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):
    result = {"query": query, "data": {}}
    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')

            # Scrape organic search results
            result["data"]["organic"] = []
            for result in soup.find_all('div', class_='g'):
                title = result.find('a')['title']
                url = result.find('a')['href']
                snippet = result.find('span', class_='aCOpRe').text
                item = {"title": title, "url": url, "snippet": snippet}
                result["data"]["organic"].append(item)

            # Scrape knowledge panel
            result["data"]["knowledge_panel"] = {}
            if soup.find('div', id='knowledge-kp'):
                result["data"]["knowledge_panel"]["title"] = soup.find('div', id='knowledge-kp').find('h3').text
                result["data"]["knowledge_panel"]["content"] = soup.find('div', id='knowledge-kp').find('div', class_='VwiC3b').text

            # Scrape images
            result["data"]["images"] = []
            for result in soup.find_all('div', class_='hdtb-mitem hdtb-msel'):
                title = result.find('a')['title']
                url = result.find('a')['href']
                snippet = ""
                item = {"title": title, "url": url, "snippet": snippet}
                result["data"]["images"].append(item)
        else:
            result["error"] = "Failed to retrieve search results"
    except Exception as e:
        result["error"] = f"An error occurred: {e}"
    return result

full_response = None  # Initialize full_response to None

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:
        if "search for" in prompt.lower():
            query = prompt.lower().replace("search for", "").strip()
            search_results = search_web(query)
            formatted_results = "\n\n".join([f"Title: {result['title']}\nURL: {result['url']}\nSnippet: {result['snippet']}" for result in search_results])
            st.session_state.messages.append({"role": "assistant", "content": formatted_results})
            with st.chat_message("assistant", avatar="🤖"):
               full_response = formatted_results
        else:
            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="🤖"):
                chat_responses_generator = generate_chat_responses(chat_completion)
                full_response = st.write_stream(chat_responses_generator)
    except Exception as e:
        st.error(e, icon="🚨")

    # Check if full_response is defined before using it
    if full_response is not None:
        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}
            )