Update app.py

app.py

@@ -39,25 +39,25 @@ def get_clip_embeddings(input_data, input_type='text'):
 
 
 veggies = load_dataset('vojtam/vegetables')
+with open('img_embeddings.pkl', 'rb') as file:
+    img_embeddings = pickle.load(file)
 
-
-text = gr.Textbox(label = "Enter the text")
+text = gr.Textbox(label = "Enter the text", 'Your text goes here')
 image = gr.Gallery()
 
 def get_similar_images(text, n = 4):
-    with open('img_embeddings.pkl', 'rb') as file:
-        img_embeddings = pickle.load(file)
-    text_embedding = get_clip_embeddings(text, input_type='text')
-    cos = nn.CosineSimilarity(dim=1, eps=1e-6)
-    sims = cos(torch.tensor(text_embedding), torch.tensor(img_embeddings))
-    top_n = np.argsort(np.array(sims))[::-1][:4]
-    print(top_n)
-    print(img_embeddings)
-    imgs = []
-    
-    for index in top_n:
-        imgs.append(veggies['train'][index.item()]['image'])
-    return imgs
+    if text:
+        text_embedding = get_clip_embeddings(text, input_type='text')
+        cos = nn.CosineSimilarity(dim=1, eps=1e-6)
+        sims = cos(torch.tensor(text_embedding), torch.tensor(img_embeddings))
+        top_n = np.argsort(np.array(sims))[::-1][:n]
+        print(top_n)
+        print(img_embeddings)
+        imgs = []
+        
+        for index in top_n:
+            imgs.append(veggies['train'][index.item()]['image'])
+        return imgs