app.py

app.py

@@ -11,6 +11,17 @@ import gradio as gr
 # Load the openAI's CLIP model
 #model, preprocess = clip.load("ViT-B/32", jit=False)
 #display output photo
+
+# taking photo IDs
+photo_ids = pd.read_csv("./photo_ids.csv")
+photo_ids = list(photo_ids['photo_id'])
+  
+# Photo dataset
+photos = pd.read_csv("./photos.tsv000", sep="\t", header=0)
+  
+# taking features vectors
+photo_features = np.load("./features.npy")
+
 def show_output_image(matched_images, photos) :
    image=[]
    for photo_id in matched_images:
@@ -42,16 +53,8 @@ def find_matches(text_features, photo_features, photo_ids, results_count=4):
   best_photo_idx = (-similarities).argsort()
   # Return the photo IDs of the best matches
   return [photo_ids[i] for i in best_photo_idx[:results_count]]
-def image_search(search_text, search_image, option):
-  # taking photo IDs
-  photo_ids = pd.read_csv("./photo_ids.csv")
-  photo_ids = list(photo_ids['photo_id'])
   
-  # Photo dataset
-  photos = pd.read_csv("./photos.tsv000", sep="\t", header=0)
-  
-  # taking features vectors
-  photo_features = np.load("./features.npy")
+def image_search(search_text, search_image, option):
   
   # check if CUDA available 
   device = "cuda" if torch.cuda.is_available() else "cpu"