Update app.py

app.py

@@ -11,7 +11,7 @@ import numpy as np
 # Initialize models and variables
 summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
-index = None
+faiss_index = None  # Renamed from 'index' to 'faiss_index'
 bookmarks = []
 fetch_cache = {}
 
@@ -78,12 +78,12 @@ def vectorize_and_index(bookmarks):
     summaries = [bookmark['summary'] for bookmark in bookmarks]
     embeddings = embedding_model.encode(summaries)
     dimension = embeddings.shape[1]
-    faiss_index = faiss.IndexFlatL2(dimension)
-    faiss_index.add(np.array(embeddings))
-    return faiss_index, embeddings
+    faiss_idx = faiss.IndexFlatL2(dimension)
+    faiss_idx.add(np.array(embeddings))
+    return faiss_idx, embeddings
 
 def process_uploaded_file(file):
-    global bookmarks, index
+    global bookmarks, faiss_index
     if file is None:
         return "Please upload a bookmarks HTML file."
 
@@ -94,16 +94,16 @@ def process_uploaded_file(file):
         fetch_url_info(bookmark)
         generate_summary(bookmark)
 
-    index, embeddings = vectorize_and_index(bookmarks)
+    faiss_index, embeddings = vectorize_and_index(bookmarks)
     return f"Successfully processed {len(bookmarks)} bookmarks."
 
 def chatbot_response(user_query):
-    if index is None or not bookmarks:
+    if faiss_index is None or not bookmarks:
         return "No bookmarks available. Please upload and process your bookmarks first."
 
     # Vectorize user query
     user_embedding = embedding_model.encode([user_query])
-    D, I = index.search(np.array(user_embedding), k=5)  # Retrieve top 5 matches
+    D, I = faiss_index.search(np.array(user_embedding), k=5)  # Retrieve top 5 matches
 
     # Generate response
     response = ""
@@ -119,30 +119,30 @@ def display_bookmarks():
         bookmark_list.append([i, bookmark['title'], bookmark['url'], status])
     return bookmark_list
 
-def edit_bookmark(index, new_title, new_url):
-    global index  # Reference the global index variable
+def edit_bookmark(bookmark_idx, new_title, new_url):
+    global faiss_index  # Reference the global faiss_index variable
     try:
-        index = int(index)
-        bookmarks[index]['title'] = new_title
-        bookmarks[index]['url'] = new_url
-        fetch_url_info(bookmarks[index])
-        generate_summary(bookmarks[index])
+        bookmark_idx = int(bookmark_idx)
+        bookmarks[bookmark_idx]['title'] = new_title
+        bookmarks[bookmark_idx]['url'] = new_url
+        fetch_url_info(bookmarks[bookmark_idx])
+        generate_summary(bookmarks[bookmark_idx])
         # Rebuild the FAISS index
-        index, embeddings = vectorize_and_index(bookmarks)
+        faiss_index, embeddings = vectorize_and_index(bookmarks)
         return "Bookmark updated successfully."
     except Exception as e:
         return f"Error: {str(e)}"
 
-def delete_bookmark(index):
-    global index  # Reference the global index variable
+def delete_bookmark(bookmark_idx):
+    global faiss_index  # Reference the global faiss_index variable
     try:
-        index = int(index)
-        bookmarks.pop(index)
+        bookmark_idx = int(bookmark_idx)
+        bookmarks.pop(bookmark_idx)
         # Rebuild the FAISS index
         if bookmarks:
-            index, embeddings = vectorize_and_index(bookmarks)
+            faiss_index, embeddings = vectorize_and_index(bookmarks)
         else:
-            index = None  # No bookmarks left
+            faiss_index = None  # No bookmarks left
         return "Bookmark deleted successfully."
     except Exception as e:
         return f"Error: {str(e)}"