working

app.py

@@ -6,6 +6,8 @@ import numpy as np
 import torch.nn.functional as F
 import clip
 import cv2
+
+prev_prompt = None
 # Compute the cosine similarity between two vectors.
 def cosine_similarity(a, b):
     return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
@@ -22,26 +24,23 @@ def compute_text_embeddings(text_prompts):
     
     return text_embeddings
 
-zoo=dg.connect(dg.CLOUD,zoo_url='https://cs.degirum.com/degirum/kvk_upload_test',token=st.secrets["DG_TOKEN"])
+zoo=dg.connect(dg.CLOUD,zoo_url='https://cs.degirum.com/degirum/kvk_upload_test', token=st.secrets["DG_TOKEN"])
 
 st.title('DeGirum CLIP model Demo')
-embeddings = []
+
 with st.sidebar:
     st.header('Specify Model Options Below')
-    
-    prompts = st.text_area("Enter text prompts (comma-separated):", value="People Running, People Talking, People Fighting, People Laughing, People Dancing")
+    prompts = st.text_area("Enter text prompts (comma-separated):", value="People Running, People sitting, People swimming, People sleeping, People watching television")
     prompts = [prompt.strip() for prompt in prompts.split(',')]
-    if st.button("Generate Text Embedding"):
-        # Call the compute_text_embeddings function when the button is clicked
-        embeddings = compute_text_embeddings(prompts)
-        st.success("Text embeddings generated successfully!")
-    
 st.text('Upload an image. Then click on the submit button')
 with st.form("model_form"):
     uploaded_file=st.file_uploader('input image')
     submitted = st.form_submit_button("Submit")
-    # embeddings = compute_text_embeddings(prompts)
+    if prev_prompt is None or prev_prompt != prompts:
+        embeddings = compute_text_embeddings(prompts)
     if submitted:
+        if prev_prompt != prompts:
+            prev_prompt = prompts
         model=zoo.load_model('clip--224x224_float_openvino_cpu_4',
                              input_image_format = "RAW"                                    
                             )