app.py

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="💬", 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("📣")

st.subheader("Groq Chat Streamlit App", divider="rainbow", anchor=False)

client = Groq(
    api_key=os.environ['GROQ_API_KEY'],
)

def get_tool_owner_info():
    owner_info = {
        "date_time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    }
    return json.dumps(owner_info)

def run_conversation(user_prompt, messages):
    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=max_tokens
    )

    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


# 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}
        )