Update app.py

app.py

@@ -12,7 +12,7 @@ from llama_index.embeddings.gemini import GeminiEmbedding
 from llama_index.core.memory import ChatMemoryBuffer
 from llama_index.readers.web import FireCrawlWebReader
 from llama_index.core import SummaryIndex
-import streamlit_analytics2 as streamlit_analytics
+#import streamlit_analytics2 as streamlit_analytics
 import time
 import dotenv
 
@@ -99,71 +99,71 @@ Be the programmer you've always wanted to be.
 3. Ask any question you want
 """)
 
-with streamlit_analytics.track():
-    # URL input for document ingestion
-    st.session_state['url'] = st.text_input("Enter URL to crawl and ingest documents (optional):", value=st.session_state['url'])
-    
-    # Collection name input
-    st.session_state['collection_name'] = st.text_input("Enter collection name for vector store (compulsory):", value=st.session_state['collection_name'])
-    
-    # Combined Ingest and Setup button
-    if st.button("Ingest and Setup"):
-        with st.spinner("Setting up query engine..."):
-            embed_setup()
-            client = qdrant_setup()
-            llm = llm_setup()
-            vector_store = QdrantVectorStore(client=client, collection_name=st.session_state['collection_name'])
-            storage_context = StorageContext.from_defaults(vector_store=vector_store)
-            
-            if st.session_state['url']:
-                st.session_state['documents'] = ingest_documents(st.session_state['url'])
-                st.session_state['index'] = VectorStoreIndex.from_documents(st.session_state['documents'], vector_store=vector_store, storage_context=storage_context)
-                st.success(f"Documents ingested from {st.session_state['url']} and query engine setup completed successfully!")
-            else:
-                st.session_state['index'] = VectorStoreIndex.from_vector_store(vector_store=vector_store, storage_context=storage_context)
-                st.success(f"Query engine setup completed successfully using existing collection: {st.session_state['collection_name']}")
-            
-            st.session_state['setup_complete'] = True
-    
-    # Query input
-    st.session_state['query'] = st.text_input("Enter your query:", value=st.session_state['query'])
-    
-    # Search button
-    if st.button("Search"):
-        if not st.session_state['setup_complete']:
-            st.error("Please complete the setup first")
-        elif st.session_state['query']:
-            with st.spinner("Searching..."):
+
+# URL input for document ingestion
+st.session_state['url'] = st.text_input("Enter URL to crawl and ingest documents (optional):", value=st.session_state['url'])
+
+# Collection name input
+st.session_state['collection_name'] = st.text_input("Enter collection name for vector store (compulsory):", value=st.session_state['collection_name'])
+
+# Combined Ingest and Setup button
+if st.button("Ingest and Setup"):
+    with st.spinner("Setting up query engine..."):
+        embed_setup()
+        client = qdrant_setup()
+        llm = llm_setup()
+        vector_store = QdrantVectorStore(client=client, collection_name=st.session_state['collection_name'])
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+        
+        if st.session_state['url']:
+            st.session_state['documents'] = ingest_documents(st.session_state['url'])
+            st.session_state['index'] = VectorStoreIndex.from_documents(st.session_state['documents'], vector_store=vector_store, storage_context=storage_context)
+            st.success(f"Documents ingested from {st.session_state['url']} and query engine setup completed successfully!")
+        else:
+            st.session_state['index'] = VectorStoreIndex.from_vector_store(vector_store=vector_store, storage_context=storage_context)
+            st.success(f"Query engine setup completed successfully using existing collection: {st.session_state['collection_name']}")
+        
+        st.session_state['setup_complete'] = True
+
+# Query input
+st.session_state['query'] = st.text_input("Enter your query:", value=st.session_state['query'])
+
+# Search button
+if st.button("Search"):
+    if not st.session_state['setup_complete']:
+        st.error("Please complete the setup first")
+    elif st.session_state['query']:
+        with st.spinner("Searching..."):
+            try:
+                chat_engine = query_index(st.session_state['index'])
+                response = chat_engine.chat(st.session_state['query'])
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}")
+                st.info("Retrying in 120 seconds...")
+                time.sleep(120)
                 try:
                     chat_engine = query_index(st.session_state['index'])
                     response = chat_engine.chat(st.session_state['query'])
                 except Exception as e:
-                    st.error(f"An error occurred: {str(e)}")
-                    st.info("Retrying in 120 seconds...")
-                    time.sleep(120)
-                    try:
-                        chat_engine = query_index(st.session_state['index'])
-                        response = chat_engine.chat(st.session_state['query'])
-                    except Exception as e:
-                        st.error(f"Retry failed. Error: {str(e)}")
-                        st.stop()
-
-            # Add the query and response to chat history
-            st.session_state['chat_history'].append(("User", st.session_state['query']))
-            st.session_state['chat_history'].append(("Assistant", str(response.response)))
-            
-            # Display the most recent response prominently
-            st.subheader("Assistant's Response:")
-            st.write(response.response)
-        else:
-            st.error("Please enter a query")
-    
-    # Sidebar for chat history
-    st.sidebar.title("Chat History")
-    for role, message in st.session_state['chat_history']:
-        st.sidebar.text(f"{role}: {message}")
-    
-    # Clear chat history button in sidebar
-    if st.sidebar.button("Clear Chat History"):
-        st.session_state['chat_history'] = []
-        st.sidebar.success("Chat history cleared!")
+                    st.error(f"Retry failed. Error: {str(e)}")
+                    st.stop()
+
+        # Add the query and response to chat history
+        st.session_state['chat_history'].append(("User", st.session_state['query']))
+        st.session_state['chat_history'].append(("Assistant", str(response.response)))
+        
+        # Display the most recent response prominently
+        st.subheader("Assistant's Response:")
+        st.write(response.response)
+    else:
+        st.error("Please enter a query")
+
+# Sidebar for chat history
+st.sidebar.title("Chat History")
+for role, message in st.session_state['chat_history']:
+    st.sidebar.text(f"{role}: {message}")
+
+# Clear chat history button in sidebar
+if st.sidebar.button("Clear Chat History"):
+    st.session_state['chat_history'] = []
+    st.sidebar.success("Chat history cleared!")