feat: Add DICOM (.dcm) file upload support with patient metadata extraction

requirements-web.txt

@@ -1,6 +1,10 @@
 # Web Application Dependencies
 Flask==3.1.3
 flask-cors==6.0.0
+Flask-Caching>=2.0.0
+Flask-Limiter>=3.0.0
+flask-talisman>=1.1.0
+gunicorn>=21.2.0
 
 python-dotenv==1.2.2
 pymongo>=4.6.0
@@ -32,3 +36,7 @@ scipy>=1.10.0
 
 # Visualization
 plotly>=5.13.0
+
+# Medical Imaging
+pydicom>=2.4.0
+

static/css/style.css

@@ -15785,4 +15785,109 @@ section {
     .seg-transform-v3 .transform-arrow i {
         transform: rotate(90deg);
     }
+}
+
+/* Dummy Weights Banner styling */
+.dummy-weights-banner {
+    background-color: #fef3c7;
+    border-bottom: 1px solid #f59e0b;
+    color: #92400e;
+    padding: 0.75rem 1rem;
+    font-size: 0.9rem;
+    font-family: var(--font-primary);
+    position: relative;
+    z-index: 1000;
+}
+
+.dark-theme .dummy-weights-banner {
+    background-color: #78350f;
+    border-bottom: 1px solid #f59e0b;
+    color: #fef3c7;
+}
+
+.dummy-weights-banner .banner-content {
+    display: flex;
+    align-items: center;
+    gap: 0.75rem;
+}
+
+.dummy-weights-banner .banner-icon {
+    color: #d97706;
+    font-size: 1.2rem;
+    flex-shrink: 0;
+}
+
+.dummy-weights-banner .banner-text a {
+    color: #3b82f6;
+    text-decoration: underline;
+    font-weight: 600;
+}
+
+.dark-theme .dummy-weights-banner .banner-text a {
+    color: #60a5fa;
+}
+
+/* Patient & Scan Info Section (DICOM) */
+.patient-info-section {
+    margin-top: 1.5rem;
+    padding: 1.25rem;
+    background: rgba(255, 255, 255, 0.05);
+    border: 1px solid var(--gray-200);
+    border-radius: 12px;
+}
+
+.dark-theme .patient-info-section {
+    background: rgba(0, 0, 0, 0.2);
+    border: 1px solid var(--gray-700);
+}
+
+.patient-info-section .patient-info-title {
+    margin-top: 0;
+    margin-bottom: 1rem;
+    font-size: 1.1rem;
+    color: var(--planetary);
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+}
+
+.dark-theme .patient-info-section .patient-info-title {
+    color: var(--universe);
+}
+
+.patient-info-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(180px, 1fr));
+    gap: 1rem;
+}
+
+.patient-info-item {
+    display: flex;
+    flex-direction: column;
+    gap: 0.25rem;
+    padding: 0.75rem;
+    background: rgba(255, 255, 255, 0.5);
+    border-radius: 8px;
+    border: 1px solid rgba(0,0,0,0.05);
+}
+
+.dark-theme .patient-info-item {
+    background: rgba(255, 255, 255, 0.02);
+    border: 1px solid rgba(255,255,255,0.05);
+}
+
+.patient-info-item .info-label {
+    font-size: 0.75rem;
+    color: var(--gray-500);
+    font-weight: 500;
+}
+
+.patient-info-item .info-value {
+    font-size: 0.95rem;
+    font-weight: 600;
+    color: var(--gray-800);
+}
+
+.dark-theme .patient-info-item .info-value {
+    color: var(--gray-200);
 }

static/js/script.js

@@ -1,7 +1,7 @@
 // NeuroScan AI - Interactive JavaScript
 
 // ==================== Configuration ====================
-const API_BASE_URL = 'http://localhost:5000';
+const API_BASE_URL = window.location.origin;
 
 // ==================== Theme ====================
 const savedTheme = localStorage.getItem('theme');
@@ -234,15 +234,15 @@ function validateAndPreviewFile(file) {
     }
 
     // Validate file type
-    const validTypes = ['image/jpeg', 'image/jpg', 'image/png', 'image/tiff'];
-    const validExtensions = ['.jpg', '.jpeg', '.png', '.tif', '.tiff'];
+    const validTypes = ['image/jpeg', 'image/jpg', 'image/png', 'image/tiff', 'application/dicom', 'application/octet-stream'];
+    const validExtensions = ['.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dcm'];
 
     const fileName = file.name.toLowerCase();
     const isValidType = validTypes.includes(file.type) || 
                        validExtensions.some(ext => fileName.endsWith(ext));
 
     if (!isValidType) {
-        showNotification('Please upload a valid image file (JPG, PNG, TIF, TIFF)', 'error');
+        showNotification('Please upload a valid image file (JPG, PNG, TIF, TIFF, DCM)', 'error');
         return;
     }
 
@@ -259,9 +259,15 @@ function validateAndPreviewFile(file) {
 
 function previewImage(file) {
     const reader = new FileReader();
+    const isDicom = file.name.toLowerCase().endsWith('.dcm');
 
     reader.onload = (e) => {
-        elements.previewImage.src = e.target.result;
+        if (isDicom) {
+            // Render a target scanner SVG placeholder for DICOM files since raw binary is not directly viewable in browser
+            elements.previewImage.src = `data:image/svg+xml;base64,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`;
+        } else {
+            elements.previewImage.src = e.target.result;
+        }
         elements.uploadArea.style.display = 'none';
         elements.imagePreview.style.display = 'block';
         setAnalyzeControlsLoading(false);
@@ -771,6 +777,43 @@ function generateResultsHTML(results) {
                 </div>
         `;
 
+        // Render Patient & Scan info if DICOM metadata exists
+        if (report.patient_info) {
+            const pInfo = report.patient_info;
+            html += `
+                <!-- Patient & Scan Metadata (DICOM) -->
+                <div class="patient-info-section">
+                    <h4 class="patient-info-title"><i class="fas fa-id-card"></i> Patient & Scan Metadata (DICOM)</h4>
+                    <div class="patient-info-grid">
+                        <div class="patient-info-item">
+                            <span class="info-label">Patient Age</span>
+                            <span class="info-value">${pInfo.patient_age || 'N/A'}</span>
+                        </div>
+                        <div class="patient-info-item">
+                            <span class="info-label">Patient Gender</span>
+                            <span class="info-value">${pInfo.patient_sex || 'N/A'}</span>
+                        </div>
+                        <div class="patient-info-item">
+                            <span class="info-label">Imaging Modality</span>
+                            <span class="info-value">${pInfo.modality || 'N/A'}</span>
+                        </div>
+                        <div class="patient-info-item">
+                            <span class="info-label">Field Strength</span>
+                            <span class="info-value">${pInfo.magnetic_field_strength || 'N/A'}</span>
+                        </div>
+                        <div class="patient-info-item">
+                            <span class="info-label">Scanning Sequence</span>
+                            <span class="info-value">${pInfo.scanning_sequence || 'N/A'}</span>
+                        </div>
+                        <div class="patient-info-item">
+                            <span class="info-label">Manufacturer</span>
+                            <span class="info-value">${pInfo.manufacturer || 'N/A'}</span>
+                        </div>
+                    </div>
+                </div>
+            `;
+        }
+
         if (hasTumor && chars.detected) {
             const size = chars.estimated_size || {};
 

templates/index.html

@@ -33,7 +33,7 @@
     <noscript><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css"></noscript>
 
     <!-- Custom CSS (blocking — intentional: sets colours before paint) -->
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}?v=1.0.1">
     <!-- Performance optimisation layer — must load after style.css -->
     <link rel="stylesheet" href="{{ url_for('static', filename='css/performance.css') }}">
 </head>
@@ -91,8 +91,23 @@
         </div>
     </nav>
     <main id="main-content">
+
+        <!-- Dummy Weights Warning Banner -->
+        {% if dummy_weights %}
+        <div class="dummy-weights-banner">
+            <div class="container">
+                <div class="banner-content">
+                    <i class="fas fa-exclamation-triangle banner-icon"></i>
+                    <div class="banner-text">
+                        <strong>Demo Mode (Untrained Models):</strong> The application is currently using randomly-initialized dummy weights. Predictions will be random (around 50% confidence). To enable real diagnostics, please download the trained weights from the <a href="https://drive.google.com/file/d/1FsuQvdxakt4AYcjA_D06la4fJ2kKxA9E/view?usp=sharing" target="_blank">Google Drive ZIP</a> or <a href="https://drive.google.com/drive/folders/13_EYULCrG8GIAvfSmaWXzFhIvfn_udYh" target="_blank">Google Drive Folder</a> and replace the weights files in the project root.
+                    </div>
+                </div>
+            </div>
+        </div>
+        {% endif %}
 
     <!-- Hero Section -->
+
     <section id="home" class="hero-section">
         <div class="hero-background">
             <div class="blob blob-1"></div>
@@ -967,7 +982,7 @@ <h4>Multi-Format Support</h4>
                                 <span class="chip active">PNG</span>
                                 <span class="chip active">TIFF</span>
                                 <span class="chip active">TIF</span>
-                                <span class="chip coming">DICOM</span>
+                                <span class="chip active">DICOM</span>
                             </div>
                         </div>
 
@@ -1237,7 +1252,7 @@ <h3>Upload Your MRI Scan</h3>
                             </div>
 
                             <div class="upload-zone-v2" id="uploadArea">
-                                <input type="file" id="fileInput" accept="image/*,.tif,.tiff" hidden>
+                                <input type="file" id="fileInput" accept="image/*,.tif,.tiff,.dcm" hidden>
 
                                 <div class="upload-visual">
                                     <div class="upload-animation">
@@ -1268,6 +1283,7 @@ <h4>Drop MRI scan here</h4>
                                     <span class="format-badge"><i class="fas fa-check"></i> PNG</span>
                                     <span class="format-badge"><i class="fas fa-check"></i> TIF</span>
                                     <span class="format-badge"><i class="fas fa-check"></i> TIFF</span>
+                                    <span class="format-badge"><i class="fas fa-check"></i> DCM</span>
                                 </div>
                             </div>
 
@@ -3888,7 +3904,7 @@ <h2 class="section-title">Proven Results</h2>
 });
     </script>
     <!-- Custom JavaScript with cache busting -->
-    <script src="{{ url_for('static', filename='js/script.js') }}" defer></script>
+    <script src="{{ url_for('static', filename='js/script.js') }}?v=1.0.1" defer></script>
 
 </body>
 </html>

tests/conftest.py

@@ -17,6 +17,8 @@
 for key in REQUIRED_ENV_VARS:
     os.environ.setdefault(key, "test")
 
+os.environ.setdefault("TESTING", "true")
+
 
 def _install_tensorflow_stub():
     if importlib.util.find_spec("tensorflow") is not None:
@@ -163,6 +165,7 @@ def app_state(app_module, monkeypatch, tmp_path):
     app_module.app.config["SCAN_HISTORY_FOLDER"] = str(history_dir)
 
     app_module.models_loaded = True
+    app_module.models_load_attempted = True
     app_module.classification_models = []
     app_module.segmentation_models = []
     app_module.memory_storage = {"users": {}, "scan_history": {}}

tests/unit/test_dicom.py

@@ -0,0 +1,60 @@
+import numpy as np
+import pytest
+from unittest.mock import MagicMock, patch
+import app
+
+def test_process_dicom_file():
+    # Set up mock dataset
+    mock_ds = MagicMock()
+    mock_ds.PatientAge = "045Y"
+    mock_ds.PatientSex = "M"
+    mock_ds.Modality = "MR"
+    mock_ds.MagneticFieldStrength = 3.0
+    mock_ds.ScanningSequence = "SE"
+    mock_ds.Manufacturer = "SIEMENS"
+    mock_ds.StudyDate = "20260712"
+
+    # 2D grayscale array of size 64x64
+    mock_ds.pixel_array = np.full((64, 64), 128, dtype=np.uint16)
+
+    with patch("pydicom.dcmread", return_value=mock_ds):
+        img_bgr, metadata = app.process_dicom_file("mock_file.dcm")
+
+        # Verify BGR conversion
+        assert img_bgr.shape == (64, 64, 3)
+        assert img_bgr.dtype == np.uint8
+
+        # Verify metadata extraction & normalization
+        assert metadata["patient_age"] == "45"
+        assert metadata["patient_sex"] == "M"
+        assert metadata["modality"] == "MR"
+        assert metadata["magnetic_field_strength"] == "3.0T"
+        assert metadata["scanning_sequence"] == "SE"
+        assert metadata["manufacturer"] == "SIEMENS"
+        assert metadata["study_date"] == "20260712"
+
+def test_process_dicom_file_missing_fields():
+    # Set up mock dataset with no metadata attributes
+    # Delete optional properties to test missing field fallbacks
+    mock_ds = MagicMock()
+    del mock_ds.PatientAge
+    del mock_ds.PatientSex
+    del mock_ds.Modality
+    del mock_ds.MagneticFieldStrength
+    del mock_ds.ScanningSequence
+    del mock_ds.Manufacturer
+    del mock_ds.StudyDate
+
+    mock_ds.pixel_array = np.full((64, 64), 100, dtype=np.uint16)
+
+    with patch("pydicom.dcmread", return_value=mock_ds):
+        img_bgr, metadata = app.process_dicom_file("mock_file.dcm")
+
+        # Verify it falls back to 'N/A' safely without throwing AttributeErrors
+        assert metadata["patient_age"] == "N/A"
+        assert metadata["patient_sex"] == "N/A"
+        assert metadata["modality"] == "N/A"
+        assert metadata["magnetic_field_strength"] == "N/A"
+        assert metadata["scanning_sequence"] == "N/A"
+        assert metadata["manufacturer"] == "N/A"
+        assert metadata["study_date"] == "N/A"