trying total score

app.py

@@ -31,18 +31,19 @@ with open('image_labels.csv', 'r') as csv_file:
 images= list(imagedict.keys())
 labels = list(set(imagedict.values()))
 
-def model_classify(im):
-    inputs = feature_extractor(images=im, return_tensors="pt")
-    outputs = model(**inputs)
-    logits = outputs.logits
-    predicted_class_idx = logits.argmax(-1).item()
-    return model.config.id2label[predicted_class_idx]
-
+def model_classify(radio, im):
+    if radio is not None:
+        inputs = feature_extractor(images=im, return_tensors="pt")
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        return model.config.id2label[predicted_class_idx]
 
 def random_image():
     imname = random.choice(images)
     im = Image.open('images/'+ imname +'.jpg')
     label = str(imagedict[imname])
+    print(label)
     labels.remove(label)
     options = sample(labels,3)
     options.append(label)
@@ -50,14 +51,16 @@ def random_image():
     options = [classes[int(i)] for i in options]
     return im, label, gr.Radio.update(value=None, choices=options), None
 
-def check_score(pred, truth, current_score):
+def check_score(pred, truth, current_score, total_score):
     if pred == classes[int(truth)]:
-        return current_score + 1, f"Your score is {current_score+1}"
-    return current_score, f"Your score is {current_score}"
+        total_score +=1
+        return current_score + 1, f"Your score is {current_score+1} out of {total_score}"
+    else:
+        total_score +=1
+        return current_score, f"Your score is {current_score} out of {total_score}"
+
 
 def compare_score(userclass, prediction):
-    print(userclass)
-    print(prediction)
     if userclass == str(prediction).split(',')[0]:
         return "Great! You and the model agree on the category"
     return "You and the model disagree"
@@ -67,10 +70,11 @@ with gr.Blocks() as demo:
     model_score = gr.State(0)
     image_label = gr.State()
     prediction = gr.State()
+    total_score = gr.State(0)
 
     with gr.Row():
         with gr.Column(min_width= 900):
-            image = gr.Image(shape=(224, 224))
+            image = gr.Image(shape=(600, 600))
             radio = gr.Radio(["option1", "option2", "option3"], label="Pick a category", interactive=True)
         with gr.Column():
             prediction = gr.Label(label="Model Prediction")
@@ -80,8 +84,8 @@ with gr.Blocks() as demo:
     btn = gr.Button("Next image")
 
     demo.load(random_image, None, [image, image_label, radio, prediction])
-    radio.change(model_classify, image, prediction)
-    radio.change(check_score, [radio, image_label, user_score], [user_score, score])
+    radio.change(model_classify, [radio, image], prediction)
+    radio.change(check_score, [radio, image_label, user_score, total_score], [user_score, score])
     #radio.change(compare_score, [radio, prediction], message)
     btn.click(random_image, None, [image, image_label, radio, prediction])