import streamlit as st
import requests
from bs4 import BeautifulSoup
import trafilatura
from smolagents import create_agent

# Streamlit UI
def main():
    st.set_page_config(page_title="AI Documentation Assistant", layout="wide")
    st.title("📖 AI Documentation Assistant")
    
    st.write("Enter the top-level URL of your documentation, and I'll find the most relevant article to answer your question.")
    
    # User input
    doc_url = st.text_input("🔗 Documentation URL (Homepage)", "https://example.com/docs")
    user_question = st.text_area("❓ Your Question", "How do I reset my password?")
    
    if st.button("🔍 Find Answer"):
        with st.spinner("Searching for relevant information..."):
            article_url, extracted_text = find_relevant_article(doc_url, user_question)
            if article_url:
                answer = generate_answer(user_question, extracted_text)
                
                st.success("✅ Answer Found!")
                st.write(answer)
                st.write(f"[🔗 Read Full Article]({article_url})")
            else:
                st.error("⚠️ No relevant articles found.")

# Step 3 & 4: Crawling and Finding the Most Relevant Article
def find_relevant_article(base_url, question):
    """Crawls the top-domain docs, finds the most relevant article, and extracts text."""
    response = requests.get(base_url)
    if response.status_code != 200:
        return None, None
    
    soup = BeautifulSoup(response.text, "html.parser")
    links = [a['href'] for a in soup.find_all('a', href=True) if base_url in a['href']]
    
    best_match = None
    best_text = ""
    
    for link in links[:10]:  # Limit to first 10 links for now
        page_text = trafilatura.extract(requests.get(link).text)
        if page_text and question.lower() in page_text.lower():
            best_match = link
            best_text = page_text
            break  # Stop at first good match
    
    return best_match, best_text

# Step 5: Generate Answer using `smolagents`
def generate_answer(question, context):
    """Defines an AI agent to generate answers based on documentation context."""
    
    def answer_logic(state):
        """Agent logic to answer based on context."""
        return f"Based on the documentation, here is my answer: {state['context'][:500]}..."  # Truncating for brevity

    # Create the agent
    agent = create_agent(
        name="QA_Agent",
        description="Answers questions based on documentation content.",
        process=answer_logic,
    )

    # Run the agent
    response = agent({"context": context, "question": question})
    return response

if __name__ == "__main__":
    main()