Update app.py

app.py

@@ -11,9 +11,9 @@ def single_image(image,mode):
     elif mode=="hsv":
         return np.array(img.convert('HSV')).flatten()
     else:
-        kmeans = sio.load('kmeans.skops')
+        kmeans = sio.load('Model/kmeans.skops')
         sift = cv2.SIFT_create()
-        img = img.convert("L")
+        img = np.array(img.convert("L"))
         keypoints, descriptors = sift.detectAndCompute(img, None)
         words = kmeans.predict(descriptors)
         hist, _ = np.histogram(words, bins=num_clusters, range=(0, num_clusters))
@@ -21,13 +21,13 @@ def single_image(image,mode):
 def Classify(img, pre,model):
     start_time = time.time() 
     image = Image.open(img)
-    file="Models/"+model+'_'+pre+'.skops'
+    file="Model/"+model+'_'+pre+'.skops'
     loaded_model = sio.load(file)
-    predictions = loaded_model.predict(single_image(image,pre))
+    predictions = loaded_model.predict(single_image(image,pre).reshape(1,-1))[0]
     end_time = time.time()  
-    elapsed_time_microseconds = (end_time - start_time) * 1_000_000_000  
+    elapsed_time_microseconds = (end_time - start_time) * 1_000  
 
-    return  predictions,(end_time - start_time) * 1_000_000_000
+    return  predictions,(end_time - start_time) * 1_000
 
 
 interface = gr.Interface(
@@ -40,13 +40,13 @@ interface = gr.Interface(
             info="Choose one" 
         )  , gr.Radio(
             ["dt", "rf", "gb"],
-            label="ML Model",
+            label="ML Model (Dicision Tree, Random Forest, Gradient Boosting)",
             info="Choose one"  
         ) 
     ],
     outputs=[
             gr.Textbox(label="Class"),
-          gr.Textbox(label="Time")
+          gr.Textbox(label="Time (milliseconds)")
 
     ]
 )