Add requirements

app.py

@@ -19,7 +19,7 @@ model = SentenceTransformer('clip-ViT-B-32')
 #Open the precomputed embeddings
 #emb_filename = 'unsplash-25k-photos-embeddings.pkl'
 ds_with_embeddings = load_dataset("kvriza8/image-embeddings", use_auth_token=True)
-ds_with_embeddings['train'].add_faiss_index(column='embeddings')
+
 # img_names, img_emb = ds_with_embeddings['train']['image'], ds_with_embeddings['train']['embeddings']
 # with open(emb_filename, 'rb') as fIn:
 #         img_names, img_emb = pickle.load(fIn)
@@ -56,6 +56,7 @@ def search_text(query, top_k=1):
 
 def get_image_from_text(text_prompt, number_to_retrieve=6):
     prompt = model.encode(text_prompt)
+    ds_with_embeddings['train'].add_faiss_index(column='embeddings')
     scores, retrieved_examples = ds_with_embeddings['train'].get_nearest_examples('embeddings', prompt,k=number_to_retrieve)
     return retrieved_examples
     # plt.figure(figsize=(15, 15))