Update app.py

app.py

@@ -18,18 +18,18 @@ folder_path = '/home/user/app/qids_folder'
 
 if not os.path.exists(folder_path):
     os.mkdir(folder_path)
-    print(f"Folder 'qids_folder' created at {folder_path}")
+    print(f"folder created at {folder_path}")
 else:
-    print(f"Folder 'qids_folder' already exists.")
+    print(f"folder already exists.")
 
 
 folder_path_1 = '/home/user/app/info_extraction'
 
 if not os.path.exists(folder_path_1):
     os.mkdir(folder_path_1)
-    print(f"Folder 'qids_folder' created at {folder_path_1}")
+    print(f"Folder created at {folder_path_1}")
 else:
-    print(f"Folder 'qids_folder' already exists.")
+    print(f"folder already exists.")
 
 model = SentenceTransformer("Lajavaness/bilingual-embedding-large", trust_remote_code=True)
 
@@ -297,8 +297,8 @@ def main_cli():
     st.caption("This Web Application is part of my master dissertation.")
     
     
-    input_sentence_user = st.text_input("Enter the sentence:", "")
-    input_mention_user = st.text_input("Enter the mention:", "")
+    input_sentence_user = st.text_input("Enter a sentence:", "")
+    input_mention_user = st.text_input("Enter a textural reference (mention) that is inside the sentence:", "")
     single = st.selectbox("Search each word individually?", ['Yes', 'No'], index=1)
     combi = st.selectbox("Make combinations of each word?", ['Yes', 'No'], index=1)
     disambi = st.selectbox("Run acronym disambiguation? (Enable it if the mention is nested)", ['Yes', 'No'], index=0)
@@ -306,7 +306,7 @@ def main_cli():
     
     if st.button("Run Entity Linking"):
         if input_sentence_user and input_mention_user:
-            # Example logic: check if the mention is in the sentence
+            # check if the mention is in the sentence
             if input_mention_user in input_sentence_user:
                 st.write("Applying Data Normalization module... (1/5)")
                 # Data Normalization
@@ -421,13 +421,13 @@ def main_cli():
                     context = i.split(":")[-1].strip()
                     list_with_contexts.append(context)
 
-                # Candidate Generation & Information Extraction
+                # Candidate Retrieval & Information Gathering
                 async def big_main(mention, single, combi):
                     mention = mention.split(",")
-                    st.write("Applying Candidate Generation module... (2/5)")
+                    st.write("Applying Candidate Retrieval module... (2/5)")
                     for i in mention:
                         await mains(i, single, combi)
-                    st.write("Applying Information Extraction module... (3/5)")
+                    st.write("Applying Information Gathering module... (3/5)")
                     for i in mention:
                         await main(i)
                         
@@ -464,7 +464,7 @@ def main_cli():
                                 lista_1.append({k: emb_mean})
 
                         sorted_data = sorted(lista_1, key=lambda x: list(x.values())[0], reverse=True)
-                        st.write(f"Applying Entity Linking module... (4/5) [{number}/{len(list_with_full_names)}]")
+                        st.write(f"Applying Candidate Matching module... (4/5) [{number}/{len(list_with_full_names)}]")
                         if sorted_data:
                             sorted_top = sorted_data[0]
                             for k, v in sorted_top.items():
@@ -510,7 +510,8 @@ def main_cli():
         execution_time = end_time - start_time
         ETA = time.strftime("%H:%M:%S", time.gmtime(execution_time))
         st.write(f"Execution time: {ETA}")
-        
+
+        # i think this part can be removed now
         folder_path = "qids_folder"
         for filename in os.listdir(folder_path):
             file_path = os.path.join(folder_path, filename)