update paths

app.py

@@ -4,40 +4,50 @@ import numpy as np
 import h5py
 import faiss
 from PIL import Image
-import io 
+import io
 import pickle
 import random
 
+
 def getRandID():
     indx = random.randrange(0, 396503)
     return indx_to_id_dict[indx], indx
 
+
 def chooseImageIndex(indexType):
-    if (indexType == "FlatIP(default)"):
+    if indexType == "FlatIP(default)":
         return image_index_IP
-    elif (indexType == "FlatL2"):
+    elif indexType == "FlatL2":
+        raise NotImplementedError
         return image_index_L2
-    elif (indexType == "HNSWFlat"):
+    elif indexType == "HNSWFlat":
+        raise NotImplementedError
         return image_index_HNSW
-    elif (indexType == "IVFFlat"):
+    elif indexType == "IVFFlat":
+        raise NotImplementedError
         return image_index_IVF
-    elif (indexType == "LSH"):
+    elif indexType == "LSH":
+        raise NotImplementedError
         return image_index_LSH
 
+
 def chooseDNAIndex(indexType):
-    if (indexType == "FlatIP(default)"):
+    if indexType == "FlatIP(default)":
         return dna_index_IP
-    elif (indexType == "FlatL2"):
+    elif indexType == "FlatL2":
+        raise NotImplementedError
         return dna_index_L2
-    elif (indexType == "HNSWFlat"):
+    elif indexType == "HNSWFlat":
+        raise NotImplementedError
         return dna_index_HNSW
-    elif (indexType == "IVFFlat"):
+    elif indexType == "IVFFlat":
+        raise NotImplementedError
         return dna_index_IVF
-    elif (indexType == "LSH"):
+    elif indexType == "LSH":
+        raise NotImplementedError
         return dna_index_LSH
 
 
-
 def searchEmbeddings(id, mod1, mod2, indexType):
     # variable and index initialization
     dim = 768
@@ -47,16 +57,15 @@ def searchEmbeddings(id, mod1, mod2, indexType):
     index = faiss.IndexFlatIP(dim)
 
     # get index
-    if (mod2 == "Image"):
+    if mod2 == "Image":
         index = chooseImageIndex(indexType)
-    elif (mod2 == "DNA"):
+    elif mod2 == "DNA":
         index = chooseDNAIndex(indexType)
-    
 
     # search for query
-    if (mod1 == "Image"):
+    if mod1 == "Image":
         query = id_to_image_emb_dict[id]
-    elif (mod1 == "DNA"):
+    elif mod1 == "DNA":
         query = id_to_dna_emb_dict[id]
     query = query.astype(np.float32)
     D, I = index.search(query, num_neighbors)
@@ -66,25 +75,26 @@ def searchEmbeddings(id, mod1, mod2, indexType):
     for indx in I[0]:
         id = indx_to_id_dict[indx]
         id_list.append(id)
-        
+
     return id_list
 
+
 with gr.Blocks() as demo:
 
     # for hf: change all file paths, indx_to_id_dict as well
 
     # load indexes
-    image_index_IP = faiss.read_index("big_image_index_FlatIP.index")
-    image_index_L2 = faiss.read_index("big_image_index_FlatL2.index")
-    image_index_HNSW = faiss.read_index("big_image_index_HNSWFlat.index")
-    image_index_IVF = faiss.read_index("big_image_index_IVFFlat.index")
-    image_index_LSH = faiss.read_index("big_image_index_LSH.index")
-
-    dna_index_IP = faiss.read_index("big_dna_index_FlatIP.index")
-    dna_index_L2 = faiss.read_index("big_dna_index_FlatL2.index")
-    dna_index_HNSW = faiss.read_index("big_dna_index_HNSWFlat.index")
-    dna_index_IVF = faiss.read_index("big_dna_index_IVFFlat.index")
-    dna_index_LSH = faiss.read_index("big_dna_index_LSH.index")
+    image_index_IP = faiss.read_index("bioscan_5m_image_IndexFlatIP.index")
+    # image_index_L2 = faiss.read_index("big_image_index_FlatL2.index")
+    # image_index_HNSW = faiss.read_index("big_image_index_HNSWFlat.index")
+    # image_index_IVF = faiss.read_index("big_image_index_IVFFlat.index")
+    # image_index_LSH = faiss.read_index("big_image_index_LSH.index")
+
+    dna_index_IP = faiss.read_index("bioscan_5m_dna_IndexFlatIP.index")
+    # dna_index_L2 = faiss.read_index("big_dna_index_FlatL2.index")
+    # dna_index_HNSW = faiss.read_index("big_dna_index_HNSWFlat.index")
+    # dna_index_IVF = faiss.read_index("big_dna_index_IVFFlat.index")
+    # dna_index_LSH = faiss.read_index("big_dna_index_LSH.index")
 
     with open("dataset_processid_list.pickle", "rb") as f:
         dataset_processid_list = pickle.load(f)
@@ -109,14 +119,15 @@ with gr.Blocks() as demo:
                 mod1 = gr.Radio(choices=["DNA", "Image"], label="Search From:")
                 mod2 = gr.Radio(choices=["DNA", "Image"], label="Search To:")
 
-        indexType = gr.Radio(choices=["FlatIP(default)", "FlatL2", "HNSWFlat", "IVFFlat", "LSH"], label="Index:", value="FlatIP(default)")
+        indexType = gr.Radio(
+            choices=["FlatIP(default)", "FlatL2", "HNSWFlat", "IVFFlat", "LSH"], label="Index:", value="FlatIP(default)"
+        )
         process_id = gr.Textbox(label="ID:", info="Enter a sample ID to search for")
-        process_id_list = gr.Textbox(label="Closest 10 matches:" )
-        search_btn = gr.Button("Search") 
+        process_id_list = gr.Textbox(label="Closest 10 matches:")
+        search_btn = gr.Button("Search")
         id_btn.click(fn=getRandID, inputs=[], outputs=[rand_id, rand_id_indx])
 
-    search_btn.click(fn=searchEmbeddings, inputs=[process_id, mod1, mod2, indexType], 
-                     outputs=[process_id_list])
-    
+    search_btn.click(fn=searchEmbeddings, inputs=[process_id, mod1, mod2, indexType], outputs=[process_id_list])
+
 
-demo.launch()
+demo.launch()