Update app.py

app.py

@@ -1,11 +1,14 @@
 import gradio as gr
 import tensorflow as tf
-import numpy as input
+import numpy as np
 
 num_classes = 200
 IMG_HEIGHT = 256
 IMG_WIDTH = 256
 
+with open(output_path, 'r') as file:
+    CLASS_LABEL = [x.strip() for x in file.readlines()]
+
 def normalize_image(img):
     img = tf.cast(img, tf.float32)/255.
     img = tf.image.resize(img, (IMG_HEIGHT, IMG_WIDTH), method='bilinear')
@@ -18,10 +21,15 @@ def predict_fn(img):
     x = np.array(img_data)
     x = np.expand_dims(x, axis=0)
     temp = model.predict(x)
-    return temp
+
+    idx = np.argsort(np.squeeze(temp))[::-1]
+    top3_value = np.asarray([temp[0][i]] for i in idx[0:3])
+    top3_idx = idx[0:3]
+    
+    return {CLASS_LABEL[i]:str(v) for i, v in zip(top3_idx,top3_value)}
 
 
 model = tf.keras.models.load_model("model.h5")
 
-interface = gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label', examples=path,)
+interface = gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label')
 interface.launch()