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from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# Use a compatible CLIP model
model = SentenceTransformer("clip-ViT-B-32")
def search_images_by_text(text, df, embeddings, top_k=30):
text_emb = model.encode([text])
filtered_embeddings = embeddings[df.index]
sims = cosine_similarity(text_emb, filtered_embeddings)[0]
top_indices = np.argsort(sims)[::-1][:top_k]
return df.iloc[top_indices]
def get_similar_images(df, image_id, embeddings, top_k=5):
index = int(image_id) # adjust based on your ID setup
query_emb = embeddings[index]
sims = cosine_similarity([query_emb], embeddings)[0]
top_indices = np.argsort(sims)[::-1][1:top_k+1]
return df.iloc[top_indices]
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