Update MMSearch-Plus dataset with encrypted text fields (images unchanged)

mmsearch_plus.py

@@ -7,8 +7,9 @@ Images are NOT encrypted and remain as PIL Image objects for accessibility.
 import base64
 import hashlib
 import os
-from typing import Dict, Any, List
+from typing import Dict, Any
 import datasets
+from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Features, Value, Sequence, Image, Split
 
 _CITATION = """\
 @article{tao2025mmsearch,
@@ -29,13 +30,6 @@ _HOMEPAGE = "https://mmsearch-plus.github.io/"
 
 _LICENSE = "CC BY-NC 4.0"
 
-_URLS = {
-    "train": [
-        "data-00000-of-00002.arrow",
-        "data-00001-of-00002.arrow"
-    ]
-}
-
 def derive_key(password: str, length: int) -> bytes:
     """Derive encryption key from password using SHA-256."""
     hasher = hashlib.sha256()
@@ -56,82 +50,76 @@ def decrypt_text(ciphertext_b64: str, password: str) -> str:
     except Exception:
         return ciphertext_b64
 
-class MmsearchPlus(datasets.GeneratorBasedBuilder):
-    """MMSearch-Plus dataset with transparent decryption."""
+def decrypt_example(example: Dict[str, Any], canary: str) -> Dict[str, Any]:
+    """Decrypt text fields in a single example."""
+    # Decrypt text fields - matches encryption script fields
+    text_fields = ['question', 'video_url', 'arxiv_id']
+    
+    for field in text_fields:
+        if field in example and example[field]:
+            example[field] = decrypt_text(example[field], canary)
+    
+    # Handle answer field (list of strings)
+    if 'answer' in example and example['answer']:
+        decrypted_answers = []
+        for answer in example['answer']:
+            if answer:
+                decrypted_answers.append(decrypt_text(answer, canary))
+            else:
+                decrypted_answers.append(answer)
+        example['answer'] = decrypted_answers
+    
+    # Images are NOT encrypted - they remain as PIL Image objects
+    return example
+
+class MmsearchPlus(GeneratorBasedBuilder):
+    """MMSearch-Plus dataset builder with transparent decryption."""
 
     VERSION = datasets.Version("1.0.0")
 
     def _info(self):
-        # Define features to handle the complete dataset schema
-        features = datasets.Features({
-            "question": datasets.Value("string"),
-            "answer": datasets.Sequence(datasets.Value("string")),
-            "num_images": datasets.Value("int64"),
-            "arxiv_id": datasets.Value("string"),
-            "video_url": datasets.Value("string"),
-            "category": datasets.Value("string"),
-            "difficulty": datasets.Value("string"),
-            "subtask": datasets.Value("string"),
-            # Image fields (not encrypted, kept as PIL Images)
-            "img_1": datasets.Image(),
-            "img_2": datasets.Image(),
-            "img_3": datasets.Image(),
-            "img_4": datasets.Image(),
-            "img_5": datasets.Image(),
-            # Additional fields that might exist in the dataset
-            "choices": datasets.Sequence(datasets.Value("string")),
-            "question_zh": datasets.Value("string"),
-            "answer_zh": datasets.Sequence(datasets.Value("string")),
-            "regex": datasets.Value("string"),
-            "text_criteria": datasets.Value("string"),
-            "original_filename": datasets.Value("string"),
-            "screenshots_dir": datasets.Value("string"),
-            "time_points": datasets.Sequence(datasets.Value("string")),
-            "search_query": datasets.Value("string"),
-            "question_type": datasets.Value("string"),
-            "requires_image_understanding": datasets.Value("bool"),
-            "source": datasets.Value("string"),
-            "content_keywords": datasets.Value("string"),
-            "reasoning": datasets.Value("string"),
-            "processed_at": datasets.Value("string"),
-            "model_used": datasets.Value("string"),
-            "entry_index": datasets.Value("int64"),
-            "original_image_paths": datasets.Sequence(datasets.Value("string")),
-            "masked_image_paths": datasets.Sequence(datasets.Value("string")),
-            "is_valid": datasets.Value("bool"),
-        })
-
-        return datasets.DatasetInfo(
+        return DatasetInfo(
             description=_DESCRIPTION,
-            features=features,
+            features=Features({
+                "question": Value("string"),
+                "answer": Sequence(Value("string")),
+                "num_images": Value("int64"),
+                "arxiv_id": Value("string"),
+                "video_url": Value("string"),
+                "category": Value("string"),
+                "difficulty": Value("string"),
+                "subtask": Value("string"),
+                "img_1": Image(),
+                "img_2": Image(),
+                "img_3": Image(),
+                "img_4": Image(),
+                "img_5": Image(),
+            }),
             homepage=_HOMEPAGE,
             license=_LICENSE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        # Get canary from environment variable or kwargs
+        # Get canary from environment variable
         canary = os.environ.get("MMSEARCH_PLUS")
-
-        # Check if passed in the builder's initialization
-        if hasattr(self, 'canary'):
-            canary = self.canary
-
+        
         if not canary:
             raise ValueError(
-                "Canary string is required for decryption. Either set the MMSEARCH_PLUS "
-                "environment variable or pass it via the dataset loading kwargs. "
-                "Example: load_dataset('path/to/dataset', trust_remote_code=True) after setting "
-                "os.environ['MMSEARCH_PLUS'] = 'your_canary_string'"
+                "\n\n⚠️  Canary string is required for decryption!\n\n"
+                "Please set the environment variable before loading:\n"
+                "    import os\n"
+                "    os.environ['MMSEARCH_PLUS'] = 'your_canary_string'\n\n"
+                "Hint: The canary is the name of this dataset repository (without the username).\n"
             )
-
-        # Download files
-        urls = _URLS["train"]
+        
+        # Download arrow files
+        urls = ["data-00000-of-00002.arrow", "data-00001-of-00002.arrow"]
         downloaded_files = dl_manager.download(urls)
-
+        
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
+            SplitGenerator(
+                name=Split.TRAIN,
                 gen_kwargs={
                     "filepaths": downloaded_files,
                     "canary": canary,
@@ -141,34 +129,21 @@ class MmsearchPlus(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, filepaths, canary):
         """Generate examples with transparent decryption."""
+        import sys
+        print(f"🔓 [MMSearch-Plus] Decrypting dataset with canary: {canary[:10]}...", file=sys.stderr)
+        
         key = 0
-
         for filepath in filepaths:
-            # Load the arrow file
+            # Load the arrow file directly
             arrow_dataset = datasets.Dataset.from_file(filepath)
-
+            
             for idx in range(len(arrow_dataset)):
                 example = arrow_dataset[idx]
-
-                # Decrypt text fields - matches encryption script fields
-                text_fields = ['question', 'video_url', 'arxiv_id']
-
-                for field in text_fields:
-                    if example.get(field):
-                        example[field] = decrypt_text(example[field], canary)
-
-                # Handle answer field (list of strings)
-                if example.get("answer"):
-                    decrypted_answers = []
-                    for answer in example["answer"]:
-                        if answer:
-                            decrypted_answers.append(decrypt_text(answer, canary))
-                        else:
-                            decrypted_answers.append(answer)
-                    example["answer"] = decrypted_answers
-
-                # Images are not encrypted - they remain as PIL Image objects
-                # No image decryption needed as per the encryption script
-
+                
+                # Apply decryption
+                example = decrypt_example(example, canary)
+                
                 yield key, example
-                key += 1
+                key += 1
+        
+        print(f"✅ [MMSearch-Plus] Decrypted {key} samples successfully!", file=sys.stderr)