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import streamlit as st
import os
import shelve
from g4f.client import Client
st.title("Streamlit Chatbot Interface")
USER_AVATAR = "👤"
BOT_AVATAR = "🤖"
client = Client()
# Ensure openai_model is initialized in session state
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = "gpt-3.5-turbo"
# Load chat history from shelve file
def load_chat_history():
with shelve.open("chat_history") as db:
return db.get("messages", [])
# Save chat history to shelve file
def save_chat_history(messages):
with shelve.open("chat_history") as db:
db["messages"] = messages
# Initialize or load chat history
if "messages" not in st.session_state:
st.session_state.messages = load_chat_history()
# Sidebar with a button to delete chat history
with st.sidebar:
if st.button("Delete Chat History"):
st.session_state.messages = []
save_chat_history([])
# Display chat messages
for message in st.session_state.messages:
avatar = USER_AVATAR if message["role"] == "user" else BOT_AVATAR
with st.chat_message(message["role"], avatar=avatar):
st.markdown(message["content"])
# Main chat interface
if prompt := st.chat_input("How can I help?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user", avatar=USER_AVATAR):
st.markdown(prompt)
with st.chat_message("assistant", avatar=BOT_AVATAR):
message_placeholder = st.empty()
full_response = ""
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=st.session_state["messages"],
)
full_response = response.choices[0].message.content
message_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})
# Save chat history after each interaction
save_chat_history(st.session_state.messages)
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