Hi all,
Apologies in advance if there is something obvious here as I am relatively new to coding. I had created an AI chatbot using the Streamlit documentation and I trained it on some Markdown files. It was all working really well until suddenly it crashed and I haven’t been able to get it working again.
To clarify I hadn’t deployed the app yet and my current python 3.11.5 and Streamlit version 1.28.2.
Anything anyone can identify to help would be massively appreciated! Thank you:)
Please see my code below:
import streamlit as st
from openai import OpenAI
from PIL import Image
from llama_index import VectorStoreIndex, ServiceContext, Document
from llama_index.llms import OpenAI
from llama_index import SimpleDirectoryReader
import os
Set OpenAI API key
os.environ[“OPENAI_API_KEY”] = st.secrets[“OPENAI_API_KEY”]
client = OpenAI(api_key=“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”)
Load data and create index
@st.cache_resource(show_spinner=False)
def load_data():
with st.spinner(text=“Loading and indexing the ConstGuide knowledge base - hang tight!”):
reader = SimpleDirectoryReader(input_dir=“/Users/joeposnett/Desktop/streamlit_data”, recursive=True)
docs = reader.load_data()
service_context = ServiceContext.from_defaults(llm=OpenAI(model=“gpt-3.5-turbo”, embed_model=‘local’, temperature=0.5, system_prompt=“you are an expert on the Streamlit Python library and your job is to answer technical questions. Assume that all questions are related to the Streamlit Python Library. Do not hallucinate features”))
index = VectorStoreIndex.from_documents(docs, service_context=service_context)
return index
index = load_data()
Setting up a session state (chat history) variable and selecting the GPT model to use.
if “openai_model” not in st.session_state:
GPT_MODEL = “gpt-3.5-turbo”
st.session_state[“openai_model”] = GPT_MODEL
with st.chat_message(name=“assistant”, avatar=“\U0001F477”):
st.write(“My name is Site-based Sam and I will be your AI construction helper”)
Initialising the chat history
if “messages” not in st.session_state:
st.session_state.messages =
Displaying chat messages from history on app re-run
for message in st.session_state.messages:
role = message[“role”]
avatar = “\U0001F477” if role == “assistant” else None
with st.chat_message(role, avatar=avatar):
st.markdown(message[“content”])
Reacting to user input
if prompt := st.chat_input(“How can I help?”):
# Displaying user message in chat message container
with st.chat_message(“user”):
st.markdown(prompt)
# Adding user message to chat history (session state variable)
st.session_state.messages.append({“role”: “user”, “content”: prompt})
Creating a chat message container for the assistant
with st.chat_message(“assistant”, avatar=“\U0001F477”):
message_placeholder = st.empty()
full_response = “”
# Call the OpenAI API and pass the model and conversation history
for response in client.chat.completions.create(
model=st.session_state[“openai_model”],
messages=[
{“role”: m[“role”], “content”: m[“content”]}
for m in st.session_state.messages
],
stream=True
):
# Convert the dictionary response to a string
response_text = response.choices[0].message.content
full_response += response_text
message_placeholder.markdown(full_response + "| ")
message_placeholder.markdown(full_response)
# Add ChatGPT response to the messages in the session state
st.session_state.messages.append({"role": "assistant", "content": full_response})
