-                        ],
-                        value="TGT Thrombogenicity Study", label="Study Type"
-                    )
-                    report_desc = gr.Textbox(
-                        placeholder="e.g. TGT with 27mm SJM bileaflet at 70bpm 150mL porcine blood",
-                        label="Experiment Description", lines=3
-                    )
-                    report_results = gr.Textbox(
-                        placeholder="e.g. TAT=12.3 ng/mL PF1.2=2.8 Hemo=45 Plt=142",
-                        label="Your Results", lines=2
-                    )
-                    report_btn = gr.Button("Generate Report", variant="primary")
-                with gr.Column(scale=2):
-                    report_output = gr.Textbox(label="Generated Report", lines=28)
-            report_btn.click(generate_report, inputs=[report_desc, report_exp, report_results], outputs=report_output)
-
-        with gr.Tab("Hypothesis Generator"):
-            gr.Markdown("### Generate testable research hypotheses for CardioLab projects")
-            with gr.Row():
-                with gr.Column(scale=1):
-                    hyp_area = gr.Dropdown(
-                        choices=[
-                            "Bileaflet MHV Thrombogenicity",
-                            "uPAD CKD Detection Accuracy",
-                            "PIV Flow Characterization",
-                            "FSI Simulation Validation",
-                            "Valve Design Comparison"
-                        ],
-                        value="Bileaflet MHV Thrombogenicity", label="Research Area"
-                    )
-                    hyp_findings = gr.Textbox(
-                        placeholder="Current observations from your experiments",
-                        label="Current Findings", lines=3
-                    )
-                    hyp_btn = gr.Button("Generate Hypotheses", variant="primary")
-                with gr.Column(scale=2):
-                    hyp_output = gr.Textbox(label="Research Hypotheses", lines=25)
-            hyp_btn.click(generate_hypothesis, inputs=[hyp_area, hyp_findings], outputs=hyp_output)
-
-    gr.HTML(FOOTER_HTML)
+                with gr.Column():
+                    r=gr.Number(label="R value",value=210)
+                    g=gr.Number(label="G value",value=140)
+                    b=gr.Number(label="B value",value=80)
+                    out3=gr.Textbox(label="Result",lines=4)
+                    gr.Button("Analyze",variant="primary").click(
+                        lambda r,g,b:"Creatinine: "+str(max(0,round(0.02*(r-b)-0.5,2)))+" mg/dL"+chr(10)+("Normal" if max(0,round(0.02*(r-b)-0.5,2))<1.2 else "Borderline" if max(0,round(0.02*(r-b)-0.5,2))<1.5 else "CKD Stage 2+"),
+                        inputs=[r,g,b],outputs=out3)
 
 demo.launch()