Update app.py

app.py

@@ -1,6 +1,3 @@
-# Author: Brian King
-# For: BrandMuscle, Copyright 2023 All Rights Reserved 
-
 import streamlit as st
 import os
 from llama_index import (
@@ -12,7 +9,7 @@ from llama_index.llms import OpenAI
 import openai
 
 # Define Streamlit layout and interaction
-st.title("Streamlit App for PDF Retrieval and Text Generation")
+st.title("Grounded Generations")
 
 # Upload PDF
 uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
@@ -51,12 +48,12 @@ if 'retrieved_text' not in st.session_state:
 if st.button("Retrieve"):
     with st.spinner('Retrieving text...'):
         # Use VectorStoreIndex to search
-        query_engine = index.as_query_engine(similarity_top_k=3)
+        query_engine = index.as_query_engine(similarity_top_k=3) 
         st.session_state['retrieved_text'] = query_engine.query(user_query)
-        st.write(f"Retrieved Text: {st.session_state['retrieved_text']}")
+        st.write(f"Retrieved Text: {st.session_state['retrieved_text']}") # store the retrieved text as a streamlit state variable 
 
 # Select content type
-content_type = st.selectbox("Select content type:", ["Blog", "Tweet"])
+content_type = st.selectbox("Select content type:", ["Blog", "Tweet"]) # make some default nonsense 
 
 # Generate text based on retrieved text and selected content type
 if st.button("Generate") and content_type:
@@ -65,7 +62,7 @@ if st.button("Generate") and content_type:
         openai.api_key = os.getenv("OPENAI_API_KEY")
         try:
             if content_type == "Blog":
-                prompt = f"Write a blog about 500 words in length using the {st.session_state['retrieved_text']}"
+                prompt = f"Write a blog about 500 words in length using the {st.session_state['retrieved_text']}" #uses content from the stored state
             elif content_type == "Tweet":
                 prompt = f"Compose a tweet using the {st.session_state['retrieved_text']}"
             response = openai.ChatCompletion.create(