Create app.py

app.py

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+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'<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=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}
+ )