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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
def generate_response(prompt, model_name, api_key):
openai.api_key = api_key
try:
completion = openai.ChatCompletion.create(
model=model_name,
messages=[{"role": "user", "content": prompt}]
)
return completion.choices[0].message.content
except openai.APIError as e: # General API Error
st.error(f"OpenAI API Error with {model_name}: {e}")
return None
except openai.RateLimitError as e: # Rate Limit Error
st.error(f"OpenAI Rate Limit Error with {model_name}: {e}")
return None
except openai.AuthenticationError as e: # Authentication Error
st.error(f"OpenAI Authentication Error with {model_name}: {e}")
return None
except Exception as e: # Catch any other exception
st.error(f"An unexpected error occurred with {model_name}: {e}")
return None
# Main Streamlit app
def main():
st.title("Chatbot with Model Switching")
# 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", "gpt-3.5-turbo"] # Add more models as needed
# Get API key
api_key = get_api_key()
if not api_key:
return
response = None
for model in models:
response = generate_response(prompt, model, api_key)
if response:
break # If a response is generated, break out of loop.
if response:
# Add bot message to state
st.session_state.messages.append({"role": "assistant", "content": response})
with st.chat_message("assistant"):
st.markdown(response)
if __name__ == "__main__":
main() |