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import streamlit as st
import openai
import requests
st.set_page_config(page_title="CodeLlama Playground - via DeepInfra", page_icon='🦙')
MODEL_IMAGES = {
"meta-llama/Meta-Llama-3-8B-Instruct": "https://em-content.zobj.net/source/twitter/376/llama_1f999.png", # Add the emoji for the Meta-Llama model
# "codellama/CodeLlama-34b-Instruct-hf": "https://em-content.zobj.net/source/twitter/376/llama_1f999.png",
# "mistralai/Mistral-7B-Instruct-v0.1": "https://em-content.zobj.net/source/twitter/376/tornado_1f32a-fe0f.png",
"mistralai/Mixtral-8x7B-Instruct-v0.1": "https://em-content.zobj.net/source/twitter/376/tornado_1f32a-fe0f.png",
}
# Create a mapping from formatted model names to their original identifiers
def format_model_name(model_key):
parts = model_key.split('/')
model_name = parts[-1] # Get the last part after '/'
name_parts = model_name.split('-')
# Custom formatting for specific models
if "Meta-Llama-3-8B-Instruct" in model_key:
return "Llama 3 8B-Instruct"
else:
# General formatting for other models
formatted_name = ' '.join(name_parts[:-2]).title() # Join them into a single string with title case
return formatted_name
formatted_names_to_identifiers = {
format_model_name(key): key for key in MODEL_IMAGES.keys()
}
# Debug to ensure names are formatted correctly
#st.write("Formatted Model Names to Identifiers:", formatted_names_to_identifiers)
selected_formatted_name = st.sidebar.radio(
"Select LLM Model",
list(formatted_names_to_identifiers.keys())
)
selected_model = formatted_names_to_identifiers[selected_formatted_name]
if MODEL_IMAGES[selected_model].startswith("http"):
st.image(MODEL_IMAGES[selected_model], width=90)
else:
st.write(f"Model Icon: {MODEL_IMAGES[selected_model]}", unsafe_allow_html=True)
# Display the selected model using the formatted name
model_display_name = selected_formatted_name # Already formatted
# st.write(f"Model being used: `{model_display_name}`")
st.sidebar.markdown('---')
API_KEY = st.secrets["api_key"]
openai.api_base = "https://api.deepinfra.com/v1/openai"
MODEL_CODELLAMA = selected_model
def get_response(api_key, model, user_input, max_tokens, top_p):
openai.api_key = api_key
try:
if "meta-llama/Meta-Llama-3-8B-Instruct" in model:
# Assume different API setup for Meta-Llama
chat_completion = requests.post(
"https://api.deepinfra.com/v1/openai/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": model,
"messages": [{"role": "user", "content": user_input}],
"max_tokens": max_tokens,
"top_p": top_p
}
).json()
return chat_completion['choices'][0]['message']['content'], None
else:
# Existing setup for other models
chat_completion = openai.ChatCompletion.create(
model=model,
messages=[{"role": "user", "content": user_input}],
max_tokens=max_tokens,
top_p=top_p
)
return chat_completion.choices[0].message.content, None
except Exception as e:
return None, str(e)
# Adjust the title based on the selected model
st.header(f"`{model_display_name}` Model")
with st.expander("About this app"):
st.write(f"""
This Chatbot app allows users to interact with various models including the new LLM models.
💡 For decent answers, you'd want to increase the `Max Tokens` value from `100` to `500`.
""")
if "api_key" not in st.session_state:
st.session_state.api_key = ""
with st.sidebar:
max_tokens = st.slider('Max Tokens', 10, 500, 100)
top_p = st.slider('Top P', 0.0, 1.0, 0.5, 0.05)
if max_tokens > 100:
user_provided_api_key = st.text_input("👇 Your DeepInfra API Key", value=st.session_state.api_key, type='password')
if user_provided_api_key:
st.session_state.api_key = user_provided_api_key
if not st.session_state.api_key:
st.warning("❄️ If you want to try this app with more than `100` tokens, you must provide your own DeepInfra API key. Get yours here → https://deepinfra.com/dash/api_keys")
if max_tokens <= 100 or st.session_state.api_key:
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response, error = get_response(st.session_state.api_key, MODEL_CODELLAMA, prompt, max_tokens, top_p)
if error:
st.error(f"Error: {error}")
else:
placeholder = st.empty()
placeholder.markdown(response)
message = {"role": "assistant", "content": response}
st.session_state.messages.append(message)
# Clear chat history function and button
def clear_chat_history():
st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
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