Update app.py

app.py

@@ -9,6 +9,7 @@ from PIL import Image
 import requests
 import torch
 import warnings
+import random
 
 # import torch.nn.functional as F
 import numpy as np
@@ -1139,8 +1140,15 @@ def prepare(raw_idx, text_embeddings, class_order):
 
     choice_values = list(zip(choice_names, choice_values))
 
-    warnings.warn(f"prepare correct_choice: {correct_choice}")
-    warnings.warn(f"prepare choice_values: {choice_values}")
+    correct_tuple = tuple(correct_choice)
+    other_choices = [choice for choice in choice_values if choice != correct_tuple]
+    keep = random.choice(other_choices)
+    new_choice_values = [correct_tuple, keep]
+    random.shuffle(new_choice_values)
+    
+    warnings.warn(f"correct_choice: {correct_choice}")
+    warnings.warn(f"choice_values: {choice_values}")
+    warnings.warn(f"correct_choice: {new_choice_values}")
 
     next_radio = gr.Radio(
         choices=choice_values,
@@ -1158,57 +1166,57 @@ def prepare(raw_idx, text_embeddings, class_order):
     return next_radio, next_image, raw_idx, correct_choice, model_choice, choice_values
 
 
-def reroll(raw_idx, text_embeddings, class_order):
-    #  prepared next question, loads image, and computes choices
+# def reroll(raw_idx, text_embeddings, class_order):
+#     #  prepared next question, loads image, and computes choices
 
-    idx = class_order[raw_idx]
-    lang_class_idxs = babel_imagenet["EN"][0]
-    class_idx = lang_class_idxs[idx]
+#     idx = class_order[raw_idx]
+#     lang_class_idxs = babel_imagenet["EN"][0]
+#     class_idx = lang_class_idxs[idx]
 
-    img_idx = 0
-    img_idx = np.random.choice(min(len(babelnet_images[class_idx]), max_image_choices))
-    img_url = babelnet_images[class_idx][img_idx]["url"]
-    class_labels = openai_en_classes
+#     img_idx = 0
+#     img_idx = np.random.choice(min(len(babelnet_images[class_idx]), max_image_choices))
+#     img_url = babelnet_images[class_idx][img_idx]["url"]
+#     class_labels = openai_en_classes
 
-    choices = list(
-        reversed(precomputed_results["EN"][idx][img_idx])
-    )  # precomputing script uses torch.topk which sorts in reverse here
-    if idx not in choices:
-        choices = [idx] + choices[1:]
-    model_choice_idx = choices[-1]
+#     choices = list(
+#         reversed(precomputed_results["EN"][idx][img_idx])
+#     )  # precomputing script uses torch.topk which sorts in reverse here
+#     if idx not in choices:
+#         choices = [idx] + choices[1:]
+#     model_choice_idx = choices[-1]
 
-    numpy.random.shuffle(choices)
+#     numpy.random.shuffle(choices)
 
-    choice_names = [class_labels[idx] for idx in choices]
-    choice_values = [0, 1, 2, 3]
+#     choice_names = [class_labels[idx] for idx in choices]
+#     choice_values = [0, 1, 2, 3]
 
-    model_choice_idx = choices.index(model_choice_idx)
-    model_choice = [choice_names[model_choice_idx], choice_values[model_choice_idx]]
-    correct_choice_idx = choices.index(idx)
-    correct_choice = [
-        choice_names[correct_choice_idx],
-        choice_values[correct_choice_idx],
-    ]
+#     model_choice_idx = choices.index(model_choice_idx)
+#     model_choice = [choice_names[model_choice_idx], choice_values[model_choice_idx]]
+#     correct_choice_idx = choices.index(idx)
+#     correct_choice = [
+#         choice_names[correct_choice_idx],
+#         choice_values[correct_choice_idx],
+#     ]
 
-    choice_values = list(zip(choice_names, choice_values))
+#     choice_values = list(zip(choice_names, choice_values))
 
-    warnings.warn(f"reroll correct_choice: {correct_choice}")
-    warnings.warn(f"reroll choice_values: {choice_values}")
+#     warnings.warn(f"reroll correct_choice: {correct_choice}")
+#     warnings.warn(f"reroll choice_values: {choice_values}")
 
-    next_radio = gr.Radio(
-        choices=choice_values,
-        interactive=True,
-        label="Select the correct answer:",
-        value=None,
-    )
-    next_image = gr.Image(
-        value=img_url,
-        width=IMG_WIDTH,
-        height=IMG_WIDTH,
-        label="What class does this image belong to?",
-    )
+#     next_radio = gr.Radio(
+#         choices=choice_values,
+#         interactive=True,
+#         label="Select the correct answer:",
+#         value=None,
+#     )
+#     next_image = gr.Image(
+#         value=img_url,
+#         width=IMG_WIDTH,
+#         height=IMG_WIDTH,
+#         label="What class does this image belong to?",
+#     )
 
-    return next_radio, next_image, raw_idx, correct_choice, model_choice, choice_values
+#     return next_radio, next_image, raw_idx, correct_choice, model_choice, choice_values
 
 
 with gr.Blocks(title="Babel-ImageNet Quiz", css=css) as demo:
@@ -1254,7 +1262,7 @@ with gr.Blocks(title="Babel-ImageNet Quiz", css=css) as demo:
             correct_text = gr.Text("Please click start to begin.")
             player_score_text = gr.Text(f"Player score: 0")
             clip_score_text = gr.Text(f"mSigLIP score: 0")
-            reroll_btn = gr.Button(value="Reroll the image (for bad images or errors)")
+            # reroll_btn = gr.Button(value="Reroll the image (for bad images or errors)")
 
     options.select(
         fn=select,
@@ -1307,14 +1315,14 @@ with gr.Blocks(title="Babel-ImageNet Quiz", css=css) as demo:
         outputs=[options, image, class_idx, correct_choice, model_choice, choices],
     )
 
-    reroll_btn.click(
-        fn=reroll,
-        inputs=[
-            class_idx,
-            text_embeddings,
-            class_order,
-        ],
-        outputs=[options, image, class_idx, correct_choice, model_choice, choices],
-    )
+    # reroll_btn.click(
+    #     fn=reroll,
+    #     inputs=[
+    #         class_idx,
+    #         text_embeddings,
+    #         class_order,
+    #     ],
+    #     outputs=[options, image, class_idx, correct_choice, model_choice, choices],
+    # )
 
 demo.launch()