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app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import json
+import os
+from tensorflow.keras.models import load_model
+
+# ─── Load model ───────────────────────────────────────────────────────────────
+model = load_model("custom_cnn.h5")
+IMG_SIZE    = 224
+NUM_OUTPUTS = model.output_shape[-1]   # auto-detects 3-class or 16-class
+
+# ─── Class / cluster labels ───────────────────────────────────────────────────
+# Priority 1: class_labels.json saved alongside the model (from the 16-class notebook)
+# Priority 2: fallback cluster names for the 3-class K-Means model
+if os.path.exists("class_labels.json"):
+    with open("class_labels.json") as f:
+        CLASS_NAMES = json.load(f)["classes"]
+else:
+    # 3-class K-Means cluster model fallback
+    CLASS_NAMES = [f"Cluster {i}" for i in range(NUM_OUTPUTS)]
+
+# ─── Which actual pathology classes are dominant in each cluster ──────────────
+# These come from analysing your K-Means cluster assignments vs ground-truth labels.
+# REPLACE these lists with the real counts from your own cluster analysis notebook.
+CLUSTER_DOMINANT = {
+    "Cluster 0": [
+        ("Normal",                    0.38),
+        ("Mild Ventriculomegaly",     0.22),
+        ("Arnold–Chiari Malformation",0.15),
+        ("Moderate Ventriculomegaly", 0.14),
+        ("Hydranencephaly",           0.11),
+    ],
+    "Cluster 1": [
+        ("Severe Ventriculomegaly",   0.35),
+        ("Dandy–Walker Malformation", 0.25),
+        ("Holoprosencephaly",         0.18),
+        ("Agenesis of Corpus Callosum",0.13),
+        ("Intracranial Tumors",       0.09),
+    ],
+    "Cluster 2": [
+        ("Intracranial Tumors",       0.30),
+        ("Intracranial Hemorrhages",  0.28),
+        ("Holoprosencephaly",         0.20),
+        ("Dandy–Walker Malformation", 0.12),
+        ("Agenesis of Corpus Callosum",0.10),
+    ],
+}
+
+# For the 16-class model, dominant "classes in cluster" = top-5 softmax outputs
+USE_SOFTMAX_DOMINANT = (NUM_OUTPUTS > 3)
+
+# ─── All 16 ground-truth class names for the dropdown ────────────────────────
+ALL_GT_CLASSES = [
+    "Normal",
+    "Mild Ventriculomegaly",
+    "Moderate Ventriculomegaly",
+    "Severe Ventriculomegaly",
+    "Arnold–Chiari Malformation",
+    "Hydranencephaly",
+    "Agenesis of Corpus Callosum",
+    "Dandy–Walker Malformation",
+    "Intracranial Tumors",
+    "Intracranial Hemorrhages",
+    "Holoprosencephaly",
+    "Cerebellar Hypoplasia",
+    "Microcephaly",
+    "Macrocephaly",
+    "Lissencephaly",
+    "Unknown / Not provided",
+]
+
+# ─── Preprocessing — mirrors the paper §3B pipeline ──────────────────────────
+def preprocess(image: np.ndarray) -> np.ndarray:
+    """Gaussian blur → median filter → CLAHE → normalize [0,1]."""
+    if image is None:
+        return None
+    img = image.astype(np.uint8)
+    # To grayscale
+    if img.ndim == 3 and img.shape[2] == 3:
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    else:
+        gray = img if img.ndim == 2 else img[:, :, 0]
+    # §3B-2: Gaussian + median
+    blurred  = cv2.GaussianBlur(gray, (5, 5), sigmaX=1.0)
+    median   = cv2.medianBlur(blurred, 5)
+    # §3B-3: CLAHE
+    clahe    = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    enhanced = clahe.apply(median)
+    # Back to RGB float32 [0,1]
+    rgb = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB).astype(np.float32) / 255.0
+    return rgb
+
+# ─── EMOJI badges for ranks ───────────────────────────────────────────────────
+RANK_EMOJI = ["🥇", "🥈", "🥉", "4️⃣", "5️⃣"]
+
+# ─── Progress-bar helper ──────────────────────────────────────────────────────
+def pct_bar(value: float, width: int = 28) -> str:
+    filled = round(value * width)
+    return "█" * filled + "░" * (width - filled)
+
+# ─── Main prediction function ─────────────────────────────────────────────────
+def predict(image, actual_class):
+    if image is None:
+        empty = "Upload an ultrasound image to begin."
+        return empty, empty, empty
+
+    # ── Preprocess & predict ──────────────────────────────────────────────────
+    proc  = preprocess(image)
+    resized = cv2.resize(proc, (IMG_SIZE, IMG_SIZE))
+    inp   = np.expand_dims(resized, axis=0)
+    probs = model.predict(inp, verbose=0)[0]   # shape: (num_classes,)
+
+    top5_idx = np.argsort(probs)[::-1][:5]
+    pred_idx   = top5_idx[0]
+    pred_label = CLASS_NAMES[pred_idx]
+    confidence = probs[pred_idx] * 100.0
+
+    # ── Panel 1: Prediction cluster ───────────────────────────────────────────
+    cluster_lines = [
+        "┌─────────────────────────────────────────┐",
+        f"│  PREDICTED CLUSTER / CLASS              │",
+        "├─────────────────────────────────────────┤",
+        f"│  {pred_label:<39} │",
+        f"│  Confidence : {confidence:>6.2f}%                    │",
+        "└─────────────────────────────────────────┘",
+        "",
+        "All cluster probabilities:",
+        "─" * 43,
+    ]
+    for i, (cname, p) in enumerate(zip(CLASS_NAMES, probs)):
+        marker = " ◀ PREDICTED" if i == pred_idx else ""
+        cluster_lines.append(
+            f"  {cname:<35}  {p*100:5.1f}%{marker}"
+        )
+    cluster_text = "\n".join(cluster_lines)
+
+    # ── Panel 2: Top-5 dominant classes ──────────────────────────────────────
+    if USE_SOFTMAX_DOMINANT:
+        # 16-class model — dominant = top-5 softmax outputs
+        dominant = [(CLASS_NAMES[i], float(probs[i])) for i in top5_idx]
+        source_note = f"(direct softmax outputs from {NUM_OUTPUTS}-class model)"
+    else:
+        # 3-class cluster model — look up pre-computed dominant pathologies
+        dominant = CLUSTER_DOMINANT.get(
+            pred_label,
+            [(f"Class {j}", 0.2) for j in range(5)]
+        )
+        source_note = f"(pathologies most common in {pred_label})"
+
+    top5_lines = [
+        f"TOP 5 DOMINANT PATHOLOGY CLASSES  {source_note}",
+        "─" * 63,
+        "",
+    ]
+    for rank, (cname, score) in enumerate(dominant):
+        bar   = pct_bar(score)
+        emoji = RANK_EMOJI[rank]
+        top5_lines.append(
+            f"  {emoji}  {cname:<40} {bar}  {score*100:5.1f}%"
+        )
+    top5_text = "\n".join(top5_lines)
+
+    # ── Panel 3: Actual class comparison ─────────────────────────────────────
+    if not actual_class or actual_class == "Unknown / Not provided":
+        actual_lines = [
+            "ℹ️  No ground-truth label provided.",
+            "",
+            "Select the actual class from the dropdown",
+            "on the left to see a correctness check.",
+        ]
+    else:
+        # For cluster model: check if actual class appears in the top-5 dominant list
+        dominant_names = [d[0] for d in dominant]
+        in_top5 = actual_class in dominant_names
+
+        # For 16-class model: direct label match
+        if USE_SOFTMAX_DOMINANT:
+            correct = (actual_class == pred_label)
+            match_str = "✅  CORRECT PREDICTION" if correct else f"❌  INCORRECT  (model predicted '{pred_label}')"
+        else:
+            # Cluster model: soft match — is the actual class in the cluster's top-5?
+            if in_top5:
+                rank_pos = dominant_names.index(actual_class) + 1
+                match_str = f"✅  CORRECT CLUSTER  ('{actual_class}' is #{rank_pos} in {pred_label})"
+            else:
+                match_str = (
+                    f"⚠️  PARTIAL MISS  ('{actual_class}' not in top-5 of {pred_label})\n"
+                    f"   This may indicate a cluster assignment issue or borderline case."
+                )
+
+        actual_lines = [
+            "GROUND TRUTH vs PREDICTION",
+            "─" * 43,
+            "",
+            f"  Actual class   :  {actual_class}",
+            f"  Predicted      :  {pred_label}  ({confidence:.1f}%)",
+            "",
+            f"  {match_str}",
+            "",
+            "─" * 43,
+            "Top-5 dominant classes in predicted cluster:",
+        ]
+        for rank, (cname, score) in enumerate(dominant):
+            tick = "  ✓" if cname == actual_class else "   "
+            actual_lines.append(f"  {tick} {rank+1}. {cname:<38} {score*100:.1f}%")
+
+    actual_text = "\n".join(actual_lines)
+
+    return cluster_text, top5_text, actual_text
+
+
+# ─── Gradio UI ────────────────────────────────────────────────────────────────
+CSS = """
+body, .gradio-container { background: #0d1117 !important; }
+.gr-box, .gr-panel    { background: #161b22 !important; border: 1px solid #30363d !important; }
+.gr-button            { background: #238636 !important; color: #fff !important; border: none !important; }
+.gr-button:hover      { background: #2ea043 !important; }
+.output-text textarea { font-family: 'Courier New', monospace !important; font-size: 13px !important;
+                        background: #0d1117 !important; color: #e6edf3 !important;
+                        border: 1px solid #30363d !important; }
+label span            { color: #8b949e !important; }
+h1, h2, h3            { color: #e6edf3 !important; }
+"""
+
+with gr.Blocks(css=CSS, title="Fetal Brain MRI Classifier 🧠") as demo:
+    gr.Markdown("""
+    # 🧠 Fetal Brain MRI Classifier
+    #### Ultrasound anomaly detection — Standard CNN / Xception transfer learning
+    Upload a fetal ultrasound image, optionally select the known ground-truth class, then click **Submit**.
+    """)
+
+    with gr.Row():
+        # ── Left column: inputs ──────────────────────────────────────────────
+        with gr.Column(scale=1):
+            image_input = gr.Image(
+                type="numpy",
+                label="Ultrasound Image",
+                image_mode="RGB",
+            )
+            actual_input = gr.Dropdown(
+                choices=ALL_GT_CLASSES,
+                value="Unknown / Not provided",
+                label="Actual Ground-Truth Class (optional)",
+            )
+            with gr.Row():
+                clear_btn  = gr.Button("Clear")
+                submit_btn = gr.Button("Submit", variant="primary")
+
+        # ── Right column: outputs ────────────────────────────────────────────
+        with gr.Column(scale=2):
+            cluster_out = gr.Textbox(
+                label="🏆  Predicted Cluster / Class",
+                lines=14,
+                interactive=False,
+            )
+            top5_out = gr.Textbox(
+                label="📊  Top 5 Dominant Pathology Classes",
+                lines=10,
+                interactive=False,
+            )
+            actual_out = gr.Textbox(
+                label="✅  Actual Class Comparison",
+                lines=12,
+                interactive=False,
+            )
+
+    # ── Wire up events ───────────────────────────────────────────────────────
+    submit_btn.click(
+        fn=predict,
+        inputs=[image_input, actual_input],
+        outputs=[cluster_out, top5_out, actual_out],
+    )
+    clear_btn.click(
+        fn=lambda: (None, "Unknown / Not provided", "", "", ""),
+        inputs=[],
+        outputs=[image_input, actual_input, cluster_out, top5_out, actual_out],
+    )
+
+demo.launch()

custom_cnn.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2186540e651e7019bb211572387d72b848a65790579322f58726a4a6c3fe9b2a
+size 134080104

requirements.txt

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+tensorflow==2.20.0
+gradio
+numpy
+opencv-python-headless