app.py

import os
from dotenv import find_dotenv, load_dotenv
import streamlit as st
from typing import Generator
from groq import Groq
import requests
from bs4 import BeautifulSoup

_ = 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("GroqChatbot", divider="rainbow", anchor=False)

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

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

models = {
    "mixtral-8x7b-32768": {"name": "Mixtral-8x7b-Instruct-v0.1", "tokens": 32768, "developer": "Mistral"},
    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
    "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
    "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
    "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
}

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 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(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

def search_web(query):
    try:
        search_url = f"https://www.google.com/search?q={query}"
        response = requests.get(search_url)
        if response.status_code == 200:
            soup = BeautifulSoup(response.text, 'html.parser')
            search_results = soup.find_all('div', class_='tF2Cxc')
            results = []
            for result in search_results:
                title = result.find('h3').text
                url = result.find('a')['href']
                snippet = result.find('span', class_='aCOpRe').text
                results.append({"title": title, "url": url, "snippet": snippet})
            return results
        else:
            return "Failed to retrieve search results"
    except Exception as e:
        return f"An error occurred: {e}"

def run_conversation(user_prompt):
    # Step 1: send the conversation and available functions to the model
    messages = [
        {
            "role": "system",
            "content": "You are a function calling LLM that uses the data extracted from the get_game_score function to answer questions around NBA game scores. Include the team and their opponent in your response."
        },
        {
            "role": "user",
            "content": user_prompt,
        }
    ]
    tools = [
        {
            "type": "internet",
            "internet": {
                "allow": ["search_web"],
                "description": "Search the web for information.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "The search query.",
                        }
                    },
                    "required": ["query"],
                },
            },
        }
    ]
    response = client.chat.completions.create(
        model=model_option,
        messages=messages,
        tools=tools,
        tool_choice="auto",  
        max_tokens=max_tokens
    )

    for chunk in response:
        if chunk.choices[0].delta.content:
            yield chunk.choices[0].delta.content

if prompt := st.text_input("Enter your prompt here..."):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("user", avatar="🕺"):  
        st.markdown(prompt)

    try:
        chat_completion = run_conversation(prompt)

        with st.chat_message("assistant", avatar="🤖"):
            chat_responses_generator = generate_chat_responses(chat_completion)
            for response in chat_responses_generator:
                st.markdown(response)
    except Exception as e:
        st.error(e, icon="🚨")