Simplify code

app.py

@@ -10,14 +10,13 @@ from transformers import CLIPProcessor, CLIPTextModel, CLIPModel
                       dict: lambda _: None})
 def load():
   model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-  text_model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
   processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
   df = {0: pd.read_csv('data.csv'), 1: pd.read_csv('data2.csv')}
   embeddings = {0: np.load('embeddings.npy'), 1: np.load('embeddings2.npy')}
   for k in [0, 1]:
     embeddings[k] = np.divide(embeddings[k], np.sqrt(np.sum(embeddings[k]**2, axis=1, keepdims=True)))
-  return model, text_model, processor, df, embeddings
-model, text_model, processor, df, embeddings = load()
+  return model, processor, df, embeddings
+model, processor, df, embeddings = load()
 
 source = {0: '\nSource: Unsplash', 1: '\nSource: The Movie Database (TMDB)'}
 
@@ -33,7 +32,7 @@ def get_html(url_list, height=200):
 
 def compute_text_embeddings(list_of_strings):
     inputs = processor(text=list_of_strings, return_tensors="pt", padding=True)
-    return model.text_projection(text_model(**inputs).pooler_output)
+    return model.get_text_features(**inputs)
 
 st.cache(show_spinner=False)
 def image_search(query, corpus, n_results=24):