GroqChatbot / app.py
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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
import datetime
import json
_ = load_dotenv(find_dotenv())
st.set_page_config(page_icon="๐Ÿ’ฌ", layout="wide", page_title="Groq 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("๐Ÿ“ฃ")
st.subheader("Groq Chat Streamlit App", divider="rainbow", anchor=False)
client = Groq(
api_key=os.environ['GROQ_API_KEY'],
)
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"},
}
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,
)
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
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:
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}",
)
for message in st.session_state.messages:
avatar = "๐Ÿค–" if message["role"] == "assistant" else "๐Ÿ•บ"
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 chunk.choices[0].message.tool_calls:
for tool_call in chunk.choices[0].message.tool_calls:
function_name = tool_call.function.name
if function_name == "time_date":
owner_info = get_tool_owner_info()
yield owner_info
def run_conversation(user_prompt):
messages=[
{
"role": "system",
"content": "You are a helpful assistant named ChattyBot."
},
{
"role": "user",
"content": user_prompt,
}
]
tools = [
{
"type": "function",
"function": {
"name": "time_date",
"description": "The tool will return information about the time and date to the AI.",
"parameters": {},
},
}
]
response = client.chat.completions.create(
model=model_option,
messages=messages,
tools=tools,
tool_choice="auto",
max_tokens=4096
)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls
if tool_calls:
available_functions = {
"time_date": get_tool_owner_info
}
messages.append(response_message)
for tool_call in tool_calls:
function_name = tool_call.function.name
function_to_call = available_functions[function_name]
function_args = json.loads(tool_call.function.arguments)
function_response = function_to_call(**function_args)
messages.append(
{
"tool_call_id": tool_call.id,
"role": "tool",
"name": function_name,
"content": function_response,
}
)
second_response = client.chat.completions.create(
model=model_option,
messages=messages
)
return second_response.choices[0].message.content
else:
return response_message.content
def get_tool_owner_info():
owner_info = {
"date_time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}
return json.dumps(owner_info)
if prompt := st.chat_input("Enter your prompt here..."):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user", avatar="๐Ÿ•บ"):
st.markdown(prompt)
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,
)
with st.chat_message("assistant", avatar="๐Ÿค–"):
chat_responses_generator = generate_chat_responses(chat_completion)
full_response = st.write_stream(chat_responses_generator)
except Exception as e:
st.error(e, icon="๐Ÿšจ")
if isinstance(full_response, str):
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
else:
combined_response = "\n".join(str(item) for item in full_response)
st.session_state.messages.append(
{"role": "assistant", "content": combined_response}
)