feat(visual): moved things around

files.py

@@ -1,4 +1,5 @@
 import streamlit as st
+import os
 from loaders.audio import process_audio
 from loaders.txt import process_txt
 from loaders.csv import process_csv

loaders/common.py

@@ -1,6 +1,7 @@
 import tempfile
 from utils import compute_sha1_from_file
 from langchain.schema import Document
+import streamlit as st
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 
 def process_file(vector_store, file, loader_class, file_suffix):

main.py

@@ -1,16 +1,14 @@
+# main.py
 import os
 import tempfile
 
 import streamlit as st
-from sidebar import sidebar
 from files import file_uploader
 from question import chat_with_doc
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.vectorstores import SupabaseVectorStore
 from supabase import Client, create_client
 
-
-
 # supabase_url = "https://fqgpcifsfmamprzldyiv.supabase.co"
 supabase_url = st.secrets.supabase_url
 supabase_key = st.secrets.supabase_service_key
@@ -20,16 +18,44 @@ supabase: Client = create_client(supabase_url, supabase_key)
 embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
 vector_store = SupabaseVectorStore(supabase, embeddings, table_name="documents")
 
+# Set the theme
+st.set_page_config(
+    page_title="Second Brain",
+    layout="wide",
+    initial_sidebar_state="expanded",
 
-
+)
 
 st.title("🧠 Second Brain 🧠")
 st.markdown("Store your knowledge in a vector store and query it with OpenAI's GPT-3/4.")
 st.markdown("---\n\n")
 
+# Initialize session state variables
+if 'model' not in st.session_state:
+    st.session_state['model'] = "gpt-3.5-turbo"
+if 'temperature' not in st.session_state:
+    st.session_state['temperature'] = 0.0
+if 'chunk_size' not in st.session_state:
+    st.session_state['chunk_size'] = 500
+if 'chunk_overlap' not in st.session_state:
+    st.session_state['chunk_overlap'] = 0
 
+# Create a radio button for user to choose between adding knowledge or asking a question
+user_choice = st.radio("Choose an action", ('Add Knowledge to the Brain', 'Ask a Question to the Brain'))
+
+if user_choice == 'Add Knowledge to the Brain':
+    # Display chunk size and overlap selection only when adding knowledge
+    st.sidebar.title("Configuration") 
+    st.sidebar.markdown("Choose your chunk size and overlap for adding knowledge.")
+    st.session_state['chunk_size'] = st.sidebar.slider("Select Chunk Size", 100, 1000, st.session_state['chunk_size'], 50)
+    st.session_state['chunk_overlap'] = st.sidebar.slider("Select Chunk Overlap", 0, 100, st.session_state['chunk_overlap'], 10)
+    file_uploader(supabase, openai_api_key, vector_store)
+elif user_choice == 'Ask a Question to the Brain':
+    # Display model and temperature selection only when asking questions
+    st.sidebar.title("Configuration") 
+    st.sidebar.markdown("Choose your model and temperature for asking questions.")
+    st.session_state['model'] = st.sidebar.selectbox("Select Model", ["gpt-3.5-turbo", "gpt-4"], index=("gpt-3.5-turbo", "gpt-4").index(st.session_state['model']))
+    st.session_state['temperature'] = st.sidebar.slider("Select Temperature", 0.0, 1.0, st.session_state['temperature'], 0.1)
+    chat_with_doc(openai_api_key, vector_store)
 
-sidebar(supabase)
-file_uploader(supabase,openai_api_key, vector_store)
 st.markdown("---\n\n")
-chat_with_doc(openai_api_key, vector_store)

question.py

@@ -3,16 +3,12 @@ from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
 from langchain.llms import OpenAI
 
-
 memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
-
-def chat_with_doc(openai_api_key,vector_store):
+def chat_with_doc(openai_api_key, vector_store):
     question = st.text_input("## Ask a question")
-    temperature = st.session_state.get("temperature", 0.0)
-    model = st.session_state.get("model", "gpt-3.5-turbo")
     button = st.button("Ask")
     if button:
-        qa = ConversationalRetrievalChain.from_llm(OpenAI(model_name=model, openai_api_key=openai_api_key, temperature=temperature), vector_store.as_retriever(), memory=memory)
+        qa = ConversationalRetrievalChain.from_llm(OpenAI(model_name=st.session_state['model'], openai_api_key=openai_api_key, temperature=st.session_state['temperature']), vector_store.as_retriever(), memory=memory)
         result = qa({"question": question})
         st.write(result["answer"])

sidebar.py

@@ -2,31 +2,10 @@ import streamlit as st
 
 
 def sidebar(supabase):
-    st.sidebar.title("Configuration") 
-    
-    
-    ## Get the number of documents in the database
+    st.sidebar.title("Database Information") 
     number_of_docs = number_of_documents(supabase)
-    ## Display the number of documents in the database
     st.sidebar.markdown(f"**Docs in DB:**  {number_of_docs}")
 
-    ## Allow user to choose model between gpt-3.5-turbo and gpt-4
-    model = st.sidebar.selectbox("Select Model", ["gpt-3.5-turbo", "gpt-4"])
-    ## Allow user to choose temperature between 0.0 and 1.0
-    temperature = st.sidebar.slider("Select Temperature", 0.0, 1.0, 0.0, 0.1)
-    ## Allow user to choose chunk_size between 100 and 1000
-    chunk_size = st.sidebar.slider("Select Chunk Size", 100, 1000, 500, 50)
-    ## Allow user to choose chunk_overlap between 0 and 100
-    chunk_overlap = st.sidebar.slider("Select Chunk Overlap", 0, 100, 0, 10)
-
-
-    ## Save the user's choices
-    st.session_state.model = model
-    st.session_state.temperature = temperature
-    st.session_state.chunk_size = chunk_size
-    st.session_state.chunk_overlap = chunk_overlap
-
 def number_of_documents(supabase):
-    ## Get the number of documents in the database
     documents = supabase.table("documents").select("id", count="exact").execute()
     return documents.count