app.py

import streamlit as st
from bs4 import BeautifulSoup
import requests
from groq import Groq
import os
from dotenv import load_env
class Website:

    def __init__(self, url):
        """
        Create this Website object from the given url using the BeautifulSoup library
        """
        self.url = url
        response = requests.get(url)
        soup = BeautifulSoup(response.content, 'html.parser')
        self.title = soup.title.string if soup.title else "No title found"
        for irrelevant in soup.body(["script", "style", "img", "input"]):
            irrelevant.decompose()
        self.text = soup.body.get_text(separator="\n", strip=True)

# Initialize Groq client
api_key = os.getenv('GROQ_API_KEY')
client = Groq(api_key=api_key)

# Streamlit UI
st.title("Welcome to WebBot")
st.write("Enter a website URL and ask questions about its content!")

# Input fields
url = st.text_input("Website URL:", "https://example.com  or .ac.ke " )
user_query = st.text_area("What would you like to know about this website")

if st.button("Submit"):
    # Scrape website content
    with st.spinner("Scraping website..."):
        website = Website(url)
    
    if "Error" in website.title:
        st.error("Failed to load the website. Please check the URL.")
    else:
        st.success("Website loaded successfully!")
        st.write(f"**Website Title:** {website.title}")

        # Call Groq API for processing
        st.write("Querying the website...")
        with st.spinner("Processing your query..."):
            try:
                chat_streaming = client.chat.completions.create(
                    messages=[
                        {"role": "system", "content": "You are a helpful assistant specializing in extracting and analyzing website content. Answer questions based on the provided website's content. Ensure responses are clear, concise, and formatted in Markdown for better readability."},
                        {"role": "user", "content": f"{user_query} Here's the content:\n{website.text}"}
                    ],
                    model="llama3-groq-70b-8192-tool-use-preview",
                    temperature=0.3,
                    max_tokens=1200,
                    top_p=1,
                    stream=True,
                )
                st.write('Passed model')

            except Exception as e:
                st.error(f"Failed to process query: {e}")
            response = ""
            try:
                for chunk in chat_streaming:
                    content = chunk.choices[0].delta.content
                    if content:  # Ensure content is not None
                        response += content
                st.write("### Response:")
                st.write(response)
            except Exception as e:
                st.error(f"Failed to process query: {e}")