Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import cv2
+
+# Load model
+model = tf.keras.models.load_model("model/model.h5")
+
+def preprocess_image(image):
+    image = np.array(image)
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    image = cv2.resize(image, (224, 224))
+    image = image / 255.0
+    image = image.reshape(1, 224, 224, 1)
+    return image
+
+def predict(image):
+    image = preprocess_image(image)
+    prediction = model.predict(image)[0][0]
+
+    if prediction > 0.5:
+        return {"Fractured": float(prediction), "Normal": float(1 - prediction)}
+    else:
+        return {"Normal": float(1 - prediction), "Fractured": float(prediction)}
+
+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"
+).launch()