Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from utils import preprocess_image
+
+# Load model
+model = tf.keras.models.load_model("model/model.h5")
+
+def predict(image):
+    processed_image = preprocess_image(image)
+    prediction = model.predict(processed_image)[0][0]
+
+    if prediction > 0.5:
+        return {
+            "Fractured": float(prediction),
+            "Normal": float(1 - prediction)
+        }
+    else:
+        return {
+            "Normal": float(1 - prediction),
+            "Fractured": float(prediction)
+        }
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),
+    title="Bone Fracture Detection",
+    description="Upload an X-ray image to detect bone fracture using deep learning"
+)
+
+interface.launch()