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from pymongo import MongoClient
import numpy as np
import config
client = MongoClient(config.MongoURI)
db = client[config.MONGO_DB]
collection = db[config.MONGO_COLLECTION]
# Custom Cosine Similarity (Sklearn uses high memory at a time of hosting)
def cosine_similarity(query_embedding, embeddings):
query = np.array(query_embedding)
embeddings = np.array(embeddings)
query_norm = np.linalg.norm(query)
embeddings_norm = np.linalg.norm(embeddings, axis=1)
dot_products = embeddings @ query
similarities = dot_products / (embeddings_norm * query_norm)
return similarities.tolist()
async def get_similar_products(query_embedding):
# Fetch relevant fields from all documents
products = list(collection.find({}, {
"_id": 1,
"embedding": 1,
"name": 1,
"category": 1,
"image_url": 1,
"tags": 1,
"target_audience": 1,
"brand": 1
}))
# Prepare data
ids = [str(p["_id"]) for p in products]
embeddings = np.array([p["embedding"] for p in products])
# Cosine similarity
similarities = cosine_similarity(query_embedding, embeddings)
results = []
brand_set = set()
for i, score in enumerate(similarities):
score_float = round(float(score), 2)
# Add product with full details
results.append({
"id": ids[i],
"similarity": score_float,
"name": products[i].get("name", "Unknown"),
"category": products[i].get("category", "Unknown"),
"image_url": products[i].get("image_url", ""),
"tags": products[i].get("tags", []),
"target_audience": products[i].get("target_audience", "unisex"),
"brand": products[i].get("brand", "unknown")
})
# Collect tags for frontend filtering if similarity >= 0.5
if score_float >= 0.7:
brand = products[i].get("brand", "")
if isinstance(brand, str):
brand = brand.strip().lower()
if brand and brand != "unknown":
brand_set.add(brand)
# Sort products by similarity descending
results.sort(key=lambda x: x["similarity"], reverse=True)
return {
"similar_products": results,
"filter_brand": list(brand_set)
}
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