Update app.py

app.py

@@ -1,5 +1,8 @@
 import gradio as gr
 import numpy as np
+from sklearn.metrics.pairwise import euclidean_distances
+import cv2
+
 
 
 encoded_images = np.load("X_encoded_compressed.npy")
@@ -31,8 +34,14 @@ def get_image(index):
     return dataset["test"][index-split]
 
 def process_image(image):
-    pass
+    img = np.array(image)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  
+    img = cv2.resize(img, (64, 64))  
+    img = img.astype('float32')  
+    img /= 255.0  
 
+    return img
+    
 def inference(image):
 
     input_image = process_image(image)
@@ -50,24 +59,11 @@ def inference(image):
     for i in top4:
       im = get_image(i)
       print(im["label"], im["timestamp"])
+
+    result_image = get_image(top4[0])
+    result = result_image['label'] + result_image['timestamp']
     
-    n=2
-    plt.figure(figsize=(8, 8))
-    for i, (image1, image2) in enumerate(zip(top4[:2], top4[2:])):
-        ax = plt.subplot(2, n, i + 1)
-        image1 = get_image(image1)["image"]
-        image2 = get_image(image2)["image"]
-    
-        plt.imshow(image1)
-        plt.gray()
-        ax.get_xaxis().set_visible(False)
-        ax.get_yaxis().set_visible(False)
-    
-        ax = plt.subplot(2, n, i + 1 + n)
-        plt.imshow(image2)
-        plt.gray()
-        ax.get_xaxis().set_visible(False)
-        ax.get_yaxis().set_visible(False)
+    return result