app.py

import os
import asyncio
import streamlit as st

from crawl4ai import AsyncWebCrawler
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig

from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma

# Load API keys from Space Secrets
os.environ["HUGGINGFACEHUB_API_KEY"] = st.secrets["HUGGINGFACEHUB_API_KEY"]
os.environ["HF_TOKEN"] = st.secrets["HF_TOKEN"]

async def run_pipeline(url: str, query: str):
    # 1️⃣ Crawler setup
    browser_config = BrowserConfig()
    run_config = CrawlerRunConfig()

    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(url=url, config=run_config)

        # 2️⃣ LangChain doc + split
        doc = Document(page_content=result.markdown.raw_markdown)
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
        chunks = text_splitter.split_documents([doc])

        # 3️⃣ Embedding + Chroma
        emb = HuggingFaceEmbeddings(model="avsolatorio/GIST-small-Embedding-v0")
        cb = Chroma(embedding_function=emb)

        cb.add_documents(chunks)

        # 4️⃣ Similarity search
        docs = cb.similarity_search(query, k=3)

        # 5️⃣ Llama3 via Nebius
        llama_model = HuggingFaceEndpoint(
            repo_id="meta-llama/Llama-3.1-8B-Instruct",
            provider="nebius",
            temperature=0.7,
            max_new_tokens=300,
            task="conversational"
        )

        llama = ChatHuggingFace(
            llm=llama_model,
            repo_id="meta-llama/Llama-3.1-8B-Instruct",
            provider="nebius",
            temperature=0.7,
            max_new_tokens=300,
            task="conversational"
        )

        response = llama.invoke(
            f"Context: {docs[0].page_content}\n\nQuestion: {query}"
        )
        return response.content

# Streamlit UI
st.title("🌐🔍 Ask Any Website with Llama3")
st.write("Enter a URL and your question — this app crawls the site and answers using Llama3!")

url = st.text_input("📌 Website URL", placeholder="https://www.example.com")
query = st.text_input("💬 Your Question", placeholder="What is this website about?")

if st.button("🔎 Crawl & Answer"):
    if not url.strip() or not query.strip():
        st.warning("❗ Please enter both a URL and a question.")
    else:
        with st.spinner("🕸️ Crawling website and generating answer..."):
            result = asyncio.run(run_pipeline(url, query))
            st.success(f"✅ **Answer:** {result}")