Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from safetensors import safe_open
+
+# ✅ Constants
+IMG_SIZE = 224
+CLASS_NAMES = ["Fractured", "Non-Fractured"]
+SAFETENSOR_PATH = "osteologic.safetensors"
+
+# ✅ Step 1: Rebuild architecture
+def build_model():
+    inputs = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
+    base_model = tf.keras.applications.MobileNetV2(weights=None, include_top=False, input_tensor=inputs)
+    x = base_model.output
+    x = tf.keras.layers.GlobalAveragePooling2D()(x)
+    x = tf.keras.layers.Dense(128, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)
+    x = tf.keras.layers.Dropout(0.5)(x)
+    outputs = tf.keras.layers.Dense(len(CLASS_NAMES), activation="softmax")(x)
+    model = tf.keras.Model(inputs, outputs)
+    return model
+
+# ✅ Step 2: Load weights from .safetensors
+def load_weights(model, path=SAFETENSOR_PATH):
+    with safe_open(path, framework="pt", device="cpu") as f:
+        for layer in model.layers:
+            if isinstance(layer, (tf.keras.layers.Conv2D, tf.keras.layers.Dense)):
+                w_key = f"{layer.name}.weight"
+                b_key = f"{layer.name}.bias"
+                if w_key in f.keys() and b_key in f.keys():
+                    weights = f.get_tensor(w_key)
+                    bias = f.get_tensor(b_key)
+                    # Adjust shape if needed (PyTorch → TF)
+                    if isinstance(layer, tf.keras.layers.Conv2D):
+                        weights = weights.transpose(2, 3, 1, 0)  # [out, in, h, w] → [h, w, in, out]
+                    layer.set_weights([weights, bias])
+    return model
+
+# ✅ Step 3: Build and load model
+model = build_model()
+model = load_weights(model)
+
+# ✅ Step 4: Prediction function
+def predict(image: Image.Image):
+    image = image.resize((IMG_SIZE, IMG_SIZE)).convert("RGB")
+    arr = np.array(image) / 255.0
+    arr = arr.reshape(1, IMG_SIZE, IMG_SIZE, 3)
+    preds = model.predict(arr)[0]
+    label = CLASS_NAMES[np.argmax(preds)]
+    confidence = round(float(np.max(preds)), 3)
+    return f"{label} ({confidence})"
+
+# ✅ Step 5: Gradio interface
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Radiograph"),
+    outputs=gr.Text(label="Prediction"),
+    title="🦴 OsteoLogic Fracture Detector",
+    description="Upload a radiograph to detect fractures using safetensors-powered MobileNetV2."
+).launch()