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import streamlit as st
import openai
import os
# Function to get the API key from Streamlit secrets
def get_api_key():
try:
return st.secrets["API_KEY"]
except KeyError:
st.error("API_KEY not found in Streamlit secrets. Please add it.")
return None
# Function to interact with the OpenAI API with streaming
def generate_response(prompt, model_name, api_key, extra_instructions=""):
try:
client = openai.OpenAI(api_key=api_key) # Instantiate OpenAI client with api_key
messages = [{"role": "user", "content": prompt + "\n" + extra_instructions}]
stream = client.chat.completions.create(
model=model_name,
messages=messages,
stream=True,
)
return stream
except openai.APIError as e:
st.error(f"OpenAI API Error with {model_name}: {e}")
return None
except openai.RateLimitError as e:
st.error(f"OpenAI Rate Limit Error with {model_name}: {e}")
return None
except openai.AuthenticationError as e:
st.error(f"OpenAI Authentication Error with {model_name}: {e}")
return None
except Exception as e:
st.error(f"An unexpected error occurred with {model_name}: {e}")
return None
# Main Streamlit app
def main():
st.title("Chatbot with Model Switching and Streaming")
# Initialize conversation history in session state
if "messages" not in st.session_state:
st.session_state.messages = []
# Display previous messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Get user input
prompt = st.chat_input("Say something")
if prompt:
# Add user message to the state
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Define model priority
models = ["gpt-4-1106-preview", "gpt-3.5-turbo"] # Add more models as needed
# Get API key
api_key = get_api_key()
if not api_key:
return
full_response = ""
for model in models:
stream = generate_response(prompt, model, api_key, extra_instructions="Please address the user as Jane Doe. The user has a premium account.")
if stream:
with st.chat_message("assistant"):
message_placeholder = st.empty()
for chunk in stream:
if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
full_response += chunk.choices[0].delta.content
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
break # Break after successful response
full_response = "" # Reset for the next model attempt
if full_response:
# Add bot message to state
st.session_state.messages.append({"role": "assistant", "content": full_response})
if __name__ == "__main__":
main() |