import streamlit as st from typing import Generator from groq import Groq st.set_page_config(page_icon="💬", layout="wide", page_title="Groq Goes Brrrrrrrr...") def icon(emoji: str): """Shows an emoji as a Notion-style page icon.""" st.write( f'{emoji}', unsafe_allow_html=True, ) icon("🏎️") st.subheader("Groq Chat Streamlit App", divider="rainbow", anchor=False) client = Groq( api_key=st.secrets["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", }, "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "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} )