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import os
from dotenv import find_dotenv, load_dotenv
import streamlit as st
from typing import Generator
from groq import Groq
_ = load_dotenv(find_dotenv())
st.set_page_config(page_icon="📃", layout="wide", page_title="Groq & LLaMA3 Chat Bot...")
def icon(emoji: str):
"""Shows an emoji as a Notion-style page icon."""
st.write(
f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
unsafe_allow_html=True,
)
# icon("⚡️")
st.subheader("Groq Chat with LLaMA3 App", divider="rainbow", anchor=False)
client = Groq(
api_key=os.environ['GROQ_API_KEY'],
)
# Initialize chat history and selected model
if "messages" not in st.session_state:
st.session_state.messages = []
if "selected_model" not in st.session_state:
st.session_state.selected_model = None
# Define model details
models = {
"llama3-70b-8192": {"name": "LLaMA3-70b", "tokens": 8192, "developer": "Meta"},
"llama3-8b-8192": {"name": "LLaMA3-8b", "tokens": 8192, "developer": "Meta"},
"llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
"gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
"mixtral-8x7b-32768": {
"name": "Mixtral-8x7b-Instruct-v0.1",
"tokens": 32768,
"developer": "Mistral",
},
}
# Layout for model selection and max_tokens slider
col1, col2 = st.columns([1, 3]) # Adjust the ratio to make the first column smaller
with col1:
model_option = st.selectbox(
"Choose a model:",
options=list(models.keys()),
format_func=lambda x: models[x]["name"],
index=0, # Default to the first model in the list
)
max_tokens_range = models[model_option]["tokens"]
max_tokens = st.slider(
"Max Tokens:",
min_value=512,
max_value=max_tokens_range,
value=min(32768, max_tokens_range),
step=512,
help=f"Adjust the maximum number of tokens (words) for the model's response. Max for selected model: {max_tokens_range}",
)
system_message = {}
if system_prompt := st.text_input("System Prompt"):
system_message = {"role": "system", "content": system_prompt}
# Detect model change and clear chat history if model has changed
if st.session_state.selected_model != model_option:
st.session_state.messages = []
st.session_state.selected_model = model_option
# Add a "Clear Chat" button
if st.button("Clear Chat"):
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
avatar = "🤖" if message["role"] == "assistant" else "😀"
with st.chat_message(message["role"], avatar=avatar):
st.markdown(message["content"])
def generate_chat_responses(chat_completion) -> Generator[str, None, None]:
"""Yield chat response content from the Groq API response."""
for chunk in chat_completion:
if chunk.choices[0].delta.content:
yield chunk.choices[0].delta.content
if prompt := st.chat_input("Enter your prompt here..."):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user", avatar="😀"):
st.markdown(prompt)
messages=[
{"role": m["role"], "content": m["content"]}
for m in st.session_state.messages]
if system_message:
messages.insert(0,system_message)
# Fetch response from Groq API
try:
chat_completion = client.chat.completions.create(
model=model_option,
messages=messages,
max_tokens=max_tokens,
stream=True,
)
# Use the generator function with st.write_stream
with st.chat_message("assistant", avatar="🤖"):
chat_responses_generator = generate_chat_responses(chat_completion)
full_response = st.write_stream(chat_responses_generator)
except Exception as e:
st.error(e, icon="❌")
# Append the full response to session_state.messages
if isinstance(full_response, str):
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
else:
# Handle the case where full_response is not a string
combined_response = "\n".join(str(item) for item in full_response)
st.session_state.messages.append(
{"role": "assistant", "content": combined_response}
) |