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nsankura commited on Apr 10

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0ee7aef

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Deploy Aquaculture Vision

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Files changed (12) hide show

.gitattributes +37 -0
Dockerfile +16 -0
README.md +9 -0
UAPB-logo.png +3 -0
ara-logo.png +3 -0
dr-ramena.png +3 -0
gsla-header-logo.png +3 -0
gsla-logo.png +3 -0
index.html +1511 -0
render.yaml +10 -0
requirements.txt +10 -0
server.py +726 -0

.gitattributes ADDED Viewed

	@@ -0,0 +1,37 @@

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

Dockerfile ADDED Viewed

	@@ -0,0 +1,16 @@

+FROM python:3.10-slim
+RUN useradd -m -u 1000 user
+ENV PATH="/home/user/.local/bin:$PATH"
+WORKDIR /app
+USER root
+RUN apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0 && rm -rf /var/lib/apt/lists/*
+USER user
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+COPY --chown=user . /app
+EXPOSE 7860
+CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "7860"]

README.md ADDED Viewed

	@@ -0,0 +1,9 @@

+---
+title: UAPB Aquaculture Vision
+emoji: 🐟
+colorFrom: yellow
+colorTo: gray
+sdk: docker
+app_port: 7860
+pinned: false
+---

UAPB-logo.png ADDED Viewed

Git LFS Details

SHA256: 13fac83f7b7a459b45f74ba4c844d7089dd3f96a85ef26efc20a3c9ab47a19bd
Pointer size: 130 Bytes
Size of remote file: 30 kB

ara-logo.png ADDED Viewed

Git LFS Details

SHA256: 63e3011aa5965f281f0ea9ab8019a93201ba021bb2b71e5b9ec23b02f03b2c51
Pointer size: 130 Bytes
Size of remote file: 36.9 kB

dr-ramena.png ADDED Viewed

Git LFS Details

SHA256: 77ffc3cd4eb1ba8d370a5ae2900e70cb62a5c684eb861f0f04c6a1d35521c757
Pointer size: 131 Bytes
Size of remote file: 474 kB

gsla-header-logo.png ADDED Viewed

Git LFS Details

SHA256: 19d89bd5aec37ee5d0ba8578cb9b19d27a9048c3aa6b5c405dabdfb2c7307271
Pointer size: 129 Bytes
Size of remote file: 7.13 kB

gsla-logo.png ADDED Viewed

Git LFS Details

SHA256: 19d89bd5aec37ee5d0ba8578cb9b19d27a9048c3aa6b5c405dabdfb2c7307271
Pointer size: 129 Bytes
Size of remote file: 7.13 kB

index.html ADDED Viewed

	@@ -0,0 +1,1511 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Department of Aquaculture and Fisheries</title>
+  <link rel="preconnect" href="https://fonts.googleapis.com">
+  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+  <link href="https://fonts.googleapis.com/css2?family=Oswald:wght@400;500;600;700&family=Open+Sans:wght@400;500;600;700&display=swap" rel="stylesheet">
+  <style>
+    :root {
+      --uapb-gold: #E8A317;
+      --uapb-gold-light: #F0B830;
+      --uapb-gold-dark: #D4950F;
+      --uapb-black: #1A1A2E;
+      --uapb-dark: #16213E;
+      --bg-primary: #FFFFFF;
+      --bg-secondary: #F8FAFC;
+      --bg-tertiary: #F1F5F9;
+      --text-primary: #0F172A;
+      --text-secondary: #475569;
+      --text-tertiary: #94A3B8;
+      --border-light: #E2E8F0;
+      --border-default: #CBD5E1;
+      --accent-blue: #0066FF;
+      --accent-green: #00C48C;
+      --accent-purple: #7B61FF;
+      --accent-orange: #FF6B35;
+      --shadow-sm: 0 1px 2px rgba(0,0,0,0.05);
+      --shadow-md: 0 4px 6px -1px rgba(0,0,0,0.1), 0 2px 4px -1px rgba(0,0,0,0.06);
+      --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.1), 0 4px 6px -2px rgba(0,0,0,0.05);
+      --radius-sm: 6px;
+      --radius-md: 10px;
+      --radius-lg: 16px;
+      --radius-full: 9999px;
+    }
+ .nav-wrapper {
+  position: relative;
+  overflow: hidden;
+  background: rgba(10, 15, 30, 0.65);
+}
+    * { box-sizing: border-box; margin: 0; padding: 0; }
+    body {
+      font-family: 'Open Sans', -apple-system, BlinkMacSystemFont, sans-serif;
+      background: var(--bg-primary);
+      color: var(--text-primary);
+      min-height: 100vh;
+      line-height: 1.6;
+    }
+    /* UAPB Header - Black with Gradient Fade */
+    .uapb-header {
+      background: linear-gradient(to right, var(--uapb-black) 0%, var(--uapb-black) 55%, #2d3748 100%);
+      border-bottom: 4px solid var(--uapb-gold);
+      box-shadow: 0 2px 10px rgba(0,0,0,0.3);
+    }
+    /* Line 59 - existing */
+}
+/* ← PASTE HERE starting at line 60 */
+/* GSLA Header */
+.gsla-header {
+  background: linear-gradient(to right, #1a3a4a 0%, #1a3a4a 55%, #2d5566 100%);
+  border-bottom: 4px solid #7BA7A0;
+  box-shadow: 0 2px 10px rgba(0,0,0,0.3);
+}
+.gsla-header .header-text h1 {
+  font-family: Oswald, sans-serif;
+  font-size: 1.8rem;
+  font-weight: 700;
+  color: var(--uapb-gold);
+  text-transform: uppercase;
+  letter-spacing: 1px;
+}
+.gsla-header .header-text p {
+  font-size: 1rem;
+  color: #C4A882;  /* ← bright white, fully visible */
+  margin-top: 6px;
+  font-weight: 300;
+  letter-spacing: 2px;
+  text-transform: uppercase;
+}
+/* ← Then line 62 continues with .header-content as normal */
+    .header-content {
+      max-width: 1400px;
+      margin: 0 auto;
+      padding: 0 24px;
+      display: flex;
+      align-items: center;
+      justify-content: space-between;
+      min-height: 170px;
+    }
+    .gsla-header .header-content {
+  min-height: 100px;   /* smaller than UAPB's 170px */
+}
+    .header-left {
+      display: flex;
+      align-items: center;
+      gap: 20px;
+    }
+    .dept-logo {
+      height: 100%;
+      max-height: 160px;
+      width: auto;
+      padding: 5px 0;
+    }
+    .header-text h1 {
+      font-family: 'Oswald', sans-serif;
+      font-size: 1.8rem;
+      font-weight: 700;
+      color: var(--uapb-gold);
+      text-transform: uppercase;
+      letter-spacing: 1px;
+    }
+    .header-text p {
+      font-size: 0.95rem;
+      color: rgba(255,255,255,0.7);
+      margin-top: 4px;
+    }
+    .header-right {
+      display: flex;
+      align-items: center;
+      gap: 20px;
+    }
+    .partner-logo {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      gap: 6px;
+      padding: 10px 16px;
+      text-decoration: none;
+      transition: all 0.2s ease;
+    }
+    .partner-logo:hover {
+      opacity: 0.85;
+    }
+    .partner-logo img {
+      height: 55px;
+      width: auto;
+    }
+    .partner-label {
+      font-size: 0.6rem;
+      color: rgba(255,255,255,0.7);
+      text-transform: uppercase;
+      letter-spacing: 0.5px;
+      font-weight: 600;
+    }
+    .status-badge {
+      display: flex;
+      align-items: center;
+      gap: 8px;
+      padding: 8px 16px;
+      background: rgba(232, 163, 23, 0.15);
+      border-radius: var(--radius-full);
+      font-size: 0.8rem;
+      color: rgba(255,255,255,0.8);
+    }
+    .status-dot {
+      width: 8px;
+      height: 8px;
+      border-radius: 50%;
+      background: #64748B;
+    }
+    .status-dot.online { background: var(--accent-green); box-shadow: 0 0 8px var(--accent-green); }
+    .status-dot.offline { background: #EF4444; }
+    /* Main Navigation - Gold Bar */
+    .main-nav
+ {
+ background: none;
+  position: relative;
+  overflow: hidden;
+    }
+    .nav-video-bg {
+  position: absolute;
+  top: 50%; left: 50%;
+  transform: translate(-50%, -50%);
+  min-width: 100%; min-height: 100%;
+  width: auto; height: auto;
+  z-index: 0;
+  opacity: 1;
+  object-fit: cover;
+  pointer-events: none;
+}
+.nav-content, .sub-nav-content { position: relative; z-index: 1; }
+    .nav-content {
+      max-width: 1400px;
+      margin: 0 auto;
+      padding: 0 24px;
+      display: flex;
+      align-items: center;
+    }
+    .nav-tab {
+      font-family: 'Oswald', sans-serif;
+      font-size: 1rem;
+      font-weight: 600;
+      text-transform: uppercase;
+      letter-spacing: 1px;
+      color: #FFFFFF;
+      padding: 16px 28px;
+      border: none;
+      background: none;
+      cursor: pointer;
+      transition: background 0.2s;
+      text-decoration: none;
+    }
+    .nav-tab:hover {
+      background: rgba(255,255,255,0.2);
+    }
+    .nav-tab.active {
+      background: rgba(255,255,255,0.25);
+    }
+    /* Sub Navigation for Species */
+   /* Sub Navigation for Species */
+.sub-nav {
+  background: none;
+  border-bottom: none;           /* ← remove the border line */
+  position: relative;
+  overflow: hidden;
+}
+   .sub-nav-content {
+      max-width: 1400px;
+      margin: 0 auto;
+      padding: 0 24px;
+      display: flex;
+      align-items: center;
+      gap: 8px;
+      min-height: 110px;
+    }
+    .sub-nav-tab {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      padding: 14px 24px;
+      font-size: 0.85rem;
+      font-weight: 600;
+      color: #FFFFFF;
+      text-decoration: none;
+      border: none;
+      background: none;
+      cursor: pointer;
+      transition: all 0.2s;
+      border-bottom: 3px solid transparent;
+    }
+    .sub-nav-tab:hover {
+      color: var(--uapb-gold-dark);
+      background: var(--bg-tertiary);
+    }
+    .sub-nav-tab.active {
+      color: var(--uapb-gold-dark);
+      border-bottom-color: var(--uapb-gold);
+      background: var(--bg-primary);
+    }
+    .sub-nav-tab .scientific-name {
+      font-weight: 700;
+      font-style: italic;
+    }
+    .sub-nav-tab .common-name {
+      font-size: 0.75rem;
+      font-weight: 400;
+      color: var(--text-tertiary);
+      margin-top: 2px;
+    }
+    .sub-nav-tab.active .common-name {
+      color: var(--text-secondary);
+    }
+    /* Tab Content */
+    .tab-content {
+      display: none;
+    }
+    .tab-content.active {
+      display: block;
+    }
+    /* Species Content */
+    .species-content {
+      display: none;
+    }
+    .species-content.active {
+      display: block;
+    }
+    /* Main Content */
+    .main-content {
+      max-width: 1400px;
+      margin: 0 auto;
+      padding: 32px 24px;
+    }
+    /* Page Header */
+    .page-header {
+      margin-bottom: 32px;
+    }
+    .page-header h2 {
+      font-family: 'Oswald', sans-serif;
+      font-size: 2rem;
+      font-weight: 700;
+      color: var(--text-primary);
+      text-transform: uppercase;
+      letter-spacing: 1px;
+      margin-bottom: 8px;
+    }
+    .page-header h2 span {
+      color: var(--uapb-gold-dark);
+    }
+    .page-header p {
+      font-size: 1rem;
+      color: var(--text-secondary);
+      max-width: 700px;
+    }
+    .species-badge {
+      display: inline-flex;
+      align-items: center;
+      gap: 8px;
+      padding: 10px 18px;
+      background: rgba(232, 163, 23, 0.1);
+      border: 1px solid var(--uapb-gold);
+      border-radius: var(--radius-full);
+      font-size: 0.9rem;
+      color: var(--uapb-gold-dark);
+      margin-top: 16px;
+    }
+    .species-badge em {
+      font-weight: 600;
+    }
+    /* Analysis Grid */
+    .analysis-grid {
+      display: grid;
+      grid-template-columns: 1fr 380px;
+      gap: 24px;
+    }
+    @media (max-width: 1100px) {
+      .analysis-grid { grid-template-columns: 1fr; }
+    }
+    /* Cards */
+    .card {
+      background: var(--bg-primary);
+      border: 1px solid var(--border-light);
+      border-radius: var(--radius-lg);
+      overflow: hidden;
+      box-shadow: var(--shadow-sm);
+    }
+    .card-header {
+      display: flex;
+      align-items: center;
+      justify-content: space-between;
+      padding: 16px 20px;
+      border-bottom: 1px solid var(--border-light);
+      background: var(--bg-secondary);
+    }
+    .card-title {
+      font-family: 'Oswald', sans-serif;
+      font-size: 0.9rem;
+      font-weight: 600;
+      color: var(--text-primary);
+      text-transform: uppercase;
+      letter-spacing: 0.5px;
+    }
+    .card-badge {
+      padding: 4px 10px;
+      background: rgba(232, 163, 23, 0.15);
+      border-radius: var(--radius-full);
+      font-size: 0.65rem;
+      font-weight: 600;
+      color: var(--uapb-gold-dark);
+      text-transform: uppercase;
+      letter-spacing: 0.5px;
+    }
+    .card-body {
+      padding: 20px;
+    }
+    /* Stats Grid */
+    .stats-grid {
+      display: grid;
+      grid-template-columns: repeat(4, 1fr);
+      gap: 16px;
+      margin-bottom: 24px;
+    }
+    @media (max-width: 1024px) { .stats-grid { grid-template-columns: repeat(2, 1fr); } }
+    @media (max-width: 640px) { .stats-grid { grid-template-columns: 1fr; } }
+    .stat-card {
+      background: var(--bg-primary);
+      border: 1px solid var(--border-light);
+      border-radius: var(--radius-lg);
+      padding: 20px;
+      border-top: 4px solid var(--uapb-gold);
+    }
+    .stat-label {
+      font-family: 'Oswald', sans-serif;
+      font-size: 0.7rem;
+      font-weight: 600;
+      color: var(--text-tertiary);
+      text-transform: uppercase;
+      letter-spacing: 1px;
+      margin-bottom: 8px;
+    }
+    .stat-value {
+      font-family: 'Oswald', sans-serif;
+      font-size: 2rem;
+      font-weight: 700;
+      color: var(--text-primary);
+    }
+    .stat-value .unit {
+      font-size: 0.9rem;
+      font-weight: 500;
+      color: var(--text-tertiary);
+      margin-left: 4px;
+    }
+    /* Charts Grid */
+    .charts-grid {
+      display: grid;
+      grid-template-columns: 1fr 1fr;
+      gap: 16px;
+      margin-bottom: 24px;
+    }
+    @media (max-width: 800px) { .charts-grid { grid-template-columns: 1fr; } }
+    .chart-placeholder {
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      height: 150px;
+      background: var(--bg-tertiary);
+      border-radius: var(--radius-md);
+      color: var(--text-tertiary);
+      font-size: 0.85rem;
+    }
+    .chart-body img {
+      width: 100%;
+      height: 150px;
+      object-fit: contain;
+      border-radius: var(--radius-md);
+      cursor: pointer;
+      transition: transform 0.2s;
+    }
+    .chart-body img:hover {
+      transform: scale(1.02);
+    }
+    /* Upload Zone */
+    .upload-zone {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      justify-content: center;
+      padding: 40px 20px;
+      border: 2px dashed var(--border-default);
+      border-radius: var(--radius-lg);
+      background: var(--bg-tertiary);
+      cursor: pointer;
+      transition: all 0.2s;
+    }
+    .upload-zone:hover, .upload-zone.drag-over {
+      border-color: var(--uapb-gold);
+      background: rgba(232, 163, 23, 0.05);
+    }
+    .upload-zone input { display: none; }
+    .upload-icon {
+      width: 56px;
+      height: 56px;
+      background: var(--uapb-gold);
+      border-radius: var(--radius-lg);
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      margin-bottom: 16px;
+    }
+    .upload-icon svg {
+      width: 28px;
+      height: 28px;
+      color: white;
+    }
+    .upload-zone h3 {
+      font-family: 'Oswald', sans-serif;
+      font-size: 1rem;
+      font-weight: 600;
+      text-transform: uppercase;
+      margin-bottom: 4px;
+    }
+    .upload-zone p {
+      font-size: 0.8rem;
+      color: var(--text-tertiary);
+    }
+    /* File Info & Progress */
+    .file-info {
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+      padding: 12px 16px;
+      background: var(--bg-tertiary);
+      border-radius: var(--radius-md);
+      margin-top: 16px;
+      font-size: 0.85rem;
+    }
+    .file-count { font-weight: 600; }
+    .file-size { color: var(--text-tertiary); font-family: monospace; font-size: 0.75rem; }
+    .progress-container { margin-top: 16px; }
+    .progress-bar {
+      height: 6px;
+      background: var(--bg-tertiary);
+      border-radius: var(--radius-full);
+      overflow: hidden;
+    }
+    .progress-fill {
+      height: 100%;
+      width: 0%;
+      background: var(--uapb-gold);
+      transition: width 0.3s;
+    }
+    .progress-text {
+      display: flex;
+      justify-content: space-between;
+      margin-top: 6px;
+      font-size: 0.7rem;
+      color: var(--text-tertiary);
+    }
+    /* Buttons */
+    .button-group {
+      display: flex;
+      gap: 12px;
+      margin-top: 20px;
+    }
+    .btn {
+      display: inline-flex;
+      align-items: center;
+      justify-content: center;
+      gap: 8px;
+      padding: 12px 24px;
+      font-family: 'Oswald', sans-serif;
+      font-size: 0.9rem;
+      font-weight: 600;
+      text-transform: uppercase;
+      letter-spacing: 1px;
+      border-radius: var(--radius-md);
+      border: none;
+      cursor: pointer;
+      transition: all 0.2s;
+    }
+    .btn:disabled { opacity: 0.5; cursor: not-allowed; }
+    .btn svg { width: 18px; height: 18px; }
+    .btn-primary {
+      background: var(--uapb-gold);
+      color: #FFFFFF;
+    }
+    .btn-primary:hover:not(:disabled) {
+      background: var(--uapb-gold-dark);
+    }
+    .btn-secondary {
+      background: var(--bg-tertiary);
+      color: var(--text-secondary);
+      border: 1px solid var(--border-default);
+    }
+    .btn-secondary:hover:not(:disabled) {
+      background: var(--bg-primary);
+      border-color: var(--uapb-gold);
+    }
+    /* Error Box */
+    .error-box {
+      padding: 14px;
+      background: #FEF2F2;
+      border-left: 4px solid #EF4444;
+      border-radius: var(--radius-md);
+      color: #DC2626;
+      font-size: 0.85rem;
+      margin-top: 16px;
+      display: none;
+    }
+    .error-box.visible { display: block; }
+    /* Results Section */
+    .section-header {
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+      margin-bottom: 16px;
+    }
+    .section-title {
+      font-family: 'Oswald', sans-serif;
+      font-size: 1.1rem;
+      font-weight: 600;
+      text-transform: uppercase;
+      letter-spacing: 0.5px;
+    }
+    .section-count {
+      font-size: 0.8rem;
+      color: var(--text-tertiary);
+    }
+    .results-grid {
+      display: grid;
+      grid-template-columns: repeat(auto-fill, minmax(300px, 1fr));
+      gap: 16px;
+    }
+    .result-card {
+      background: var(--bg-primary);
+      border: 1px solid var(--border-light);
+      border-radius: var(--radius-lg);
+      overflow: hidden;
+      transition: all 0.2s;
+    }
+    .result-card:hover {
+      box-shadow: var(--shadow-lg);
+    }
+    .result-header {
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+      padding: 12px 16px;
+      background: var(--bg-secondary);
+      border-bottom: 1px solid var(--border-light);
+    }
+    .result-filename {
+      font-size: 0.8rem;
+      font-weight: 600;
+      max-width: 140px;
+      overflow: hidden;
+      text-overflow: ellipsis;
+      white-space: nowrap;
+    }
+    .result-stats {
+      display: flex;
+      gap: 6px;
+    }
+    .result-stat {
+      padding: 3px 8px;
+      background: var(--bg-primary);
+      border: 1px solid var(--border-light);
+      border-radius: var(--radius-full);
+      font-size: 0.65rem;
+    }
+    .result-stat strong {
+      color: var(--uapb-gold-dark);
+    }
+    .result-image {
+      position: relative;
+      cursor: pointer;
+    }
+    .result-image img {
+      display: block;
+      width: 100%;
+      height: 180px;
+      object-fit: cover;
+    }
+    .click-hint {
+      position: absolute;
+      bottom: 8px;
+      right: 8px;
+      background: rgba(0,0,0,0.7);
+      color: white;
+      padding: 4px 8px;
+      border-radius: 4px;
+      font-size: 0.6rem;
+      opacity: 0;
+      transition: opacity 0.2s;
+    }
+    .result-image:hover .click-hint { opacity: 1; }
+    .result-data {
+      padding: 10px 16px;
+      background: var(--bg-tertiary);
+      font-family: monospace;
+      font-size: 0.6rem;
+      color: var(--text-secondary);
+      max-height: 50px;
+      overflow-y: auto;
+    }
+    /* Extension Page Styles */
+    .profile-section {
+      display: grid;
+      grid-template-columns: 300px 1fr;
+      gap: 32px;
+      margin-bottom: 40px;
+    }
+    @media (max-width: 900px) {
+      .profile-section { grid-template-columns: 1fr; }
+    }
+    .profile-card {
+      background: var(--bg-primary);
+      border: 1px solid var(--border-light);
+      border-radius: var(--radius-lg);
+      padding: 32px;
+      text-align: center;
+      border-top: 4px solid var(--uapb-gold);
+    }
+    .profile-avatar {
+      width: 130px;
+      height: 130px;
+      background: var(--uapb-gold);
+      border-radius: 50%;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      margin: 0 auto 20px;
+      font-family: 'Oswald', sans-serif;
+      font-size: 3rem;
+      font-weight: 700;
+      color: white;
+      overflow: hidden;
+      border: 4px solid var(--uapb-gold);
+      box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+    }
+    .profile-avatar img {
+      width: 100%;
+      height: 100%;
+      object-fit: cover;
+    }
+    .profile-name {
+      font-family: 'Oswald', sans-serif;
+      font-size: 1.4rem;
+      font-weight: 700;
+      color: var(--text-primary);
+      margin-bottom: 8px;
+    }
+    .profile-title {
+      font-size: 0.9rem;
+      color: var(--uapb-gold-dark);
+      font-weight: 600;
+      margin-bottom: 4px;
+    }
+    .profile-dept {
+      font-size: 0.85rem;
+      color: var(--text-secondary);
+      margin-bottom: 20px;
+    }
+    .profile-links {
+      display: flex;
+      flex-direction: column;
+      gap: 8px;
+    }
+    .profile-link {
+      padding: 10px 16px;
+      background: var(--bg-tertiary);
+      border-radius: var(--radius-md);
+      font-size: 0.85rem;
+      color: var(--text-secondary);
+      text-decoration: none;
+      transition: all 0.2s;
+    }
+    .profile-link:hover {
+      background: var(--uapb-gold);
+      color: #FFFFFF;
+    }
+    .research-areas {
+      display: grid;
+      grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
+      gap: 20px;
+    }
+    .research-card {
+      background: var(--bg-primary);
+      border: 1px solid var(--border-light);
+      border-radius: var(--radius-lg);
+      padding: 24px;
+      transition: all 0.2s;
+    }
+    .research-card:hover {
+      box-shadow: var(--shadow-md);
+      border-color: var(--uapb-gold);
+    }
+    .research-icon {
+      width: 48px;
+      height: 48px;
+      background: rgba(232, 163, 23, 0.1);
+      border-radius: var(--radius-md);
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      margin-bottom: 16px;
+      color: var(--uapb-gold-dark);
+    }
+    .research-card h4 {
+      font-family: 'Oswald', sans-serif;
+      font-size: 1rem;
+      font-weight: 600;
+      margin-bottom: 8px;
+      color: var(--text-primary);
+    }
+    .research-card p {
+      font-size: 0.85rem;
+      color: var(--text-secondary);
+      line-height: 1.6;
+    }
+    /* Modal */
+    .modal-overlay {
+      display: none;
+      position: fixed;
+      top: 0; left: 0; right: 0; bottom: 0;
+      background: rgba(0,0,0,0.9);
+      z-index: 1000;
+      align-items: center;
+      justify-content: center;
+      padding: 20px;
+    }
+    .modal-overlay.active { display: flex; }
+    .modal-content {
+      max-width: 95vw;
+      max-height: 95vh;
+      position: relative;
+    }
+    .modal-content img {
+      max-width: 100%;
+      max-height: 90vh;
+      border-radius: var(--radius-lg);
+    }
+    .modal-close {
+      position: absolute;
+      top: -40px; right: 0;
+      background: white;
+      border: none;
+      width: 36px; height: 36px;
+      border-radius: 50%;
+      font-size: 24px;
+      cursor: pointer;
+    }
+    .modal-title {
+      color: white;
+      text-align: center;
+      margin-top: 12px;
+      font-size: 0.9rem;
+    }
+    /* Footer */
+    .footer {
+      background: var(--uapb-black);
+      border-top: 4px solid var(--uapb-gold);
+      padding: 24px 0;
+      margin-top: 48px;
+    }
+    .footer-content {
+      max-width: 1400px;
+      margin: 0 auto;
+      padding: 0 24px;
+      display: flex;
+      justify-content: space-between;
+      align-items: center;
+    }
+    .footer-left {
+      font-size: 0.85rem;
+      color: rgba(255,255,255,0.7);
+    }
+    .footer-links {
+      display: flex;
+      gap: 24px;
+    }
+    .footer-link {
+      font-size: 0.85rem;
+      color: var(--uapb-gold);
+      text-decoration: none;
+    }
+    .footer-link:hover { text-decoration: underline; }
+    /* Spinner */
+    .spinner {
+      width: 18px; height: 18px;
+      border: 2px solid #FFFFFF;
+      border-top-color: transparent;
+      border-radius: 50%;
+      animation: spin 0.8s linear infinite;
+    }
+    @keyframes spin { to { transform: rotate(360deg); } }
+    @keyframes fadeIn { from { opacity: 0; transform: translateY(10px); } to { opacity: 1; transform: translateY(0); } }
+    .animate-in { animation: fadeIn 0.4s ease forwards; }
+    .partners-block { display: flex; flex-direction: column; align-items: center; gap: 4px; }
+    .partners-eyebrow { font-size: 0.52rem; font-weight: 700; letter-spacing: 2.5px; text-transform: uppercase; color: rgba(255,255,255,0.28); padding-right: 3px; }
+    .partners-logos { display: flex; align-items: center; background: transparent; border: none; border-radius: 12px; overflow: hidden; transition: border-color 0.2s; }
+    .partners-logos:hover { border-color: rgba(232,163,23,0.25); }
+    .partner-logo-item { display: flex; align-items: center; justify-content: center; padding: 11px 22px; text-decoration: none; transition: background 0.2s ease; height: 64px; }
+    .partner-logo-item:hover { background: rgba(255,255,255,0.07); }
+    .partner-logo-item img { height: 49px; width: auto; display: block; }
+    .partner-logo-item.gsla-item img { height: 45px; }
+    .partner-divider { width: 1px; height: 32px; background: linear-gradient(to bottom, transparent, rgba(255,255,255,0.15), transparent); flex-shrink: 0; }
+  </style>
+</head>
+<body>
+  <!-- Header -->
+  <header class="uapb-header">
+    <div class="header-content">
+      <div class="header-left">
+        <img src="/static/UAPB-logo.png" alt="Logo" class="dept-logo">
+        <div class="header-text">
+          <h1>Department of Aquaculture and Fisheries</h1>
+          <p>Fish Nutrigenomics and AI Lab | Dr. Yathish Ramena, Director, Center of Excellence</p>
+        </div>
+      </div>
+      <div class="header-right">
+        <div class="partners-block">
+          <span class="partners-eyebrow"> Research Partners</span>
+          <div class="partners-logos">
+            <a href="https://aralliance.org" target="_blank" class="partner-logo-item" title="Arkansas Research Alliance">
+              <img src="/static/ara-logo.png" alt="Arkansas Research Alliance">
+            </a>
+          </div>
+        </div>
+      </div>
+    </div>
+  </header>
+  <!-- GSLA Header -->
+<header class="gsla-header">
+  <div class="header-content">
+    <div class="header-left">
+      <img src="static/gsla-header-logo.png" alt="GSLA Logo" class="dept-logo">
+      <div class="header-text">
+        <h1>The Artemia You Can Trust!</h1>
+        <p>Highest Quality | Best Nutrition</p>
+      </div>
+    </div>
+  </div>
+</header>
+  <!-- Main Navigation - Gold Bar -->
+  <!-- Main Navigation - Gold Bar -->
+<div class="nav-wrapper">
+  <video class="nav-video-bg" autoplay muted playsinline loop>
+    <source src="static/gsla-video.mp4" type="video/mp4">
+  </video>
+<nav class="main-nav">
+  <div class="nav-content">
+    <button class="nav-tab active" data-tab="research">Research</button>
+    <button class="nav-tab" data-tab="extension">Extension</button>
+  </div>
+</nav>
+  <!-- Research Tab Content -->
+  <nav class="sub-nav">
+  </video>
+  <div class="sub-nav-content">
+        <button class="sub-nav-tab active" data-species="vannamei">
+          <span class="scientific-name">L. vannamei</span>
+          <span class="common-name">(Pacific White Shrimp)</span>
+        </button>
+        <button class="sub-nav-tab" data-species="monodon">
+          <span class="scientific-name">P. monodon</span>
+          <span class="common-name">(Tiger Shrimp)</span>
+        </button>
+        <button class="sub-nav-tab" data-species="bass">
+          <span class="scientific-name">M. salmoides</span>
+          <span class="common-name">(Largemouth Bass)</span>
+        </button>
+        <button class="sub-nav-tab" data-species="prawn">
+          <span class="scientific-name">M. rosenbergii</span>
+          <span class="common-name">(Giant River Prawn)</span>
+        </button>
+      </div>
+    </nav>
+  </div><!-- end nav-wrapper -->
+<div id="tab-research" class="tab-content active">
+    <!-- Species Analysis Panels -->
+    <div id="species-vannamei" class="species-content active">
+      <div class="main-content">
+        <div class="page-header">
+          <h2><em>Litopenaeus vannamei</em> <span>Analysis</span></h2>
+          <p>AI-powered detection, length measurement, and weight estimation using computer vision and deep learning.</p>
+          <div class="species-badge">Pacific White Shrimp — <em>L. vannamei</em></div>
+        </div>
+        <div class="stats-grid">
+          <div class="stat-card"><div class="stat-label">Total Detected</div><div class="stat-value" id="statCount-vannamei">—</div></div>
+          <div class="stat-card"><div class="stat-label">Avg Length</div><div class="stat-value"><span id="statLength-vannamei">—</span><span class="unit">mm</span></div></div>
+          <div class="stat-card"><div class="stat-label">Avg Weight</div><div class="stat-value"><span id="statWeight-vannamei">—</span><span class="unit">g</span></div></div>
+          <div class="stat-card"><div class="stat-label">Total Biomass</div><div class="stat-value"><span id="statBiomass-vannamei">—</span><span class="unit">g</span></div></div>
+        </div>
+        <div class="analysis-grid">
+          <div>
+            <div class="charts-grid">
+              <div class="card"><div class="card-header"><span class="card-title">Length Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="lengthChart-vannamei"><div class="chart-placeholder">Upload images to generate</div></div></div>
+              <div class="card"><div class="card-header"><span class="card-title">Weight Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="weightChart-vannamei"><div class="chart-placeholder">Upload images to generate</div></div></div>
+            </div>
+            <div class="section-header"><h3 class="section-title">Detection Results</h3><span class="section-count" id="processedCount-vannamei">0 images processed</span></div>
+            <div class="results-grid" id="resultsGrid-vannamei"></div>
+          </div>
+          <div>
+            <div class="card">
+              <div class="card-header"><span class="card-title">Upload Images</span><span class="card-badge">Analysis</span></div>
+              <div class="card-body">
+                <label class="upload-zone" id="dropzone-vannamei"><input type="file" id="fileInput-vannamei" multiple accept="image/*"><div class="upload-icon"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/><polyline points="17 8 12 3 7 8"/><line x1="12" y1="3" x2="12" y2="15"/></svg></div><h3>Drop images here</h3><p>or click to browse</p></label>
+                <div class="file-info"><span class="file-count" id="fileCount-vannamei">No files selected</span><span class="file-size" id="fileSize-vannamei">—</span></div>
+                <div class="progress-container"><div class="progress-bar"><div class="progress-fill" id="progressFill-vannamei"></div></div><div class="progress-text"><span id="progressStatus-vannamei">Ready</span><span id="progressPercent-vannamei">0%</span></div></div>
+                <div class="button-group"><button class="btn btn-primary" id="analyzeBtn-vannamei" onclick="runAnalysis('vannamei')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><polygon points="5 3 19 12 5 21 5 3"/></svg>Analyze</button><button class="btn btn-secondary" onclick="resetTab('vannamei')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8"/><path d="M3 3v5h5"/></svg>Reset</button></div>
+                <div class="error-box" id="errorBox-vannamei"></div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+    <!-- P. monodon -->
+    <div id="species-monodon" class="species-content">
+      <div class="main-content">
+        <div class="page-header">
+          <h2><em>Penaeus monodon</em> <span>Analysis</span></h2>
+          <p>AI-powered detection and biomass estimation for Black Tiger Shrimp aquaculture research.</p>
+          <div class="species-badge">Tiger Shrimp — <em>P. monodon</em></div>
+        </div>
+        <div class="stats-grid">
+          <div class="stat-card"><div class="stat-label">Total Detected</div><div class="stat-value" id="statCount-monodon">—</div></div>
+          <div class="stat-card"><div class="stat-label">Avg Length</div><div class="stat-value"><span id="statLength-monodon">—</span><span class="unit">mm</span></div></div>
+          <div class="stat-card"><div class="stat-label">Avg Weight</div><div class="stat-value"><span id="statWeight-monodon">—</span><span class="unit">g</span></div></div>
+          <div class="stat-card"><div class="stat-label">Total Biomass</div><div class="stat-value"><span id="statBiomass-monodon">—</span><span class="unit">g</span></div></div>
+        </div>
+        <div class="analysis-grid">
+          <div>
+            <div class="charts-grid">
+              <div class="card"><div class="card-header"><span class="card-title">Length Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="lengthChart-monodon"><div class="chart-placeholder">Upload images to generate</div></div></div>
+              <div class="card"><div class="card-header"><span class="card-title">Weight Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="weightChart-monodon"><div class="chart-placeholder">Upload images to generate</div></div></div>
+            </div>
+            <div class="section-header"><h3 class="section-title">Detection Results</h3><span class="section-count" id="processedCount-monodon">0 images processed</span></div>
+            <div class="results-grid" id="resultsGrid-monodon"></div>
+          </div>
+          <div>
+            <div class="card">
+              <div class="card-header"><span class="card-title">Upload Images</span><span class="card-badge">Analysis</span></div>
+              <div class="card-body">
+                <label class="upload-zone" id="dropzone-monodon"><input type="file" id="fileInput-monodon" multiple accept="image/*"><div class="upload-icon"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/><polyline points="17 8 12 3 7 8"/><line x1="12" y1="3" x2="12" y2="15"/></svg></div><h3>Drop images here</h3><p>or click to browse</p></label>
+                <div class="file-info"><span class="file-count" id="fileCount-monodon">No files selected</span><span class="file-size" id="fileSize-monodon">—</span></div>
+                <div class="progress-container"><div class="progress-bar"><div class="progress-fill" id="progressFill-monodon"></div></div><div class="progress-text"><span id="progressStatus-monodon">Ready</span><span id="progressPercent-monodon">0%</span></div></div>
+                <div class="button-group"><button class="btn btn-primary" id="analyzeBtn-monodon" onclick="runAnalysis('monodon')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><polygon points="5 3 19 12 5 21 5 3"/></svg>Analyze</button><button class="btn btn-secondary" onclick="resetTab('monodon')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8"/><path d="M3 3v5h5"/></svg>Reset</button></div>
+                <div class="error-box" id="errorBox-monodon"></div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+    <!-- M. salmoides -->
+    <div id="species-bass" class="species-content">
+      <div class="main-content">
+        <div class="page-header">
+          <h2><em>Micropterus salmoides</em> <span>Analysis</span></h2>
+          <p>AI-powered detection and biomass estimation for Largemouth Bass aquaculture research.</p>
+          <div class="species-badge">Largemouth Bass — <em>M. salmoides</em></div>
+        </div>
+        <div class="stats-grid">
+          <div class="stat-card"><div class="stat-label">Total Detected</div><div class="stat-value" id="statCount-bass">—</div></div>
+          <div class="stat-card"><div class="stat-label">Avg Length</div><div class="stat-value"><span id="statLength-bass">—</span><span class="unit">mm</span></div></div>
+          <div class="stat-card"><div class="stat-label">Avg Weight</div><div class="stat-value"><span id="statWeight-bass">—</span><span class="unit">g</span></div></div>
+          <div class="stat-card"><div class="stat-label">Total Biomass</div><div class="stat-value"><span id="statBiomass-bass">—</span><span class="unit">g</span></div></div>
+        </div>
+        <div class="analysis-grid">
+          <div>
+            <div class="charts-grid">
+              <div class="card"><div class="card-header"><span class="card-title">Length Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="lengthChart-bass"><div class="chart-placeholder">Upload images to generate</div></div></div>
+              <div class="card"><div class="card-header"><span class="card-title">Weight Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="weightChart-bass"><div class="chart-placeholder">Upload images to generate</div></div></div>
+            </div>
+            <div class="section-header"><h3 class="section-title">Detection Results</h3><span class="section-count" id="processedCount-bass">0 images processed</span></div>
+            <div class="results-grid" id="resultsGrid-bass"></div>
+          </div>
+          <div>
+            <div class="card">
+              <div class="card-header"><span class="card-title">Upload Images</span><span class="card-badge">Analysis</span></div>
+              <div class="card-body">
+                <label class="upload-zone" id="dropzone-bass"><input type="file" id="fileInput-bass" multiple accept="image/*"><div class="upload-icon"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/><polyline points="17 8 12 3 7 8"/><line x1="12" y1="3" x2="12" y2="15"/></svg></div><h3>Drop images here</h3><p>or click to browse</p></label>
+                <div class="file-info"><span class="file-count" id="fileCount-bass">No files selected</span><span class="file-size" id="fileSize-bass">—</span></div>
+                <div class="progress-container"><div class="progress-bar"><div class="progress-fill" id="progressFill-bass"></div></div><div class="progress-text"><span id="progressStatus-bass">Ready</span><span id="progressPercent-bass">0%</span></div></div>
+                <div class="button-group"><button class="btn btn-primary" id="analyzeBtn-bass" onclick="runAnalysis('bass')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><polygon points="5 3 19 12 5 21 5 3"/></svg>Analyze</button><button class="btn btn-secondary" onclick="resetTab('bass')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8"/><path d="M3 3v5h5"/></svg>Reset</button></div>
+                <div class="error-box" id="errorBox-bass"></div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+    <!-- M. rosenbergii -->
+    <div id="species-prawn" class="species-content">
+      <div class="main-content">
+        <div class="page-header">
+          <h2><em>Macrobrachium rosenbergii</em> <span>Analysis</span></h2>
+          <p>AI-powered detection and biomass estimation for Giant Freshwater Prawn aquaculture research.</p>
+          <div class="species-badge">Giant River Prawn — <em>M. rosenbergii</em></div>
+        </div>
+        <div class="stats-grid">
+          <div class="stat-card"><div class="stat-label">Total Detected</div><div class="stat-value" id="statCount-prawn">—</div></div>
+          <div class="stat-card"><div class="stat-label">Avg Length</div><div class="stat-value"><span id="statLength-prawn">—</span><span class="unit">mm</span></div></div>
+          <div class="stat-card"><div class="stat-label">Avg Weight</div><div class="stat-value"><span id="statWeight-prawn">—</span><span class="unit">g</span></div></div>
+          <div class="stat-card"><div class="stat-label">Total Biomass</div><div class="stat-value"><span id="statBiomass-prawn">—</span><span class="unit">g</span></div></div>
+        </div>
+        <div class="analysis-grid">
+          <div>
+            <div class="charts-grid">
+              <div class="card"><div class="card-header"><span class="card-title">Length Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="lengthChart-prawn"><div class="chart-placeholder">Upload images to generate</div></div></div>
+              <div class="card"><div class="card-header"><span class="card-title">Weight Distribution</span><span class="card-badge">Histogram</span></div><div class="card-body" id="weightChart-prawn"><div class="chart-placeholder">Upload images to generate</div></div></div>
+            </div>
+            <div class="section-header"><h3 class="section-title">Detection Results</h3><span class="section-count" id="processedCount-prawn">0 images processed</span></div>
+            <div class="results-grid" id="resultsGrid-prawn"></div>
+          </div>
+          <div>
+            <div class="card">
+              <div class="card-header"><span class="card-title">Upload Images</span><span class="card-badge">Analysis</span></div>
+              <div class="card-body">
+                <label class="upload-zone" id="dropzone-prawn"><input type="file" id="fileInput-prawn" multiple accept="image/*"><div class="upload-icon"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/><polyline points="17 8 12 3 7 8"/><line x1="12" y1="3" x2="12" y2="15"/></svg></div><h3>Drop images here</h3><p>or click to browse</p></label>
+                <div class="file-info"><span class="file-count" id="fileCount-prawn">No files selected</span><span class="file-size" id="fileSize-prawn">—</span></div>
+                <div class="progress-container"><div class="progress-bar"><div class="progress-fill" id="progressFill-prawn"></div></div><div class="progress-text"><span id="progressStatus-prawn">Ready</span><span id="progressPercent-prawn">0%</span></div></div>
+                <div class="button-group"><button class="btn btn-primary" id="analyzeBtn-prawn" onclick="runAnalysis('prawn')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><polygon points="5 3 19 12 5 21 5 3"/></svg>Analyze</button><button class="btn btn-secondary" onclick="resetTab('prawn')"><svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8"/><path d="M3 3v5h5"/></svg>Reset</button></div>
+                <div class="error-box" id="errorBox-prawn"></div>
+              </div>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+  <!-- Extension Tab Content -->
+  <div id="tab-extension" class="tab-content">
+    <div class="main-content">
+      <div class="page-header">
+        <h2>Extension <span>Services</span></h2>
+        <p>Outreach programs, industry partnerships, and technology transfer from the Fish Nutrigenomics and AI Lab.</p>
+      </div>
+      <div class="profile-section">
+        <div class="profile-card">
+          <div class="profile-avatar">
+            <img src="/static/dr-ramena.png" alt="Dr. Yathish Ramena" onerror="this.style.display='none'; this.parentElement.innerHTML='YR';">
+          </div>
+          <div class="profile-name">Dr. Yathish Ramena</div>
+          <div class="profile-title">Director, Fish Nutrigenomics and AI Lab</div>
+          <div class="profile-dept">Aquaculture Species Analysis</div>
+          <div class="profile-links">
+            <a href="mailto:ramenay@uapb.edu" class="profile-link">✉️ ramenay@uapb.edu</a>
+          </div>
+        </div>
+        <div>
+          <h3 style="font-family: 'Oswald', sans-serif; font-size: 1.2rem; margin-bottom: 20px; text-transform: uppercase;">Research Focus Areas</h3>
+          <div class="research-areas">
+            <div class="research-card">
+              <div class="research-icon"><svg width="24" height="24" fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M12 2L2 7l10 5 10-5-10-5zM2 17l10 5 10-5M2 12l10 5 10-5"/></svg></div>
+              <h4>Fish Nutrigenomics</h4>
+              <p>Investigating the interaction between nutrition and gene expression in aquaculture species to optimize growth and health outcomes.</p>
+            </div>
+            <div class="research-card">
+              <div class="research-icon"><svg width="24" height="24" fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><rect x="2" y="3" width="20" height="14" rx="2"/><path d="M8 21h8M12 17v4"/></svg></div>
+              <h4>AI & Computer Vision</h4>
+              <p>Developing deep learning models for automated species detection, biomass estimation, and health monitoring in aquaculture systems.</p>
+            </div>
+            <div class="research-card">
+              <div class="research-icon"><svg width="24" height="24" fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><path d="M14 2v6h6M16 13H8M16 17H8M10 9H8"/></svg></div>
+              <h4>Precision Aquaculture</h4>
+              <p>Integrating IoT sensors, real-time monitoring, and predictive analytics for sustainable and efficient aquaculture production.</p>
+            </div>
+            <div class="research-card">
+              <div class="research-icon"><svg width="24" height="24" fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><circle cx="12" cy="12" r="10"/><path d="M12 6v6l4 2"/></svg></div>
+              <h4>Growth Modeling</h4>
+              <p>Developing mathematical models for predicting growth trajectories and optimizing feeding strategies in commercial aquaculture.</p>
+            </div>
+          </div>
+        </div>
+      </div>
+      <h3 style="font-family: 'Oswald', sans-serif; font-size: 1.2rem; margin: 40px 0 20px; text-transform: uppercase;">Extension Programs</h3>
+      <div class="research-areas">
+        <div class="research-card">
+          <div class="research-icon"><svg width="24" height="24" fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M17 21v-2a4 4 0 0 0-4-4H5a4 4 0 0 0-4 4v2"/><circle cx="9" cy="7" r="4"/><path d="M23 21v-2a4 4 0 0 0-3-3.87M16 3.13a4 4 0 0 1 0 7.75"/></svg></div>
+          <h4>Industry Training Workshops</h4>
+          <p>Hands-on training sessions for aquaculture farmers on best practices, disease management, and technology adoption.</p>
+        </div>
+        <div class="research-card">
+          <div class="research-icon"><svg width="24" height="24" fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M21 12a9 9 0 01-9 9m9-9a9 9 0 00-9-9m9 9H3m9 9a9 9 0 01-9-9m9 9c1.657 0 3-4.03 3-9s-1.343-9-3-9m0 18c-1.657 0-3-4.03-3-9s1.343-9 3-9"/></svg></div>
+          <h4>Technology Transfer</h4>
+          <p>Bridging research and industry by providing AI-powered tools and decision support systems to aquaculture producers.</p>
+        </div>
+        <div class="research-card">
+          <div class="research-icon"><svg width="24" height="24" fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><path d="M12 6.253v13m0-13C10.832 5.477 9.246 5 7.5 5S4.168 5.477 3 6.253v13C4.168 18.477 5.754 18 7.5 18s3.332.477 4.5 1.253m0-13C13.168 5.477 14.754 5 16.5 5c1.747 0 3.332.477 4.5 1.253v13C19.832 18.477 18.247 18 16.5 18c-1.746 0-3.332.477-4.5 1.253"/></svg></div>
+          <h4>Educational Resources</h4>
+          <p>Developing curricula, online courses, and educational materials for the next generation of aquaculture professionals.</p>
+        </div>
+      </div>
+    </div>
+  </div>
+  <!-- Image Modal -->
+  <div class="modal-overlay" id="imageModal" onclick="closeModal()">
+    <div class="modal-content" onclick="event.stopPropagation()">
+      <button class="modal-close" onclick="closeModal()">×</button>
+      <img id="modalImage" src="" alt="Expanded view">
+      <div class="modal-title" id="modalTitle"></div>
+    </div>
+  </div>
+  <!-- Footer -->
+  <footer class="footer">
+    <div class="footer-content">
+      <div class="footer-left">© 2026 Aquaculture Species Analysis • Fish Nutrigenomics and AI Lab</div>
+      <div class="footer-links">
+        <a href="/docs" class="footer-link">API Docs</a>
+      </div>
+    </div>
+  </footer>
+  <script>
+    const $ = id => document.getElementById(id);
+    // Modal
+    function openModal(src, title) {
+      $('modalImage').src = src;
+      $('modalTitle').textContent = title || '';
+      $('imageModal').classList.add('active');
+      document.body.style.overflow = 'hidden';
+    }
+    function closeModal() {
+      $('imageModal').classList.remove('active');
+      document.body.style.overflow = '';
+    }
+    document.addEventListener('keydown', e => { if (e.key === 'Escape') closeModal(); });
+    // Main Tab Navigation
+    document.querySelectorAll('.nav-tab').forEach(tab => {
+      tab.addEventListener('click', () => {
+        document.querySelectorAll('.nav-tab').forEach(t => t.classList.remove('active'));
+        document.querySelectorAll('.tab-content').forEach(c => c.classList.remove('active'));
+        tab.classList.add('active');
+        $('tab-' + tab.dataset.tab).classList.add('active');
+      });
+    });
+    // Species Sub-Navigation
+    document.querySelectorAll('.sub-nav-tab').forEach(tab => {
+      tab.addEventListener('click', () => {
+        document.querySelectorAll('.sub-nav-tab').forEach(t => t.classList.remove('active'));
+        document.querySelectorAll('.species-content').forEach(c => c.classList.remove('active'));
+        tab.classList.add('active');
+        $('species-' + tab.dataset.species).classList.add('active');
+      });
+    });
+    // Species setup
+    const species = ['vannamei', 'monodon', 'bass', 'prawn'];
+    species.forEach(sp => {
+      const fileInput = $('fileInput-' + sp);
+      const dropzone = $('dropzone-' + sp);
+      fileInput.addEventListener('change', () => updateFileInfo(sp));
+      dropzone.addEventListener('dragover', e => { e.preventDefault(); dropzone.classList.add('drag-over'); });
+      dropzone.addEventListener('dragleave', () => dropzone.classList.remove('drag-over'));
+      dropzone.addEventListener('drop', e => {
+        e.preventDefault();
+        dropzone.classList.remove('drag-over');
+        fileInput.files = e.dataTransfer.files;
+        updateFileInfo(sp);
+      });
+    });
+    const formatBytes = b => {
+      if (b === 0) return '0 B';
+      const k = 1024, s = ['B', 'KB', 'MB', 'GB'];
+      const i = Math.floor(Math.log(b) / Math.log(k));
+      return parseFloat((b / Math.pow(k, i)).toFixed(2)) + ' ' + s[i];
+    };
+    function updateFileInfo(sp) {
+      const files = $('fileInput-' + sp).files;
+      if (!files.length) { $('fileCount-' + sp).textContent = 'No files selected'; $('fileSize-' + sp).textContent = '—'; return; }
+      let size = 0; for (const f of files) size += f.size;
+      $('fileCount-' + sp).textContent = files.length + ' file' + (files.length > 1 ? 's' : '') + ' selected';
+      $('fileSize-' + sp).textContent = formatBytes(size);
+      showError(sp, '');
+    }
+    function setProgress(sp, pct, status) {
+      $('progressFill-' + sp).style.width = pct + '%';
+      $('progressPercent-' + sp).textContent = pct + '%';
+      if (status) $('progressStatus-' + sp).textContent = status;
+    }
+    function showError(sp, msg) {
+      const box = $('errorBox-' + sp);
+      box.textContent = msg;
+      box.classList.toggle('visible', !!msg);
+    }
+    async function checkHealth() {
+      try {
+        await fetch('/health');
+        $('statusDot').className = 'status-dot online';
+        $('statusLabel').textContent = 'API Online';
+      } catch {
+        $('statusDot').className = 'status-dot offline';
+        $('statusLabel').textContent = 'API Offline';
+      }
+    }
+    checkHealth(); setInterval(checkHealth, 10000);
+    async function runAnalysis(sp) {
+      const files = $('fileInput-' + sp).files;
+      if (!files.length) { showError(sp, 'Please select images first.'); return; }
+      const btn = $('analyzeBtn-' + sp);
+      btn.disabled = true;
+      btn.innerHTML = '<span class="spinner"></span> Analyzing...';
+      showError(sp, '');
+      setProgress(sp, 10, 'Uploading...');
+      const form = new FormData();
+      for (const f of files) form.append('files', f);
+      try {
+        setProgress(sp, 30, 'Processing...');
+        const res = await fetch('/detect?species=' + sp, { method: 'POST', body: form });
+        setProgress(sp, 70, 'Analyzing...');
+        const text = await res.text();
+        let data; try { data = JSON.parse(text); } catch { throw new Error(text); }
+        if (!res.ok) throw new Error(data.detail || data.error || 'Failed');
+        setProgress(sp, 90, 'Rendering...');
+        displayResults(sp, data);
+        setProgress(sp, 100, 'Complete ✓');
+      } catch (err) {
+        showError(sp, err.message);
+        setProgress(sp, 0, 'Error');
+      } finally {
+        btn.disabled = false;
+        btn.innerHTML = '<svg fill="none" stroke="currentColor" stroke-width="2" viewBox="0 0 24 24"><polygon points="5 3 19 12 5 21 5 3"/></svg>Analyze';
+        setTimeout(() => setProgress(sp, 0, 'Ready'), 2000);
+      }
+    }
+    function displayResults(sp, data) {
+      const s = data.overall_summary || {};
+      const h = data.histograms || {};
+      const imgs = data.per_image || [];
+      $('statCount-' + sp).textContent = s.total_specimens ?? data.overall_total_shrimp ?? '—';
+      $('statLength-' + sp).textContent = s.average_length_mm ?? data.overall_average_length_mm ?? '—';
+      $('statWeight-' + sp).textContent = s.average_weight_g ?? '—';
+      $('statBiomass-' + sp).textContent = s.total_biomass_g ?? '—';
+      if (h.length_histogram_base64 || data.histogram_png_base64) {
+        const src = 'data:image/png;base64,' + (h.length_histogram_base64 || data.histogram_png_base64);
+        $('lengthChart-' + sp).innerHTML = '<img src="' + src + '" onclick="openModal(\'' + src + '\', \'Length Distribution\')">';
+      }
+      if (h.weight_histogram_base64) {
+        const src = 'data:image/png;base64,' + h.weight_histogram_base64;
+        $('weightChart-' + sp).innerHTML = '<img src="' + src + '" onclick="openModal(\'' + src + '\', \'Weight Distribution\')">';
+      }
+      $('processedCount-' + sp).textContent = imgs.length + ' image' + (imgs.length !== 1 ? 's' : '') + ' processed';
+      const grid = $('resultsGrid-' + sp);
+      grid.innerHTML = '';
+      imgs.forEach((item, i) => {
+        const card = document.createElement('div');
+        card.className = 'result-card animate-in';
+        card.style.animationDelay = (i * 80) + 'ms';
+        if (item.error) {
+          card.innerHTML = '<div class="result-header"><span class="result-filename">' + (item.filename || 'Error') + '</span></div><div style="padding:16px;color:#DC2626;font-size:0.85rem;">' + item.error + '</div>';
+        } else {
+          const cnt = item.specimen_count ?? item.shrimp_count ?? 0;
+          const avg = item.summary?.average_length_mm ?? item.average_length_mm ?? 0;
+          const wgt = item.summary?.average_weight_g ?? 0;
+          const lens = item.weights_g?.map((w,j) => item.lengths_mm[j] + 'mm/' + w + 'g') || item.lengths_mm?.map(l => l + 'mm') || [];
+          const imgSrc = item.annotated_image_png_base64 ? 'data:image/png;base64,' + item.annotated_image_png_base64 : '';
+          card.innerHTML = '<div class="result-header"><span class="result-filename">' + (item.filename || 'Image') + '</span><div class="result-stats"><span class="result-stat">Count: <strong>' + cnt + '</strong></span><span class="result-stat"><strong>' + avg + '</strong>mm</span>' + (wgt ? '<span class="result-stat"><strong>' + wgt + '</strong>g</span>' : '') + '</div></div>' + (imgSrc ? '<div class="result-image" onclick="openModal(\'' + imgSrc + '\', \'' + (item.filename || 'Result') + '\')"><img src="' + imgSrc + '"><span class="click-hint">Click to expand</span></div>' : '') + (lens.length ? '<div class="result-data">' + lens.join(' • ') + '</div>' : '');
+        }
+        grid.appendChild(card);
+      });
+    }
+    function resetTab(sp) {
+      $('fileInput-' + sp).value = '';
+      $('fileCount-' + sp).textContent = 'No files selected';
+      $('fileSize-' + sp).textContent = '—';
+      setProgress(sp, 0, 'Ready');
+      showError(sp, '');
+      ['statCount', 'statLength', 'statWeight', 'statBiomass'].forEach(id => { const el = $(id + '-' + sp); if (el) el.textContent = '—'; });
+      $('lengthChart-' + sp).innerHTML = '<div class="chart-placeholder">Upload images to generate</div>';
+      $('weightChart-' + sp).innerHTML = '<div class="chart-placeholder">Upload images to generate</div>';
+      $('resultsGrid-' + sp).innerHTML = '';
+      $('processedCount-' + sp).textContent = '0 images processed';
+    }
+  </script>
+</body>
+</html>

render.yaml ADDED Viewed

	@@ -0,0 +1,10 @@

+services:
+  - type: web
+    name: aquaculture-vision
+    env: python
+    runtime: python-3.11
+    buildCommand: pip install -r requirements.txt
+    startCommand: uvicorn server:app --host 0.0.0.0 --port $PORT
+    envVars:
+      - key: PYTHON_VERSION
+        value: 3.11

requirements.txt ADDED Viewed

	@@ -0,0 +1,10 @@

+fastapi
+uvicorn[standard]
+ultralytics
+opencv-python-headless
+python-multipart
+Pillow
+numpy
+matplotlib
+requests
+gdown

server.py ADDED Viewed

	@@ -0,0 +1,726 @@

+"""
+Department of Aquaculture and Fisheries
+Fish Nutrigenomics and AI Lab | Dr. Yathish Ramena, Director
+Advanced Species Detection with Length and Weight Estimation
+Now with Roboflow Integration for Largemouth Bass Detection!
+Version 3.1 - Fixed: Bass shows species + confidence only (no estimated measurements)
+"""
+from fastapi import FastAPI, UploadFile, File, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse, JSONResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
+from typing import List, Optional
+from ultralytics import YOLO
+import numpy as np
+import cv2
+import base64
+import tempfile
+import os
+import io
+import requests
+from datetime import datetime
+# Matplotlib setup - MUST be before pyplot import
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+# ═══════════════════════════════════════════════════════════════════════════════
+# ROBOFLOW CONFIGURATION FOR BASS DETECTION
+# ═══════════════════════════════════════════════════════════════════════════════
+ROBOFLOW_API_KEY = "tya4HWqSPsfoQAmR03ES"  # Your Roboflow Private API Key
+ROBOFLOW_MODEL_ENDPOINT = "https://serverless.roboflow.com/bass-fish-detection-06gec/1"
+# ═══════════════════════════════════════════════════════════════════════════════
+# DOWNLOAD WEIGHTS FROM GOOGLE DRIVE (for shrimp model)
+# ═══════════════════════════════════════════════════════════════════════════════
+MODEL_PATH = "weights.pt"
+GOOGLE_DRIVE_FILE_ID = "14VSgbeQJyBizH-wTAq36WTph1miZoG5b"
+def download_weights_from_gdrive(file_id: str, destination: str):
+    """Download file from Google Drive using gdown"""
+    if os.path.exists(destination):
+        file_size = os.path.getsize(destination)
+        if file_size > 1000000:  # > 1MB means it's likely valid
+            print(f"✓ Weights file already exists: {destination} ({file_size} bytes)")
+            return True
+        else:
+            print(f"⚠ Existing file too small ({file_size} bytes), re-downloading...")
+            os.remove(destination)
+    print(f"⬇ Downloading weights from Google Drive...")
+    print(f"  File ID: {file_id}")
+    try:
+        import gdown
+        url = f"https://drive.google.com/uc?id={file_id}"
+        print(f"  URL: {url}")
+        output = gdown.download(url, destination, quiet=False, fuzzy=True)
+        if output and os.path.exists(destination):
+            file_size = os.path.getsize(destination)
+            print(f"✓ Downloaded: {destination} ({file_size} bytes)")
+            if file_size > 1000000:
+                return True
+            else:
+                print(f"✗ File too small, might be an error page")
+                return False
+        else:
+            print(f"✗ gdown returned: {output}")
+            return False
+    except Exception as e:
+        print(f"✗ gdown failed: {e}")
+    # Fallback: try with confirm parameter
+    try:
+        import gdown
+        url = f"https://drive.google.com/uc?id={file_id}&confirm=t"
+        print(f"  Trying fallback URL: {url}")
+        output = gdown.download(url, destination, quiet=False)
+        if output and os.path.exists(destination) and os.path.getsize(destination) > 1000000:
+            print(f"✓ Fallback download successful")
+            return True
+    except Exception as e:
+        print(f"✗ Fallback also failed: {e}")
+    return False
+# Download weights if not present
+print("=" * 60)
+print("INITIALIZING AQUACULTURE VISION API")
+print("=" * 60)
+if not download_weights_from_gdrive(GOOGLE_DRIVE_FILE_ID, MODEL_PATH):
+    print("=" * 60)
+    print("ERROR: Could not download weights.pt from Google Drive!")
+    print("Please check:")
+    print("  1. File ID is correct: " + GOOGLE_DRIVE_FILE_ID)
+    print("  2. File is shared as 'Anyone with the link'")
+    print("  3. Google Drive link is accessible")
+    print("=" * 60)
+    raise FileNotFoundError(f"Could not download {MODEL_PATH} from Google Drive")
+print(f"Loading YOLO model from {MODEL_PATH}...")
+print(f"Roboflow API configured for Bass detection")
+app = FastAPI(
+    title="Aquaculture Vision API",
+    description="AI-powered detection and biomass estimation for aquaculture species",
+    version="3.1 - Bass detection shows species + confidence only"
+)
+# Mount static files for logo and other assets
+app.mount("/static", StaticFiles(directory="."), name="static")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+# ═══════════════════════════════════════════════════════════════════════════════
+# CONFIGURATION
+# ═══════════════════════════════════════════════════════════════════════════════
+MODEL_PATH = "weights.pt"
+PIXELS_PER_MM = 6.5
+CONF_THRESHOLD = 0.40
+MASK_ALPHA = 0.4
+# Load YOLO model for shrimp
+model = YOLO(MODEL_PATH)
+# ═══════════════════════════════════════════════════════════════════════════════
+# SPECIES CONFIG - Length-Weight Relationships: W = a × L^b
+# ═══════════════════════════════════════════════════════════════════════════════
+SPECIES_CONFIG = {
+    "vannamei": {
+        "display_name": "Pacific White Shrimp",
+        "scientific_name": "Litopenaeus vannamei",
+        "weight_a": 8.54e-6,
+        "weight_b": 2.997,
+        "color": (0, 255, 127),
+        "min_harvest_mm": 100,
+        "optimal_harvest_mm": 130,
+        "use_roboflow": False,
+    },
+    "monodon": {
+        "display_name": "Tiger Shrimp",
+        "scientific_name": "Penaeus monodon",
+        "weight_a": 7.2e-6,
+        "weight_b": 3.05,
+        "color": (255, 165, 0),
+        "min_harvest_mm": 120,
+        "optimal_harvest_mm": 150,
+        "use_roboflow": False,
+    },
+    "bass": {
+        "display_name": "Largemouth Bass",
+        "scientific_name": "Micropterus salmoides",
+        "weight_a": 7.0e-6,
+        "weight_b": 3.19,
+        "color": (100, 149, 237),  # Cornflower blue
+        "min_harvest_mm": 250,
+        "optimal_harvest_mm": 350,
+        "use_roboflow": True,  # Use Roboflow API for bass!
+    },
+    "prawn": {
+        "display_name": "Giant River Prawn",
+        "scientific_name": "Macrobrachium rosenbergii",
+        "weight_a": 6.8e-6,
+        "weight_b": 3.08,
+        "color": (147, 112, 219),
+        "min_harvest_mm": 150,
+        "optimal_harvest_mm": 200,
+        "use_roboflow": False,
+    },
+    "default": {
+        "display_name": "Unknown Species",
+        "scientific_name": "N/A",
+        "weight_a": 8.54e-6,
+        "weight_b": 3.0,
+        "color": (0, 255, 127),
+        "min_harvest_mm": 100,
+        "optimal_harvest_mm": 150,
+        "use_roboflow": False,
+    }
+}
+def get_species_config(species_key: str) -> dict:
+    return SPECIES_CONFIG.get(species_key, SPECIES_CONFIG["default"])
+def is_target_class(class_name: str) -> bool:
+    """Check if detected class is a target species"""
+    if class_name == "shrimp - v1 2025-10-24 5-22pm":
+        return True
+    lower = class_name.lower()
+    return any(kw in lower for kw in ["shrimp", "fish", "prawn", "bass"])
+def max_pairwise_distance(points_xy: np.ndarray) -> float:
+    if points_xy.shape[0] < 2:
+        return 0.0
+    diff = points_xy[:, None, :] - points_xy[None, :, :]
+    dist = np.sqrt((diff ** 2).sum(axis=2))
+    return float(dist.max())
+def estimate_weight(length_mm: float, species_config: dict) -> float:
+    """W = a × L^b"""
+    a = species_config["weight_a"]
+    b = species_config["weight_b"]
+    if length_mm <= 0:
+        return 0.0
+    return a * (length_mm ** b)
+def get_size_category(length_mm: float, species_config: dict) -> str:
+    min_harvest = species_config["min_harvest_mm"]
+    optimal_harvest = species_config["optimal_harvest_mm"]
+    if length_mm < min_harvest * 0.7:
+        return "juvenile"
+    elif length_mm < min_harvest:
+        return "sub-harvest"
+    elif length_mm < optimal_harvest:
+        return "harvestable"
+    return "optimal"
+# ═══════════════════════════════════════════════════════════════════════════════
+# ROBOFLOW BASS DETECTION FUNCTION
+# ═══════════════════════════════════════════════════════════════════════════════
+def detect_bass_with_roboflow(image_path: str, confidence: float = 0.40):
+    """
+    Detect bass fish using Roboflow API
+    Returns list of detections with bounding boxes
+    """
+    try:
+        # Read image and encode to base64
+        with open(image_path, "rb") as f:
+            image_data = base64.b64encode(f.read()).decode("utf-8")
+        # Call Roboflow API
+        response = requests.post(
+            ROBOFLOW_MODEL_ENDPOINT,
+            params={
+                "api_key": ROBOFLOW_API_KEY,
+                "confidence": 40,  # Roboflow uses 0-100
+            },
+            data=image_data,
+            headers={
+                "Content-Type": "application/x-www-form-urlencoded"
+            },
+            timeout=30
+        )
+        if response.status_code == 200:
+            result = response.json()
+            print(f"✓ Roboflow API response: {len(result.get('predictions', []))} detections")
+            return result
+        else:
+            print(f"✗ Roboflow API error: {response.status_code} - {response.text}")
+            return {"predictions": [], "error": response.text}
+    except Exception as e:
+        print(f"✗ Roboflow API exception: {str(e)}")
+        return {"predictions": [], "error": str(e)}
+def process_bass_image(image_path: str, species_config: dict, calibration: float):
+    """
+    Process a single image for bass detection using Roboflow
+    Returns detection results with annotations
+    NOW: Shows only species + confidence (no fake length/weight)
+    """
+    # Get Roboflow detections
+    roboflow_result = detect_bass_with_roboflow(image_path, CONF_THRESHOLD)
+    predictions = roboflow_result.get("predictions", [])
+    # Read image for annotation
+    bgr = cv2.imread(image_path)
+    if bgr is None:
+        return None, 0, [], [], "Could not read image"
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    overlay = rgb.copy()
+    detection_count = 0
+    species_detected = []
+    confidences = []
+    for pred in predictions:
+        # Get bounding box
+        x = pred.get("x", 0)
+        y = pred.get("y", 0)
+        width = pred.get("width", 0)
+        height = pred.get("height", 0)
+        confidence = pred.get("confidence", 0)
+        class_name = pred.get("class", "bass")
+        detection_count += 1
+        species_detected.append(class_name)
+        confidences.append(confidence)
+        # Calculate bounding box corners
+        x1 = int(x - width / 2)
+        y1 = int(y - height / 2)
+        x2 = int(x + width / 2)
+        y2 = int(y + height / 2)
+        # Draw bounding box
+        color = species_config["color"]
+        cv2.rectangle(overlay, (x1, y1), (x2, y2), color, -1)  # Filled for overlay effect
+    # Blend overlay
+    annotated = cv2.addWeighted(rgb, 1 - MASK_ALPHA, overlay, MASK_ALPHA, 0)
+    # Draw bounding boxes and labels (species + confidence ONLY)
+    for pred in predictions:
+        x = pred.get("x", 0)
+        y = pred.get("y", 0)
+        width = pred.get("width", 0)
+        height = pred.get("height", 0)
+        confidence = pred.get("confidence", 0)
+        class_name = pred.get("class", "bass")
+        x1 = int(x - width / 2)
+        y1 = int(y - height / 2)
+        x2 = int(x + width / 2)
+        y2 = int(y + height / 2)
+        # Draw box outline
+        cv2.rectangle(annotated, (x1, y1), (x2, y2), species_config["color"], 3)
+        # Draw label: SPECIES + CONFIDENCE only (no length/weight)
+        label = f"{class_name}"
+        conf_label = f"{confidence*100:.0f}%"
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        font_scale = 0.7
+        thickness = 2
+        # Get text sizes
+        (tw, th), _ = cv2.getTextSize(label, font, font_scale, thickness)
+        (cw, ch), _ = cv2.getTextSize(conf_label, font, 0.5, 1)
+        # Position text above bounding box
+        text_x = max(0, x1)
+        text_y = max(th + 10, y1 - 10)
+        # Background rectangle for species name
+        cv2.rectangle(annotated, (text_x - 2, text_y - th - 8), (text_x + tw + 4, text_y + 4), (0, 0, 0), -1)
+        # Species name
+        cv2.putText(annotated, label, (text_x, text_y - 2), font, font_scale, (255, 255, 255), thickness, cv2.LINE_AA)
+        # Confidence badge (green)
+        cv2.rectangle(annotated, (text_x - 2, text_y + 6), (text_x + cw + 4, text_y + ch + 12), (0, 200, 100), -1)
+        cv2.putText(annotated, conf_label, (text_x, text_y + ch + 8), font, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
+    return annotated, detection_count, species_detected, confidences, None
+# ═══════════════════════════════════════════════════════════════════════════════
+# API ENDPOINTS
+# ═══════════════════════════════════════════════════════════════════════════════
+@app.get("/health")
+def health():
+    return {
+        "ok": True,
+        "model_loaded": model is not None,
+        "roboflow_configured": bool(ROBOFLOW_API_KEY),
+        "version": "3.1",
+        "timestamp": datetime.now().isoformat()
+    }
+@app.get("/", response_class=HTMLResponse)
+def home():
+    with open("index.html", "r", encoding="utf-8") as f:
+        return f.read()
+@app.get("/species")
+def list_species():
+    """List all supported species configurations"""
+    return {k: {
+        "display_name": v["display_name"],
+        "scientific_name": v["scientific_name"],
+        "detection_method": "Roboflow API" if v.get("use_roboflow") else "Local YOLO"
+    } for k, v in SPECIES_CONFIG.items() if k != "default"}
+@app.post("/detect")
+async def detect(
+    files: List[UploadFile] = File(...),
+    pixels_per_mm: Optional[float] = Query(default=None),
+    species: Optional[str] = Query(default="vannamei")
+):
+    calibration = pixels_per_mm if pixels_per_mm else PIXELS_PER_MM
+    species_config = get_species_config(species)
+    use_roboflow = species_config.get("use_roboflow", False)
+    per_image = []
+    all_lengths: List[float] = []
+    all_weights: List[float] = []
+    overall_total = 0
+    for up in files:
+        suffix = os.path.splitext(up.filename)[1] or ".jpg"
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+            tmp.write(await up.read())
+            image_path = tmp.name
+        try:
+            # ═══════════════════════════════════════════════════════════════════
+            # ROUTE: Use Roboflow for Bass, Local YOLO for Shrimp/Prawn
+            # ═══════════════════════════════════════════════════════════════════
+            if use_roboflow:
+                # BASS DETECTION via Roboflow API
+                # Shows: COUNT + SPECIES + CONFIDENCE only (no length/weight)
+                print(f"🐟 Using Roboflow API for bass detection: {up.filename}")
+                annotated, detection_count, species_list, confidences, error = process_bass_image(
+                    image_path, species_config, calibration
+                )
+                if error:
+                    per_image.append({
+                        "filename": up.filename,
+                        "error": error,
+                        "shrimp_count": 0,
+                        "specimen_count": 0,
+                        "average_length_mm": 0.0,
+                        "lengths_mm": [],
+                        "annotated_image_png_base64": ""
+                    })
+                    continue
+                overall_total += detection_count
+                # Encode annotated image
+                annotated_bgr = cv2.cvtColor(annotated, cv2.COLOR_RGB2BGR)
+                ok, png = cv2.imencode(".png", annotated_bgr)
+                b64 = base64.b64encode(png.tobytes()).decode("utf-8") if ok else ""
+                # For bass: NO length/weight data (just count and species)
+                per_image.append({
+                    "filename": up.filename,
+                    "shrimp_count": detection_count,
+                    "specimen_count": detection_count,
+                    "species_detected": species_list,
+                    "confidences": [round(c * 100, 1) for c in confidences],
+                    "average_length_mm": 0,  # Not available for bass
+                    "lengths_mm": [],  # Not available for bass
+                    "weights_g": [],  # Not available for bass
+                    "summary": {
+                        "note": "Length/weight requires calibrated camera setup",
+                        "detection_count": detection_count,
+                        "species_found": list(set(species_list)),
+                        "avg_confidence": round(sum(confidences) / len(confidences) * 100, 1) if confidences else 0
+                    },
+                    "annotated_image_png_base64": b64,
+                    "detection_method": "Roboflow API"
+                })
+            else:
+                # SHRIMP/PRAWN DETECTION via Local YOLO model
+                # Shows: COUNT + LENGTH + WEIGHT (calibrated for shrimp tank)
+                print(f"🦐 Using local YOLO model for detection: {up.filename}")
+                results = model(image_path, verbose=False, conf=CONF_THRESHOLD)
+                r = results[0]
+                bgr = cv2.imread(image_path)
+                if bgr is None:
+                    per_image.append({
+                        "filename": up.filename,
+                        "error": "Could not read image.",
+                        "shrimp_count": 0,
+                        "average_length_mm": 0.0,
+                        "lengths_mm": [],
+                        "annotated_image_png_base64": ""
+                    })
+                    continue
+                rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+                overlay = rgb.copy()
+                lengths_mm = []
+                weights_g = []
+                text_labels = []
+                if r.masks is not None and r.boxes is not None:
+                    for mask, box in zip(r.masks, r.boxes):
+                        class_id = int(box.cls[0])
+                        class_name = model.names.get(class_id, str(class_id))
+                        conf = float(box.conf[0])
+                        if not is_target_class(class_name) or conf < CONF_THRESHOLD:
+                            continue
+                        if mask.xy is None or len(mask.xy) == 0:
+                            continue
+                        pts = np.array(mask.xy[0], dtype=np.float32)
+                        if pts.shape[0] < 2:
+                            continue
+                        max_px = max_pairwise_distance(pts)
+                        length_mm = max_px / calibration
+                        weight_g = estimate_weight(length_mm, species_config)
+                        lengths_mm.append(float(length_mm))
+                        weights_g.append(float(weight_g))
+                        # Draw mask
+                        pts_int = pts.astype(np.int32).reshape((-1, 1, 2))
+                        cv2.fillPoly(overlay, [pts_int], color=species_config["color"])
+                        # Label
+                        x1 = int(box.xyxy[0][0])
+                        y1 = int(box.xyxy[0][1])
+                        text_labels.append((x1, y1, f"{length_mm:.1f}mm | {weight_g:.2f}g"))
+                annotated = cv2.addWeighted(rgb, 1 - MASK_ALPHA, overlay, MASK_ALPHA, 0)
+                # Draw text
+                for (x, y, text) in text_labels:
+                    font = cv2.FONT_HERSHEY_SIMPLEX
+                    font_scale = 0.55
+                    thickness = 2
+                    (tw, th), _ = cv2.getTextSize(text, font, font_scale, thickness)
+                    x = max(0, x)
+                    y = max(th + 8, y - 8)
+                    cv2.rectangle(annotated, (x - 2, y - th - 8), (x + tw + 4, y + 4), (0, 0, 0), -1)
+                    cv2.putText(annotated, text, (x, y - 2), font, font_scale, (255, 255, 255), thickness, cv2.LINE_AA)
+                specimen_count = len(lengths_mm)
+                avg_len = float(np.mean(lengths_mm)) if specimen_count > 0 else 0.0
+                avg_weight = float(np.mean(weights_g)) if specimen_count > 0 else 0.0
+                total_biomass = sum(weights_g)
+                overall_total += specimen_count
+                all_lengths.extend(lengths_mm)
+                all_weights.extend(weights_g)
+                # Encode image
+                annotated_bgr = cv2.cvtColor(annotated, cv2.COLOR_RGB2BGR)
+                ok, png = cv2.imencode(".png", annotated_bgr)
+                b64 = base64.b64encode(png.tobytes()).decode("utf-8") if ok else ""
+                per_image.append({
+                    "filename": up.filename,
+                    "shrimp_count": specimen_count,
+                    "specimen_count": specimen_count,
+                    "average_length_mm": round(avg_len, 2),
+                    "lengths_mm": [round(x, 2) for x in lengths_mm],
+                    "weights_g": [round(x, 3) for x in weights_g],
+                    "summary": {
+                        "average_length_mm": round(avg_len, 2),
+                        "average_weight_g": round(avg_weight, 3),
+                        "total_biomass_g": round(total_biomass, 3),
+                    },
+                    "annotated_image_png_base64": b64,
+                    "detection_method": "Local YOLO"
+                })
+        except Exception as e:
+            per_image.append({
+                "filename": up.filename,
+                "error": f"Processing failed: {str(e)}",
+                "shrimp_count": 0,
+                "average_length_mm": 0.0,
+                "lengths_mm": [],
+                "annotated_image_png_base64": ""
+            })
+        finally:
+            try:
+                os.remove(image_path)
+            except:
+                pass
+    # Overall stats
+    overall_avg_length = float(np.mean(all_lengths)) if all_lengths else 0.0
+    overall_avg_weight = float(np.mean(all_weights)) if all_weights else 0.0
+    total_biomass_g = sum(all_weights)
+    # Size distribution (only for shrimp with actual measurements)
+    size_dist = {"juvenile": 0, "sub-harvest": 0, "harvestable": 0, "optimal": 0}
+    for length in all_lengths:
+        cat = get_size_category(length, species_config)
+        size_dist[cat] += 1
+    total = len(all_lengths) if all_lengths else 1
+    size_pct = {k: round(v / total * 100, 1) for k, v in size_dist.items()}
+    # Generate histograms (only for shrimp with actual measurements)
+    histograms = {}
+    if all_lengths and not use_roboflow:
+        try:
+            # Length histogram - compact size
+            fig1, ax1 = plt.subplots(figsize=(6, 3), facecolor='#F8FAFC')
+            ax1.set_facecolor('#F8FAFC')
+            ax1.hist(all_lengths, bins=20, color='#0066FF', edgecolor='white', alpha=0.85)
+            ax1.axvline(x=overall_avg_length, color='#00C48C', linestyle='--', linewidth=2, label=f'Mean: {overall_avg_length:.1f}mm')
+            ax1.set_xlabel('Length (mm)', fontsize=10, color='#0F172A')
+            ax1.set_ylabel('Frequency', fontsize=10, color='#0F172A')
+            ax1.set_title(f'{species_config["display_name"]} Length Distribution', fontsize=11, fontweight='bold', color='#0F172A')
+            ax1.legend(loc='upper right', fontsize=8)
+            ax1.tick_params(colors='#0F172A', labelsize=8)
+            ax1.spines['top'].set_visible(False)
+            ax1.spines['right'].set_visible(False)
+            ax1.spines['bottom'].set_color('#CBD5E1')
+            ax1.spines['left'].set_color('#CBD5E1')
+            plt.tight_layout()
+            buf1 = io.BytesIO()
+            fig1.savefig(buf1, format="png", dpi=120, facecolor='#F8FAFC', bbox_inches='tight')
+            plt.close(fig1)
+            histograms["length_histogram_base64"] = base64.b64encode(buf1.getvalue()).decode("utf-8")
+        except Exception as e:
+            print(f"Length histogram error: {e}")
+        try:
+            # Weight histogram - compact size
+            fig2, ax2 = plt.subplots(figsize=(6, 3), facecolor='#F8FAFC')
+            ax2.set_facecolor('#F8FAFC')
+            ax2.hist(all_weights, bins=20, color='#7B61FF', edgecolor='white', alpha=0.85)
+            ax2.axvline(x=overall_avg_weight, color='#00C48C', linestyle='--', linewidth=2, label=f'Mean: {overall_avg_weight:.1f}g')
+            ax2.set_xlabel('Weight (g)', fontsize=10, color='#0F172A')
+            ax2.set_ylabel('Frequency', fontsize=10, color='#0F172A')
+            ax2.set_title(f'{species_config["display_name"]} Weight Distribution', fontsize=11, fontweight='bold', color='#0F172A')
+            ax2.legend(loc='upper right', fontsize=8)
+            ax2.tick_params(colors='#0F172A', labelsize=8)
+            ax2.spines['top'].set_visible(False)
+            ax2.spines['right'].set_visible(False)
+            ax2.spines['bottom'].set_color('#CBD5E1')
+            ax2.spines['left'].set_color('#CBD5E1')
+            plt.tight_layout()
+            buf2 = io.BytesIO()
+            fig2.savefig(buf2, format="png", dpi=120, facecolor='#F8FAFC', bbox_inches='tight')
+            plt.close(fig2)
+            histograms["weight_histogram_base64"] = base64.b64encode(buf2.getvalue()).decode("utf-8")
+        except Exception as e:
+            print(f"Weight histogram error: {e}")
+    # Build response based on detection type
+    if use_roboflow:
+        # BASS response - no length/weight data
+        return JSONResponse({
+            "timestamp": datetime.now().isoformat(),
+            "species": species,
+            "species_info": {
+                "display_name": species_config["display_name"],
+                "scientific_name": species_config["scientific_name"]
+            },
+            "detection_method": "Roboflow API",
+            "calibration_pixels_per_mm": calibration,
+            "overall_summary": {
+                "total_specimens": overall_total,
+                "note": "Length/weight measurement requires calibrated camera setup. Currently showing detection count only.",
+                "average_length_mm": 0,
+                "average_weight_g": 0,
+                "total_biomass_g": 0,
+            },
+            "histograms": {},  # No histograms for bass
+            "per_image": per_image,
+            # Legacy fields
+            "overall_total_shrimp": overall_total,
+            "overall_average_length_mm": 0,
+            "histogram_png_base64": ""
+        })
+    else:
+        # SHRIMP response - full length/weight data
+        return JSONResponse({
+            "timestamp": datetime.now().isoformat(),
+            "species": species,
+            "species_info": {
+                "display_name": species_config["display_name"],
+                "scientific_name": species_config["scientific_name"]
+            },
+            "detection_method": "Local YOLO",
+            "calibration_pixels_per_mm": calibration,
+            "overall_summary": {
+                "total_specimens": overall_total,
+                "average_length_mm": round(overall_avg_length, 2),
+                "average_weight_g": round(overall_avg_weight, 3),
+                "total_biomass_g": round(total_biomass_g, 3),
+                "total_biomass_kg": round(total_biomass_g / 1000, 6),
+                "size_distribution": {
+                    "counts": size_dist,
+                    "percentages": size_pct
+                },
+                "length_stats": {
+                    "min": round(min(all_lengths), 2) if all_lengths else 0,
+                    "max": round(max(all_lengths), 2) if all_lengths else 0,
+                    "std": round(float(np.std(all_lengths)), 2) if all_lengths else 0
+                },
+                "weight_stats": {
+                    "min": round(min(all_weights), 3) if all_weights else 0,
+                    "max": round(max(all_weights), 3) if all_weights else 0,
+                    "std": round(float(np.std(all_weights)), 3) if all_weights else 0
+                }
+            },
+            "histograms": histograms,
+            "per_image": per_image,
+            # Legacy fields
+            "overall_total_shrimp": overall_total,
+            "overall_average_length_mm": round(overall_avg_length, 2),
+            "histogram_png_base64": histograms.get("length_histogram_base64", "")
+        })
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8002, reload=True)