Update README.md

README.md

@@ -33,17 +33,13 @@ language:
 size_categories:
 - n<1K
 ---
-
 # IllusionVQA: Optical Illusion Dataset
 
 Paper Link: <br>
-GitHub Link: <br>
-
+Github Link: <be>
 ## TL;DR
 IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localization tasks. GPT4V achieved 62.99% accuracy on comprehension and 49.7% on localization, while humans achieved 91.03% and 100% respectively.
 
-
-
 ## Usage
 ```python
 from datasets import load_dataset
@@ -62,61 +58,34 @@ def construct_mcq(options, correct_option):
     correct_option_letter = None
     i = "a"
     mcq = ""
-
     for option in options:
         if option == correct_option:
             correct_option_letter = i
         mcq += f"{i}. {option}\n"
         i = chr(ord(i) + 1)
-
     mcq = mcq[:-1]
     return mcq, correct_option_letter
 
 def add_row(content, data, i, with_answer=False):  
-
     mcq, correct_option_letter = construct_mcq(data["options"], data["answer"])
-
-    content.append({
-            "type": "text",
-            "text": "Image "+str(i)+": "+data["question"]+"\n"+mcq
-        })
-    
-    content.append(
-        {
-            "type": "image_url",
-            "image_url": {
-                "url": f"data:image/jpeg;base64,{encode_image(data["image"])}",
-                "detail": "low"
-            }
-        }
-    )
+    content.append({ "type": "text",
+            "text": "Image "+str(i)+": "+data["question"]+"\n"+mcq })
+    content.append({ "type": "image_url",
+            "image_url": {"url": f"data:image/jpeg;base64,{encode_image(data["image"])}",
+                "detail": "low"}})
     if with_answer:
-        content.append(
-            {
-                "type": "text",
-                "text": "Answer {}: ".format(i)+correct_option_letter
-            }
-        )
+        content.append({"type": "text", "text": "Answer {}: ".format(i)+correct_option_letter})
     else:
-        content.append(
-            {
-                "type": "text",
-                "text": "Answer {}: ".format(i),
-            }
-        )
-    
+        content.append({"type": "text", "text": "Answer {}: ".format(i), })
     return content
 
-dataset = load_dataset("csebuetnlp/illusionVQA-Comprehension")
+dataset = load_dataset("csebuetnlp/illusionVQA-Soft-Localization")
 client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
-
-content = [
-    {
+content = [{
         "type": "text",
         "text": "You'll be given an image, an instruction and some choices. You have to select the correct one. Do not explain your reasoning. Answer with the option's letter from the given choices directly. Here are a few examples:",
-    }
-]
+    }]
 
 ### Add the few examples
 i = 1
@@ -124,11 +93,7 @@ for data in dataset["train"]:
     content = add_row(content, data, i, with_answer=True)
     i += 1
 
-content.append({
-                    "type": "text",
-                    "text": "Now you try it!",
-                })
-
+content.append({"type": "text","text": "Now you try it!",})
 next_idx = i
 
 ### Add the test data
@@ -138,12 +103,7 @@ content_t = add_row(content.copy(), test_data, next_idx, with_answer=False)
 ### Get the answer from GPT-4
 response = client.chat.completions.create(
     model="gpt-4-vision-preview",
-    messages=[
-        {
-            "role": "user",
-            "content": content_t,
-        }
-    ],
+    messages=[{"role": "user","content": content_t,}],
     max_tokens=5,
 )
 gpt4_answer = response.choices[0].message.content
@@ -158,4 +118,4 @@ The dataset creator makes no representations or warranties regarding the copyrig
 You agree to the terms and conditions specified in this license by downloading or using this dataset. If you do not agree with these terms, do not download or use the dataset.
 
 
-### Citation
+### Citation