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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
st.set_page_config(page_icon="💬", layout="wide", page_title="Groq Chat Bot...")
_ = load_dotenv(find_dotenv())
GA_TRACKING_ID = os.environ['GA_TRACKING_ID']
# insert Google Analytics
st.components.v1.html(
f"""
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id={GA_TRACKING_ID}"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){{dataLayer.push(arguments);}}
gtag('js', new Date());
gtag('config', '{GA_TRACKING_ID}');
</script>
""",
height=0,
width=0,
)
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 Streamlit 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 = {
"mixtral-8x7b-32768": {
"name": "Mixtral-8x7b-Instruct-v0.1",
"tokens": 32768,
"developer": "Mistral",
},
"llama3-70b-8192": {"name": "LLaMA3-70b-chat", "tokens": 8192, "developer": "Meta"},
"llama3-8b-8192": {"name": "LLaMA3-8b-chat", "tokens": 8192, "developer": "Meta"},
"gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
}
# Layout for model selection and max_tokens slider
col1, col2 = st.columns(2)
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
)
# 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
max_tokens_range = models[model_option]["tokens"]
with col2:
# Adjust max_tokens slider dynamically based on the selected model
max_tokens = st.slider(
"Max Tokens:",
min_value=512, # Minimum value to allow some flexibility
max_value=max_tokens_range,
# Default value or max allowed if less
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}",
)
# 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)
# Fetch response from Groq API
try:
chat_completion = client.chat.completions.create(
model=model_option,
messages=[
{"role": m["role"], "content": m["content"]}
for m in st.session_state.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}
) |