Update app.py

app.py

@@ -59,19 +59,20 @@ classnames = [
 ]
 
 
-def predict(path):
-    image = path.reshape((224, 224, 3))
-    image = tf.keras.utils.img_to_array(image)
-    image = np.expand_dims(image, axis=0)
-    pred = model.predict(image, verbose=0)
-    pred = pred[0]
-    confidences = {classnames[i]: round(float(pred[i]), 2) for i in range(50)}
+def predict(image):
+    img = tf.image.resize(image, (224, 224))
+    
+    img = tf.cast(img, tf.float32) / 255.0
+    
+    pred = model.predict(tf.expand_dims(img, axis=0))
+    
+    confidences = {classnames[i]: float(pred[0][i]) for i in range(len(classnames))}
     return confidences
 
 
 gr.Interface(
     fn=predict,
-    inputs=gr.Image(shape=(224, 224)),
+    inputs=gr.Image(),
     outputs=gr.Label(num_top_classes=5),
     examples=[
         "Dalbergia oliveri.JPG",