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# Import the necessary libraries
import streamlit as st
from openai import OpenAI # TODO: Install the OpenAI library using pip install openai
st.title("Mini Project 2: Streamlit Chatbot")
# TODO: Replace with your actual OpenAI API key
openai_key = "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"
client = OpenAI(api_key=openai_key)
# Define a function to get the conversation history (Not required for Part-2, will be useful in Part-3)
def get_conversation() -> str:
# return: A formatted string representation of the conversation.
conversation = ""
for message in st.session_state.messages:
role = message["role"]
content = message["content"]
conversation += f"{role}: {content}\n"
return conversation
# Check for existing session state variables
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = "gpt-3.5-turbo" # Initialize model
if "messages" not in st.session_state:
st.session_state.messages = [] # Initialize messages as an empty list
# Display existing chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Wait for user input
if prompt := st.chat_input("What would you like to chat about?"):
# Append user message to messages
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message
with st.chat_message("user"):
st.markdown(prompt)
# Generate AI response
with st.chat_message("assistant"):
# Send request to OpenAI API
response = client.chat.completions.create(
model=st.session_state["openai_model"],
messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state.messages]
)
ai_response = response.choices[0].message.content
# Display AI response
st.markdown(ai_response)
# Append AI response to messages
st.session_state.messages.append({"role": "assistant", "content": ai_response}) |