Spaces:
Running on Zero
Running on Zero
Commit Β·
e238bb7
1
Parent(s): 86072ea
update style
Browse files
app.py
CHANGED
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@@ -897,44 +897,265 @@ example_images_cnt = [f for f in glob("example_imgs/cnt/*")]
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example_tracking_zips = [f for f in glob("example_imgs/tra/*.zip")]
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# ===== Gradio UI =====
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#density_map_output .image-container {
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height: 500px !important;
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}
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#density_map_output img {
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height: 480px !important;
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width: auto !important;
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max-width: 90% !important;
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object-fit: contain !important;
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}
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"""
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) as demo:
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gr.Markdown(
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"""
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# π¬ MicroscopyMatching: Microscopy Image Analysis Suite
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@@ -946,7 +1167,7 @@ with gr.Blocks(
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### π‘ Technical Details:
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**MicroscopyMatching** - A general-purpose microscopy image analysis toolkit based on
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### π Note:
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@@ -972,7 +1193,7 @@ with gr.Blocks(
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1. Upload an image or select an example image (supports various formats: .png, .jpg, .tif)
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2. (Optional) Specify a target object with a bounding box and select "Yes", or click "Run Segmentation" directly
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3. Click "Run Segmentation"
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4. View the segmentation results, download the original predicted mask (.tif format); if needed, click "Clear Selection" to choose a new image
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π€ Rate and submit feedback to help us improve the model!
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"""
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maximum=1.0,
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step=0.05,
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value=0.5,
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label="
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)
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# Download Original Prediction
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1. Upload an image or select an example image (supports multiple formats: .png, .jpg, .tif)
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2. (Optional) Specify a target object with a bounding box and select "Yes", or click "Run Counting" directly
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3. Click "Run Counting"
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4. View the density map, download the original prediction (.npy format); if needed, click "Clear Selection" to choose a new image to run
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π€ Rate and submit feedback to help us improve the model!
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"""
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maximum=1.0,
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step=0.05,
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value=0.3,
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label="
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)
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count_status = gr.Textbox(
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label="π Statistics",
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1. Upload a ZIP file or select from the example library. The ZIP should contain a sequence of TIF images named in chronological order (e.g., t000.tif, t001.tif...)
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2. (Optional) Specify a target object with a bounding box on the first frame and select "Yes", or click "Run Tracking" directly
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3. Click "Run Tracking"
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4.
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π€ Rate and submit feedback to help us improve the model!
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maximum=1.0,
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step=0.05,
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value=0.3,
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label="
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)
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track_output = gr.Textbox(
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example_tracking_zips = [f for f in glob("example_imgs/tra/*.zip")]
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# ===== Gradio UI =====
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CSS = """
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/* ββ Layout ββββββββββββββββββββββββββββββββββββββββββββ */
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.gradio-container {
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max-width: 1380px !important;
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margin: 0 auto !important;
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font-family: 'Inter', 'Segoe UI', system-ui, sans-serif !important;
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}
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/* ββ Header markdown polish βββββββββββββββββββββββββββββ */
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.gradio-container .prose h1 {
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font-size: 2rem !important;
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font-weight: 700 !important;
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color: #1e293b !important;
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letter-spacing: -0.5px !important;
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margin-bottom: 10px !important;
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}
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.gradio-container .prose h3 {
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font-size: 1rem !important;
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font-weight: 600 !important;
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color: #0284c7 !important;
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margin-top: 14px !important;
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margin-bottom: 4px !important;
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}
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.gradio-container .prose p {
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margin-top: 4px !important;
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margin-bottom: 6px !important;
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color: #475569 !important;
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line-height: 1.7 !important;
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}
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.gradio-container .prose ul,
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.gradio-container .prose ol {
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margin-top: 4px !important;
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margin-bottom: 6px !important;
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}
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.gradio-container .prose li {
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color: #475569 !important;
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line-height: 1.7 !important;
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}
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/* ββ Top-level header section βββββββββββββββββββββββββββ */
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.gradio-container > .gap > .prose:first-child {
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background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 50%, #f0fdf4 100%) !important;
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border: 1px solid #bae6fd !important;
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border-radius: 16px !important;
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padding: 28px 36px !important;
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margin-bottom: 20px !important;
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box-shadow: 0 4px 20px rgba(14,165,233,0.08) !important;
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}
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/* ββ Tabs ββββββββββββββββββββββββββββββββββββββββββββββ */
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.tabs > .tab-nav {
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border-bottom: 2px solid #e2e8f0 !important;
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margin-bottom: 20px !important;
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gap: 4px !important;
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}
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.tabs button {
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font-size: 15px !important;
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font-weight: 600 !important;
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padding: 11px 24px !important;
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border-radius: 8px 8px 0 0 !important;
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color: #64748b !important;
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transition: color 0.15s, background 0.15s !important;
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}
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.tabs button:hover {
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color: #0ea5e9 !important;
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background: #f0f9ff !important;
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}
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.tabs button.selected {
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color: #0284c7 !important;
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border-bottom: 3px solid #0284c7 !important;
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background: transparent !important;
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}
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/* ββ Buttons βββββββββββββββββββββββββββββββββββββββββββ */
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button.primary {
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background: linear-gradient(135deg, #0284c7 0%, #0ea5e9 100%) !important;
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border: none !important;
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border-radius: 10px !important;
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color: #fff !important;
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font-weight: 600 !important;
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font-size: 15px !important;
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box-shadow: 0 3px 12px rgba(14,165,233,0.35) !important;
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transition: transform 0.12s ease, box-shadow 0.15s ease !important;
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}
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button.primary:hover {
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transform: translateY(-2px) !important;
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box-shadow: 0 6px 20px rgba(14,165,233,0.45) !important;
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}
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button.secondary {
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border-radius: 10px !important;
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font-weight: 500 !important;
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border: 1.5px solid #cbd5e1 !important;
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color: #475569 !important;
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transition: border-color 0.12s, color 0.12s, background 0.12s !important;
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}
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button.secondary:hover {
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border-color: #94a3b8 !important;
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color: #1e293b !important;
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background: #f8fafc !important;
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}
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/* ββ Blocks and panels βββββββββββββββββββββββββββββββββ */
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.gradio-container .block { border-radius: 14px !important; }
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.gradio-container .gr-form,
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.gradio-container .gr-box,
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.gradio-container .gr-panel {
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border-radius: 14px !important;
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border-color: #e2e8f0 !important;
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}
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/* ββ Labels ββββββββββββββββββββββββββββββββββββββββββββ */
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label { font-weight: 500 !important; color: #374151 !important; }
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/* ββ Image output ββββββββββββββββββββββββββββββββββββββ */
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.uniform-height {
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height: 480px !important;
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display: flex !important;
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align-items: center !important;
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justify-content: center !important;
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border-radius: 12px !important;
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background: #f8fafc !important;
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}
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.uniform-height img, .uniform-height canvas {
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max-height: 480px !important;
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object-fit: contain !important;
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}
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/* ββ Density map output ββββββββββββββββββββββββββββββββ */
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#density_map_output { height: 480px !important; }
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#density_map_output .image-container { height: 480px !important; }
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#density_map_output img {
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height: 460px !important;
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width: auto !important;
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max-width: 95% !important;
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object-fit: contain !important;
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}
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/* ββ Tab content description markdown βββββββββββββββββ */
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.tabitem .prose h2 {
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font-size: 1.3rem !important;
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font-weight: 700 !important;
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color: #1e293b !important;
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margin-top: 0 !important;
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margin-bottom: 10px !important;
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padding-bottom: 8px !important;
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border-bottom: 2px solid #e0f2fe !important;
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}
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.tabitem .prose:nth-child(2) {
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background: #f8fafc !important;
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border: 1px solid #e2e8f0 !important;
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border-radius: 10px !important;
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padding: 12px 18px !important;
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margin-bottom: 16px !important;
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}
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.tabitem .prose:nth-child(2) p,
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.tabitem .prose:nth-child(2) li {
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font-size: 0.91rem !important;
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color: #64748b !important;
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}
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.tabitem .prose:nth-child(2) strong {
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color: #0f172a !important;
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}
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/* ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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+
DARK MODE (.dark is added to <html> by Gradio)
|
| 1065 |
+
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ */
|
| 1066 |
+
|
| 1067 |
+
/* ββ Header text βββββββββββββββββββββββββββββββββββββββ */
|
| 1068 |
+
.dark .gradio-container .prose h1 {
|
| 1069 |
+
color: #e2e8f0 !important;
|
| 1070 |
+
}
|
| 1071 |
+
.dark .gradio-container .prose h3 {
|
| 1072 |
+
color: #38bdf8 !important;
|
| 1073 |
+
}
|
| 1074 |
+
.dark .gradio-container .prose p,
|
| 1075 |
+
.dark .gradio-container .prose li {
|
| 1076 |
+
color: #94a3b8 !important;
|
| 1077 |
+
}
|
| 1078 |
+
|
| 1079 |
+
/* ββ Top-level header card βββββββββββββββββββββββββββββ */
|
| 1080 |
+
.dark .gradio-container > .gap > .prose:first-child {
|
| 1081 |
+
background: linear-gradient(135deg, #0c1a2e 0%, #0f2942 50%, #0d1f12 100%) !important;
|
| 1082 |
+
border-color: #1e3a5f !important;
|
| 1083 |
+
box-shadow: 0 4px 20px rgba(0,0,0,0.4) !important;
|
| 1084 |
+
}
|
| 1085 |
+
|
| 1086 |
+
/* ββ Tabs ββββββββββββββββββββββββββββββββββββββββββββββ */
|
| 1087 |
+
.dark .tabs > .tab-nav {
|
| 1088 |
+
border-bottom-color: #334155 !important;
|
| 1089 |
+
}
|
| 1090 |
+
.dark .tabs button {
|
| 1091 |
+
color: #94a3b8 !important;
|
| 1092 |
+
}
|
| 1093 |
+
.dark .tabs button:hover {
|
| 1094 |
+
color: #38bdf8 !important;
|
| 1095 |
+
background: rgba(56,189,248,0.08) !important;
|
| 1096 |
+
}
|
| 1097 |
+
.dark .tabs button.selected {
|
| 1098 |
+
color: #38bdf8 !important;
|
| 1099 |
+
border-bottom-color: #38bdf8 !important;
|
| 1100 |
+
}
|
| 1101 |
+
|
| 1102 |
+
/* ββ Buttons βββββββββββββββββββββββββββββββββββββββββββ */
|
| 1103 |
+
.dark button.secondary {
|
| 1104 |
+
border-color: #475569 !important;
|
| 1105 |
+
color: #94a3b8 !important;
|
| 1106 |
+
background: transparent !important;
|
| 1107 |
+
}
|
| 1108 |
+
.dark button.secondary:hover {
|
| 1109 |
+
border-color: #64748b !important;
|
| 1110 |
+
color: #e2e8f0 !important;
|
| 1111 |
+
background: rgba(255,255,255,0.05) !important;
|
| 1112 |
+
}
|
| 1113 |
+
|
| 1114 |
+
/* ββ Blocks / panels βββββββββββββββββββββββββββββββββββ */
|
| 1115 |
+
.dark .gradio-container .gr-form,
|
| 1116 |
+
.dark .gradio-container .gr-box,
|
| 1117 |
+
.dark .gradio-container .gr-panel {
|
| 1118 |
+
border-color: #334155 !important;
|
| 1119 |
+
}
|
| 1120 |
+
|
| 1121 |
+
/* ββ Labels ββββββββββββββββββββββββββββββββββββββββββββ */
|
| 1122 |
+
.dark label {
|
| 1123 |
+
color: #cbd5e1 !important;
|
| 1124 |
+
}
|
| 1125 |
+
|
| 1126 |
+
/* ββ Image output area βββββββββββββββββββββββββββββββββ */
|
| 1127 |
+
.dark .uniform-height {
|
| 1128 |
+
background: #1e293b !important;
|
| 1129 |
+
}
|
| 1130 |
+
|
| 1131 |
+
/* ββ Tab content markdown ββββββββββββββββββββββββββββββ */
|
| 1132 |
+
.dark .tabitem .prose h2 {
|
| 1133 |
+
color: #e2e8f0 !important;
|
| 1134 |
+
border-bottom-color: #1e3a5f !important;
|
| 1135 |
+
}
|
| 1136 |
+
.dark .tabitem .prose:nth-child(2) {
|
| 1137 |
+
background: #1e293b !important;
|
| 1138 |
+
border-color: #334155 !important;
|
| 1139 |
+
}
|
| 1140 |
+
.dark .tabitem .prose:nth-child(2) p,
|
| 1141 |
+
.dark .tabitem .prose:nth-child(2) li {
|
| 1142 |
+
color: #94a3b8 !important;
|
| 1143 |
+
}
|
| 1144 |
+
.dark .tabitem .prose:nth-child(2) strong {
|
| 1145 |
+
color: #e2e8f0 !important;
|
| 1146 |
+
}
|
| 1147 |
+
"""
|
| 1148 |
|
| 1149 |
+
with gr.Blocks(
|
| 1150 |
+
title="Microscopy Analysis Suite",
|
| 1151 |
+
theme=gr.themes.Soft(
|
| 1152 |
+
primary_hue=gr.themes.colors.sky,
|
| 1153 |
+
secondary_hue=gr.themes.colors.slate,
|
| 1154 |
+
neutral_hue=gr.themes.colors.slate,
|
| 1155 |
+
font=gr.themes.GoogleFont("Inter"),
|
| 1156 |
+
),
|
| 1157 |
+
css=CSS,
|
| 1158 |
+
) as demo:
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1159 |
gr.Markdown(
|
| 1160 |
"""
|
| 1161 |
# π¬ MicroscopyMatching: Microscopy Image Analysis Suite
|
|
|
|
| 1167 |
|
| 1168 |
### π‘ Technical Details:
|
| 1169 |
|
| 1170 |
+
**MicroscopyMatching** - A general-purpose microscopy image analysis toolkit based on pre-trained Latent Diffusion Model
|
| 1171 |
|
| 1172 |
### π Note:
|
| 1173 |
|
|
|
|
| 1193 |
1. Upload an image or select an example image (supports various formats: .png, .jpg, .tif)
|
| 1194 |
2. (Optional) Specify a target object with a bounding box and select "Yes", or click "Run Segmentation" directly
|
| 1195 |
3. Click "Run Segmentation"
|
| 1196 |
+
4. View the segmentation results (you can adjust the overlay opacity by sliding the opacity bar below the visualization), download the original predicted mask (.tif format); if needed, click "Clear Selection" to choose a new image
|
| 1197 |
|
| 1198 |
π€ Rate and submit feedback to help us improve the model!
|
| 1199 |
"""
|
|
|
|
| 1245 |
maximum=1.0,
|
| 1246 |
step=0.05,
|
| 1247 |
value=0.5,
|
| 1248 |
+
label="πͺ Overlay Opacity"
|
| 1249 |
)
|
| 1250 |
|
| 1251 |
# Download Original Prediction
|
|
|
|
| 1378 |
1. Upload an image or select an example image (supports multiple formats: .png, .jpg, .tif)
|
| 1379 |
2. (Optional) Specify a target object with a bounding box and select "Yes", or click "Run Counting" directly
|
| 1380 |
3. Click "Run Counting"
|
| 1381 |
+
4. View the density map (you can adjust the density opacity by sliding the opacity bar below the visualization), download the original prediction (.npy format); if needed, click "Clear Selection" to choose a new image to run
|
| 1382 |
|
| 1383 |
π€ Rate and submit feedback to help us improve the model!
|
| 1384 |
"""
|
|
|
|
| 1436 |
maximum=1.0,
|
| 1437 |
step=0.05,
|
| 1438 |
value=0.3,
|
| 1439 |
+
label="πͺ Density Opacity"
|
| 1440 |
)
|
| 1441 |
count_status = gr.Textbox(
|
| 1442 |
label="π Statistics",
|
|
|
|
| 1572 |
1. Upload a ZIP file or select from the example library. The ZIP should contain a sequence of TIF images named in chronological order (e.g., t000.tif, t001.tif...)
|
| 1573 |
2. (Optional) Specify a target object with a bounding box on the first frame and select "Yes", or click "Run Tracking" directly
|
| 1574 |
3. Click "Run Tracking"
|
| 1575 |
+
4. View the tracking results (you can adjust the overlay opacity by sliding the opacity bar below the visualization), download the CTC format results; if needed, click "Clear Selection" to choose a new ZIP file to run
|
| 1576 |
|
| 1577 |
π€ Rate and submit feedback to help us improve the model!
|
| 1578 |
|
|
|
|
| 1634 |
maximum=1.0,
|
| 1635 |
step=0.05,
|
| 1636 |
value=0.3,
|
| 1637 |
+
label="πͺ Overlay Opacity"
|
| 1638 |
)
|
| 1639 |
|
| 1640 |
track_output = gr.Textbox(
|