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diff --git a/scripts/app.py b/scripts/app.py
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index 0000000000000000000000000000000000000000..2a4db5fd9f6eb39fdc396e754142853a75da9a03
--- /dev/null
+++ b/scripts/app.py
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+import gradio as gr
+import torch
+from PIL import Image
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from transformers import CLIPProcessor, CLIPModel, AutoProcessor, AutoModel
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load models and processors
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+dino_model = AutoModel.from_pretrained("facebook/dinov2-base").to(device)
+dino_processor = AutoProcessor.from_pretrained("facebook/dinov2-base")
+
+def get_image_embedding(image, model, processor, model_type):
+    if isinstance(image, str):  # Handle file input
+        image = Image.open(image)
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    with torch.no_grad():
+        if model_type == "clip":
+            embedding = model.get_image_features(**inputs)
+        elif model_type == "dinov2":
+            outputs = model(**inputs)
+            embedding = outputs.last_hidden_state.mean(dim=1)  # Global pooling
+    embedding /= embedding.norm(dim=-1, keepdim=True)  # Normalize
+    return embedding
+
+def retrieve_images(query_img, gallery_imgs, model, processor, model_type, top_k=10):
+    query_embedding = get_image_embedding(query_img, model, processor, model_type)
+    
+    gallery_embeddings = []
+    for img in gallery_imgs:
+        emb = get_image_embedding(img, model, processor, model_type)
+        gallery_embeddings.append((emb, img))
+    
+    rank_list = []
+    for emb, img in gallery_embeddings:
+        similarity_score = (query_embedding @ emb.T).item()
+        rank_list.append((similarity_score, img))
+    
+    rank_list = sorted(rank_list, key=lambda x: x[0], reverse=True)[:top_k]
+    return [img for _, img in rank_list]
+
+def display_results(query_img, gallery_imgs, top_k):
+    clip_results = retrieve_images(query_img, gallery_imgs, clip_model, clip_processor, "clip", top_k)
+    dino_results = retrieve_images(query_img, gallery_imgs, dino_model, dino_processor, "dinov2", top_k)
+    return [query_img] + clip_results, [query_img] + dino_results
+
+def gradio_interface(query_img, gallery_imgs, top_k):
+    if not isinstance(gallery_imgs, list):
+        gallery_imgs = [gallery_imgs]
+    gallery_imgs = [img.name if hasattr(img, 'name') else img for img in gallery_imgs]  # Handle NamedString issue
+    clip_res, dino_res = display_results(query_img, gallery_imgs, top_k)
+    return clip_res, dino_res
+
+import copy 
+
+gallery_path = "dataset/gallery"
+filenames = os.listdir(gallery_path)
+
+flag_filenames = [filename for filename in filenames if "flag" in filenames] 
+tattoo_filenames = [filename for filename in filenames if "tattoo" in filename] 
+
+gallery_examples_flags = [os.path.join(gallery_path, filename) for filename in flag_filenames ]
+gallery_examples_tattoos =  [os.path.join(gallery_path, filename) for filename in tattoo_filenames ]
+
+query_examples = ["dataset/query/american_flag46.jpg", "dataset/query/bird.jpg"]
+
+print(gallery_examples_flags)
+print(gallery_examples_tattoos)
+
+demo = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Image(type="pil", label="Query Image"),
+        gr.File(file_types=["image"], label="Gallery Images", file_count="multiple", elem_id="gallery-files"),
+        gr.Slider(1, 30, value=10, step=1, label="Top-K Matches"),
+    ],
+    outputs=[
+        gr.Gallery(label="CLIP Retrieval Results", elem_id="clip-results", rows=[1], columns=[30]),
+        gr.Gallery(label="DINOv2 Retrieval Results", elem_id="dino-results", rows=[1], columns=[30]),
+    ],
+    title="CLIP vs DINOv2 Image Retrieval",
+    description="Upload a query image and gallery images to see the top-k retrieval results side by side using CLIP and DINOv2.",
+    examples=[[query_examples[0], gallery_examples_flags, 10], [query_examples[1], gallery_examples_tattoos, 10]],
+    css="""
+    #gallery-files {
+        max-height: 150px;
+        overflow-y: scroll;
+    }
+    #clip-results, #dino-results {
+        max-height: 150px;
+    }
+    """
+)
+
+demo.launch(share=True)
diff --git a/scripts/image_retrieval.py b/scripts/image_retrieval.py
new file mode 100644
index 0000000000000000000000000000000000000000..483330f42e276f4d0c70cbdc6dfe95045755e71a
--- /dev/null
+++ b/scripts/image_retrieval.py
@@ -0,0 +1,128 @@
+import torch
+from PIL import Image
+import os
+from matplotlib import pyplot as plt 
+import argparse
+from transformers import CLIPProcessor, CLIPModel
+from transformers import AutoProcessor, AutoModel
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def create_gallery(gallery_paths, model, processor):
+    gallery = []
+
+    for path in gallery_paths:
+        img = Image.open(os.path.join(args.gallery_path,path))
+        img_inputs = processor(images=img, return_tensors="pt").to(device)
+        with torch.no_grad():
+            if args.model == "clip":
+                img_embedding = model.get_image_features(**img_inputs)
+            elif args.model == "dinov2":
+                with torch.no_grad():
+                    outputs = model(**img_inputs)
+                img_embedding = outputs.last_hidden_state.mean(dim=1)  
+            img_embedding /= img_embedding.norm(dim=-1, keepdim=True)
+
+        gallery.append([img_embedding, os.path.join(args.gallery_path, path)])
+
+    return gallery
+
+def retrieval(args):
+    if args.model == "clip":
+        model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
+        processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+    elif args.model == "dinov2":
+        # Load DINOv2 model
+        model_name = "facebook/dinov2-base"
+        model = AutoModel.from_pretrained(model_name)
+        processor = AutoProcessor.from_pretrained(model_name)
+
+
+    gallery_paths = os.listdir(args.gallery_path)
+    query_paths = os.listdir(args.query_path)
+
+    print("--- Initalizing gallery ---")
+    gallery = create_gallery(gallery_paths, model, processor)
+
+    for k, query_path in enumerate(query_paths):
+        query_image = Image.open(os.path.join(args.query_path, query_path))
+        img_inputs = processor(images=query_image, return_tensors="pt").to(device)
+        with torch.no_grad():
+            if args.model == "clip":
+                query_embedding = model.get_image_features(**img_inputs)
+            elif args.model == "dinov2":
+                with torch.no_grad():
+                    outputs = model(**img_inputs)
+                query_embedding = outputs.last_hidden_state.mean(dim=1)  
+            query_embedding /= query_embedding.norm(dim=-1, keepdim=True)
+
+
+        fig = plt.figure()
+        plot_length = 11
+        rank_list = []
+
+        gallery_ax = fig.add_subplot(1,plot_length,1) #add query image in the left top place in plot
+        gallery_ax.imshow(query_image)
+
+        print(f"--- Starting image retrieval for query image: {query_path}")
+        logit_scale = 100
+        query_normalized = query_embedding / query_embedding.norm(dim=1, keepdim=True)
+
+        for item in gallery:
+             # normalized features
+            gallery_normalized = item[0] / item[0].norm(dim=1, keepdim=True)
+            # cosine similarity as logits
+            similarity_score = (logit_scale * query_normalized @ gallery_normalized.t()).item()
+            similarity_score = round(similarity_score,3)
+            rank_list.append([similarity_score, item[1]])  # add gallery image with its similarity score to this query image in ranking list 
+    
+        rank_list = sorted(rank_list, key=lambda x: x[0], reverse = True)
+
+    
+        for i in range(2,plot_length): 
+            gallery_ax = fig.add_subplot(1,plot_length,i)
+            img = Image.open(rank_list[i][1])
+            gallery_ax.imshow(img)
+            gallery_ax.set_title('%.1f'% rank_list[i][0], fontsize=8) #add similarity score as title
+            gallery_ax.axis('off')
+        plt.savefig(os.path.join(args.outDir, "plot_"+ str(k)+".jpg"))
+        plt.close()
+
+
+
+if __name__ == "__main__":
+    # Create an argument parser
+    parser = argparse.ArgumentParser(description="CLIP Image Retriever")
+
+    # Add arguments
+    parser.add_argument(
+        '--gallery-path', 
+        type=str, 
+        default="dataset/gallery/",
+        help="Directory containing the gallery images"
+    )
+    parser.add_argument(
+        '--query-path', 
+        type=str, 
+        default="dataset/query/",
+        help="Directory containing the query images"
+    )
+    parser.add_argument(
+        '--outDir', 
+        type=str, 
+        default="outputs/retrieval_clip",
+        help="Directory containing the output plots"
+    )
+    parser.add_argument(
+        '--model', 
+        type=str, 
+        default="clip",
+        help="Model type. i.e clip or dinov2"
+    )
+
+    # Parse the arguments
+    args = parser.parse_args()
+
+    os.makedirs(args.outDir, exist_ok=True)
+    retrieval(args)
\ No newline at end of file