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import streamlit as st
import os
import pandas as pd
import openai
from openai import OpenAI
import pkg_resources
import shutil
import main
### To trigger trulens evaluation
main.main()
### Finally, start streamlit app
leaderboard_path = pkg_resources.resource_filename(
"trulens_eval", "Leaderboard.py"
)
evaluation_path = pkg_resources.resource_filename(
"trulens_eval", "pages/Evaluations.py"
)
ux_path = pkg_resources.resource_filename(
"trulens_eval", "ux"
)
shutil.copyfile(leaderboard_path, os.path.join("pages", "1_Leaderboard.py"))
shutil.copyfile(evaluation_path, os.path.join("pages", "2_Evaluations.py"))
if os.path.exists("./ux"):
shutil.rmtree("./ux")
shutil.copytree(ux_path, "./ux")
# App title
st.set_page_config(page_title="💬 Open AI Chatbot")
openai_api = os.getenv("OPENAI_API_KEY")
data_df = pd.DataFrame(
{
"Completion": [30, 40, 100, 10],
}
)
data_df.index = ["Chapter 1", "Chapter 2", "Chapter 3", "Chapter 4"]
# Replicate Credentials
with st.sidebar:
st.title("💬 Open AI Chatbot")
st.write("This chatbot is created using the GPT model from Open AI.")
if openai_api:
pass
elif "OPENAI_API_KEY" in st.secrets:
st.success("API key already provided!", icon="✅")
openai_api = st.secrets["OPENAI_API_KEY"]
else:
openai_api = st.text_input("Enter OpenAI API token:", type="password")
if not (openai_api.startswith("sk-") and len(openai_api)==51):
st.warning("Please enter your credentials!", icon="⚠️")
else:
st.success("Proceed to entering your prompt message!", icon="👉")
### for streamlit purpose
os.environ["OPENAI_API_KEY"] = openai_api
st.subheader("Models and parameters")
selected_model = st.sidebar.selectbox("Choose an OpenAI model",
["gpt-3.5-turbo-1106", "gpt-4-1106-preview"],
key="selected_model")
temperature = st.sidebar.slider("temperature", min_value=0.01, max_value=2.0,
value=0.1, step=0.01)
st.data_editor(
data_df,
column_config={
"Completion": st.column_config.ProgressColumn(
"Completion %",
help="Percentage of content covered",
format="%.1f%%",
min_value=0,
max_value=100,
),
},
hide_index=False,
)
st.markdown("📖 Reach out to SakiMilo to learn how to create this app!")
# Store LLM generated responses
if "messages" not in st.session_state.keys():
st.session_state.messages = [{"role": "assistant",
"content": "How may I assist you today?"}]
# Display or clear chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
def clear_chat_history():
st.session_state.messages = [{"role": "assistant",
"content": "How may I assist you today?"}]
st.sidebar.button("Clear Chat History", on_click=clear_chat_history)
def generate_llm_response(client, prompt_input):
system_content = ("You are a helpful assistant. "
"You do not respond as 'User' or pretend to be 'User'. "
"You only respond once as 'Assistant'."
)
completion = client.chat.completions.create(
model=selected_model,
messages=[
{"role": "system", "content": system_content},
] + st.session_state.messages,
temperature=temperature,
stream=True
)
return completion
# User-provided prompt
if prompt := st.chat_input(disabled=not openai_api):
client = OpenAI()
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response = generate_llm_response(client, prompt)
placeholder = st.empty()
full_response = ""
for chunk in response:
if chunk.choices[0].delta.content is not None:
full_response += chunk.choices[0].delta.content
placeholder.markdown(full_response)
placeholder.markdown(full_response)
message = {"role": "assistant", "content": full_response}
st.session_state.messages.append(message) |