Update app.py

app.py

@@ -41,19 +41,19 @@ class_list = list(df['class'])
 zip_path = "pascal_raw.zip"
 zip_file = zipfile.ZipFile(zip_path)
 
-# text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
-# text_tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
+text_tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
 d = 1024
 text_index = faiss.index_factory(d, "Flat", faiss.METRIC_INNER_PRODUCT)
 faiss.read_index("text_index.index")
 
 def T2Isearch(query, k=50):
     # Encode the text query        
-    # inputs = text_tokenizer([query], padding=True, return_tensors="pt")    
-    # outputs = text_model(**inputs)
-    # query_embedding = outputs.text_embeds
-    query_vector = test_xt[0]
-    # query_vector = np.array([query_embedding])
+    inputs = text_tokenizer([query], padding=True, return_tensors="pt")    
+    outputs = text_model(**inputs)
+    query_embedding = outputs.text_embeds
+    # query_vector = test_xt[0]
+    query_vector = np.array([query_embedding])
     faiss.normalize_L2(query_vector)
     # text_index.nprobe = index.ntotal
     text_index.nprobe = 100