diff --git a/codegrab/vqa3.py b/codegrab/vqa3.py
new file mode 100644
index 0000000..25d38ba
--- /dev/null
+++ b/codegrab/vqa3.py
@@ -0,0 +1,84 @@
+import os
+import argparse
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+from collections import defaultdict
+
+
+def classify_images(model_name, image_paths, class_names):
+    # Load CLIP model and processor
+    model = CLIPModel.from_pretrained(model_name)
+    processor = CLIPProcessor.from_pretrained(model_name)
+
+    classification_results = defaultdict(list)
+
+    # Perform zero-shot classification on each image
+    for image_path in image_paths:
+        try:
+            image = Image.open(image_path)
+
+            # Process the input image and text labels
+            inputs = processor(
+                text=class_names,
+                images=image,
+                return_tensors="pt",
+                padding=True
+            )
+
+            # Run the model and get logits
+            outputs = model(**inputs)
+            logits_per_image = outputs.logits_per_image
+
+            # Calculate probabilities
+            probs = logits_per_image.softmax(dim=1)
+
+            # Get the predicted label
+            pred_label = class_names[probs.argmax(dim=1).item()]
+
+            classification_results[pred_label].append(image_path)
+
+        except Exception as e:
+            print(f"Skipping {image_path} due to error: {e}")
+
+    for label, images in classification_results.items():
+        print(f"{label}:")
+        for image_path in images:
+            print(f"  {image_path}")
+
+
+def main():
+    available_models = [
+        "openai/clip-vit-large-patch14",
+        "openai/clip-vit-base-patch32",
+        "openai/clip-vit-base-patch16"
+    ]
+
+    parser = argparse.ArgumentParser(description="CLIP-based Image Classifier")
+    parser.add_argument("--model", type=str, default="openai/clip-vit-base-patch16",
+                        help="Model name to use for classification (default: openai/clip-vit-base-patch16)")
+    parser.add_argument("-c", "--category", action="append",default=["image is safe for work", "image is not safe for work"],help="Add a classification category (e.g., 'man', 'woman', 'child', 'animal'). If not specified, the default categories will be 'safe for work' and 'not safe for work'.")
+    parser.add_argument("paths", metavar="path", type=str, nargs="+",
+                        help="List of image file paths or directories")
+
+    args = parser.parse_args()
+
+    if args.model.lower() == "list":
+        print("Available models:")
+        for model in available_models:
+            print(f"  {model}")
+        return
+
+    image_paths = []
+    for path in args.paths:
+        if os.path.isdir(path):
+            image_paths.extend([os.path.join(path, file) for file in os.listdir(path)])
+        elif os.path.isfile(path):
+            image_paths.append(path)
+        else:
+            print(f"Skipping {path}, not a valid file or directory")
+
+    classify_images(args.model, image_paths, args.category)
+
+
+if __name__ == "__main__":
+    main()