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import streamlit as st
import os
import openai
def get_completion(client, model_id, messages, args):
completion_args = {
"model": model_id,
"messages": messages,
"frequency_penalty": args.frequency_penalty,
"max_tokens": args.max_tokens,
"n": args.n,
"presence_penalty": args.presence_penalty,
"seed": args.seed,
"stop": args.stop,
"stream": args.stream,
"temperature": args.temperature,
"top_p": args.top_p,
}
completion_args = {
k: v for k, v in completion_args.items() if v is not None
}
try:
response = client.chat.completions.create(**completion_args)
return response
except Exception as e:
print(f"Error during API call: {e}")
return None
# App title
st.set_page_config(page_title="Turing Test")
# OpenAI Client Setup
with st.sidebar:
st.title('🦙💬 Welcome to Turing Test')
# Hardcoded API key
openai_api_key = "super-secret-token"
# st.success('API key already provided!', icon='✅')
os.environ['OPENAI_API_KEY'] = openai_api_key
openai.api_key = openai_api_key
openai.api_base = "https://turingtest--example-vllm-openai-compatible-serve.modal.run/v1"
client = openai.OpenAI(api_key=openai_api_key, base_url=openai.api_base)
# Add system prompt input
st.subheader('System Prompt')
system_prompt = st.text_area("Enter a system prompt:",
"you are rolplaying as an old grandma",
help="This message sets the behavior of the AI.")
st.subheader('Models and parameters')
selected_model = st.sidebar.selectbox('Choose a model', ['meta-llama/Meta-Llama-3.1-8B-Instruct'], key='selected_model')
temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=5.0, value=0.8, step=0.1)
top_p = st.sidebar.slider('top_p', min_value=0.01, max_value=1.0, value=0.95, step=0.01)
max_length = st.sidebar.slider('max_length', min_value=32, max_value=1024, value=32, step=8)
# Store chat history
if "messages" not in st.session_state.keys():
st.session_state.messages = [
{"role": "system", "content": system_prompt},
{"role": "assistant", "content": "Hello!"}
]
# Display chat messages (excluding system message)
for message in st.session_state.messages[1:]:
with st.chat_message(message["role"]):
st.write(message["content"])
def clear_chat_history():
st.session_state.messages = [
{"role": "system", "content": system_prompt},
{"role": "assistant", "content": "Hello!"}
]
st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
# Function for generating Llama2 response using OpenAI client API
def generate_llama2_response(prompt_input, model, temperature, top_p, max_length):
class Args:
def __init__(self):
self.frequency_penalty = 0
self.max_tokens = max_length
self.n = 1
self.presence_penalty = 0
self.seed = 42
self.stop = None
self.stream = False
self.temperature = temperature
self.top_p = top_p
args = Args()
# Update system message before each completion
st.session_state.messages[0] = {"role": "system", "content": system_prompt}
response = get_completion(client, model, st.session_state.messages, args)
if response:
return response.choices[0].message.content
else:
return "Sorry, there was an error generating a response."
# User-provided prompt
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
# Generate 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_llama2_response(prompt, selected_model, temperature, top_p, max_length)
placeholder = st.empty()
full_response = ''
for item in response:
full_response += item
placeholder.markdown(full_response)
message = {"role": "assistant", "content": full_response}
st.session_state.messages.append(message) |