Update app.py

app.py

@@ -244,36 +244,30 @@ def match_keywords_and_titles_case2(file_path):
     model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v2')
     sentences = keywords
     
-    # Using tqdm to show progress and estimated time
-    progress_bar = tqdm(total=len(sentences), desc="Embeddings progress", ncols=100)
-    time.sleep(0.1)
+    # # Using tqdm to show progress and estimated time
+    # progress_bar = tqdm(total=len(sentences), desc="Embeddings progress", ncols=100)
+    # time.sleep(0.1)
+
 
-    # # Using st.progress to show progress
     # progress_bar = st.progress(0)
     # embeddings = []
 
+    # start_time = time.time()
+    # display_interval = int(len(keywords) * 0.05)  # 5% of total keywords
     # for i, keyword in enumerate(keywords):
     #     embedding = model.encode([keyword])
     #     embeddings.append(embedding[0])
-    #     progress_bar.progress((i + 1) / len(keywords))
-
-    # Using st.progress to show progress and estimate time left
-    progress_bar = st.progress(0)
-    embeddings = []
-
-    start_time = time.time()
-    display_interval = int(len(keywords) * 0.05)  # 5% of total keywords
-    for i, keyword in enumerate(keywords):
-        embedding = model.encode([keyword])
-        embeddings.append(embedding[0])
         
-        current_progress = (i + 1) / len(keywords)
-        progress_bar.progress(current_progress)
+    #     current_progress = (i + 1) / len(keywords)
+    #     progress_bar.progress(current_progress)
         
-        if (i + 1) % display_interval == 0:
-            elapsed_time = time.time() - start_time
-            estimated_time_left = (elapsed_time / current_progress) * (1 - current_progress)
-            st.write(f"Estimated time left: {estimated_time_left:.2f} seconds")
+    #     if (i + 1) % display_interval == 0:
+    #         elapsed_time = time.time() - start_time
+    #         estimated_time_left = (elapsed_time / current_progress) * (1 - current_progress)
+    #         st.write(f"Estimated time left: {estimated_time_left:.2f} seconds")
+
+
+    embeddings = model.encode(sentences)
 
 
     # Initialize keyword usage tracking