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import streamlit as st
import os
from llama_index import (
ServiceContext,
SimpleDirectoryReader,
VectorStoreIndex,
)
# Import OpenAI only once, avoiding naming conflict
from openai import OpenAI as OpenAIClient
client = OpenAIClient(api_key=os.getenv("OPENAI_API_KEY"))
# Define Streamlit layout and interaction
st.title("Grounded Generations")
# Upload PDF
uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
@st.cache_resource(show_spinner=False)
def load_data(uploaded_file):
with st.spinner('Indexing document...'):
# Save the uploaded file temporarily
with open("temp.pdf", "wb") as f:
f.write(uploaded_file.read())
# Read and index documents using SimpleDirectoryReader
reader = SimpleDirectoryReader(input_dir="./", recursive=False)
docs = reader.load_data()
# The model configuration should be moved to where you actually call the OpenAI API
service_context = ServiceContext.from_defaults(
system_prompt="You are an AI assistant that uses context from a PDF to assist the user in generating text."
)
index = VectorStoreIndex.from_documents(docs, service_context=service_context)
return index
# Placeholder for document indexing
if uploaded_file is not None:
index = load_data(uploaded_file)
# Take user query input
user_query = st.text_input("Search for the products/info you want to use to ground your generated text content:")
# Initialize session_state for retrieved_text if not already present
if 'retrieved_text' not in st.session_state:
st.session_state['retrieved_text'] = ''
# Search and display retrieved text
if st.button("Retrieve"):
with st.spinner('Retrieving text...'):
# Use VectorStoreIndex to search
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']}")
# Select content type
content_type = st.selectbox("Select content type:", ["Blog", "Tweet"])
# Generate text based on retrieved text and selected content type
if st.button("Generate") and content_type:
with st.spinner('Generating text...'):
try:
prompt = f"Write a blog about 500 words in length using {st.session_state['retrieved_text']}" if content_type == "Blog" else f"Compose a tweet using {st.session_state['retrieved_text']}"
response = client.chat.completions.create(model="gpt-3.5-turbo-16k",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
])
generated_text = response.choices[0].message.content
st.write(f"Generated Text: {generated_text}")
except Exception as e:
st.write(f"An error occurred: {e}")
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