diff --git a/relevance-scoring/app.py b/relevance-scoring/app.py
new file mode 100644
index 00000000..db2922e8
--- /dev/null
+++ b/relevance-scoring/app.py
@@ -0,0 +1,49 @@
+from metadata.image_processor import generate_image_description
+from metadata.link_processor import extract_text_from_url
+from embeddings.embedding_generator import generate_embedding, calculate_similarity
+from scoring.relevance_scorer import calculate_relevance_score
+from transformers import pipeline
+import numpy as np
+
+def process_comment(comment, image_urls=None, links=None):
+    text_embedding = generate_embedding(comment)
+
+    # Initialize the caption generator
+    caption_generator = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+    # Process images
+    image_scores = []
+    if image_urls:
+        for url in image_urls:
+            metadata = generate_image_description(url, caption_generator)
+            if metadata and metadata['description']:
+                image_emb = generate_embedding(metadata['description'])
+                image_scores.append(calculate_similarity(text_embedding, image_emb))
+
+    # Process links
+    link_scores = []
+    if links:
+        for link in links:
+            metadata = extract_text_from_url(link)
+            if metadata and metadata['content']:
+                link_emb = generate_embedding(metadata['content'])
+                link_scores.append(calculate_similarity(text_embedding, link_emb))
+
+    # Combine scores
+    image_embedding = max(image_scores, default=None)
+    link_embedding = max(link_scores, default=None)
+    
+    final_score = calculate_relevance_score(
+        text_embedding,
+        image_embedding=image_embedding if image_embedding is not None else np.zeros_like(text_embedding),
+        link_embedding=link_embedding if link_embedding is not None else np.zeros_like(text_embedding)
+    )
+    return final_score
+
+if __name__ == "__main__":
+    comment = "This comment addresses layout issues in the UI."
+    image_urls = ["https://www.bing.com/th?id=OADD2.7490516793165_1K4Y6UMUPT5JEHB4D8&pid=21.2&c=16&roil=0&roit=0.033&roir=1&roib=0.8186&w=300&h=157&dynsize=1&qlt=90"]
+    links = ["https://example.com/sample-page"]
+
+    score = process_comment(comment, image_urls, links)
+    print(f"Final Relevance Score: {score}")
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diff --git a/relevance-scoring/metadata/image_processor.py b/relevance-scoring/metadata/image_processor.py
index f48e97a5..aec74d60 100644
--- a/relevance-scoring/metadata/image_processor.py
+++ b/relevance-scoring/metadata/image_processor.py
@@ -48,6 +48,7 @@ def generate_image_description(image_url, caption_generator):
     
     # Sample image URL
     image_url = "https://th.bing.com/th?id=ORMS.99706f16f78dd7e84c31c95eef897656&pid=Wdp&w=268&h=140&qlt=90&c=1&rs=1&dpr=1.5&p=0"
+    
     # Generate and print metadata
     metadata = generate_image_description(image_url, caption_generator)
     print(metadata)
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diff --git a/relevance-scoring/scoring/relevance_scorer.py b/relevance-scoring/scoring/relevance_scorer.py
new file mode 100644
index 00000000..98430bee
--- /dev/null
+++ b/relevance-scoring/scoring/relevance_scorer.py
@@ -0,0 +1,32 @@
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+def calculate_similarity(embedding1, embedding2):
+    embedding1 = np.array(embedding1).reshape(1, -1)
+    embedding2 = np.array(embedding2).reshape(1, -1)
+    logging.info(f"Embedding1 shape: {embedding1.shape}")
+    logging.info(f"Embedding2 shape: {embedding2.shape}")
+    return cosine_similarity(embedding1, embedding2)[0][0]
+
+def calculate_relevance_score(text_embedding, image_embedding=None, link_embedding=None):
+    score = 0
+    if image_embedding is not None:
+        score += calculate_similarity(text_embedding, image_embedding) * 0.5  # Weight: 50%
+    if link_embedding is not None:
+        score += calculate_similarity(text_embedding, link_embedding) * 0.5  # Weight: 50%
+    return score
+
+if __name__ == "__main__":
+    # Example usage
+    def generate_embedding(text):
+        # Placeholder function for generating embeddings
+        # Replace this with your actual embedding generation logic
+        return np.random.rand(768)
+
+    text_emb = generate_embedding("a UI with layout issues")
+    img_emb = generate_embedding("a UI with red lines showing a spacing issue")
+    relevance_score = calculate_relevance_score(text_emb, image_embedding=img_emb)
+    print(f"Relevance Score: {relevance_score:.2f}")
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