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'],
)

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 = "🤖" if message["role"] == "assistant" else "🕺"
    with st.chat_message(message["role"], avatar=avatar):
        st.markdown(message["content"])

def generate_chat_responses(user_prompt):
    """Fetches response from the Groq API using the run_conversation function."""
    response = run_conversation(user_prompt)
    yield response  # Yield the response content

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": {},
            },
        }
    ]
    try:
        response = client.chat.completions.create(
            model=model_option,
            messages=messages,
            tools=tools,
            tool_choice="auto",
            max_tokens=4096
        )

        response_message = response.choices[0].delta
        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].delta.content
        else:
            return response_message.content
    except Exception as e:
        st.error(e, icon="🚨")
        return None

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=""):
        st.markdown(prompt)

    try:
        # Use generate_chat_responses with user prompt
        with st.chat_message("assistant", avatar=""):
            chat_responses_generator = generate_chat_responses(prompt)
            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}
        )