Update app.py

app.py

@@ -8,53 +8,7 @@ model_path = "microsoft/git-base-vqav2"
 dataset_name = "Multimodal-Fatima/OK-VQA_train"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-questions = ["What can happen the objects shown are thrown on the ground?",
-             "What was the machine beside the bowl used for?",
-             "What kind of cars are in the photo?",
-             "What is the hairstyle of the blond called?",
-             "How old do you have to be in canada to do this?",
-             "Can you guess the place where the man is playing?",
-             "What loony tune character is in this photo?",
-             "Whose birthday is being celebrated?",
-             "Where can that toilet seat be bought?",
-             "What do you call the kind of pants that the man on the right is wearing?"]
-
-processor = AutoProcessor.from_pretrained(model_path)
-model = AutoModelForVisualQuestionAnswering.from_pretrained(model_path)
-
-
-def main(select_exemple_num):
-    selectednum = select_exemple_num
-    exemple_img = f"image{selectednum}.jpg"
-    img = Image.open(exemple_img)
-    question = questions[selectednum - 1]
-
-    encoding = processor(img, question, return_tensors='pt')
-
-    outputs = model(**encoding)
-    logits = outputs.logits
-
-    # ---
-    output_str = 'pridicted : \n'
-    predicted_classes = torch.sigmoid(logits)
-
-    probs, classes = torch.topk(predicted_classes, 5)
-    ans = ''
-
-    for prob, class_idx in zip(probs.squeeze().tolist(), classes.squeeze().tolist()):
-        print(prob, model.config.id2label[class_idx])
-        output_str += str(prob)
-        output_str += " "
-        output_str += model.config.id2label[class_idx]
-        output_str += "\n"
-        if not ans:
-            ans = model.config.id2label[class_idx]
-
-    print(ans)
-    # ---
-    output_str += f"\nso I think it's answer is : \n{ans}"
-
-    return exemple_img, question, output_str
+def main():
 
 
 demo = gr.Interface(