import streamlit as st
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin
import re
from typing import List, Tuple
from collections import deque
import time
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

def crawl(start_url: str, max_depth: int = 1, delay: float = 0.1) :
    visited = set()
    results = []
    queue = deque([(start_url, 0)])  
    crawled_urls = []  

    while queue:
        url, depth = queue.popleft()

        if depth > max_depth or url in visited:
            continue

        visited.add(url)
        crawled_urls.append(url)  

        try:
            time.sleep(delay)  
            response = requests.get(url)
            soup = BeautifulSoup(response.text, 'html.parser')

            text = soup.get_text()
            text = re.sub(r'\s+', ' ', text).strip()

            results.append((url, text))

            if depth < max_depth:
                for link in soup.find_all('a', href=True):
                    next_url = urljoin(url, link['href'])
                    if next_url.startswith('https://docs.nvidia.com/cuda/') and next_url not in visited:
                        queue.append((next_url, depth + 1))

        except Exception as e:
            print(f"Error crawling {url}: {e}")

    return results[:10], crawled_urls[:10]  

def chunk_text(text: str, max_chunk_size: int = 1000) -> List[str]:
    chunks = []
    current_chunk = ""
    
    for sentence in re.split(r'(?<=[.!?])\s+', text):
        if len(current_chunk) + len(sentence) <= max_chunk_size:
            current_chunk += sentence + " "
        else:
            chunks.append(current_chunk.strip())
            current_chunk = sentence + " "
    
    if current_chunk:
        chunks.append(current_chunk.strip())
    
    return chunks

class InMemoryStorage:
    def __init__(self):
        self.embeddings = np.array([])
        self.texts = []
        self.urls = []

    def insert(self, embeddings, texts, urls):
        if self.embeddings.size == 0:
            self.embeddings = embeddings
        else:
            self.embeddings = np.vstack((self.embeddings, embeddings))
        self.texts.extend(texts)
        self.urls.extend(urls)

    def search(self, query_embedding, top_k=5):
        if self.embeddings.size == 0:
            return []
        similarities = np.dot(self.embeddings, query_embedding)
        top_indices = np.argsort(similarities)[-top_k:][::-1]
        return [(self.texts[i], self.urls[i]) for i in top_indices]

def get_sentence_transformer():
    return SentenceTransformer('distilbert-base-nli-mean-tokens')

def insert_chunks(storage, chunks: List[str], urls: List[str]):
    model = get_sentence_transformer()
    embeddings = model.encode(chunks)
    storage.insert(embeddings, chunks, urls)

def vector_search(storage, query: str, top_k: int = 5):
    model = get_sentence_transformer()
    query_embedding = model.encode([query])[0]
    return storage.search(query_embedding, top_k)

class QuestionAnsweringSystem:
    def __init__(self):
        self.tokenizer = T5Tokenizer.from_pretrained("t5-small")
        self.model = T5ForConditionalGeneration.from_pretrained("t5-small")
        self.tokenizer.model_max_length = 1024
        self.model.config.max_length = 1024
    
    def answer_question(self, question: str, context: str) -> str:
        input_text = f"question: {question} context: {context}"
        inputs = self.tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
        
        outputs = self.model.generate(inputs.input_ids, 
                                      max_length=1024, 
                                      num_beams=4, 
                                      early_stopping=True)
        answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        return answer

def get_answer(storage, qa_system: QuestionAnsweringSystem, query: str) -> Tuple[str, str]:
    results = vector_search(storage, query)
    if not results:
        return "No relevant documents found.", ""
    context = " ".join([result[0] for result in results])
    answer = qa_system.answer_question(query, context)
    source_url = results[0][1] if results else ""
    return answer, source_url

# Streamlit UI
st.title("CUDA Documentation QA System")

storage = InMemoryStorage()
qa_system = QuestionAnsweringSystem()

# Crawling and processing the data
if st.button('Crawl CUDA Documentation'):
    with st.spinner('Crawling CUDA documentation...'):
        crawled_data, crawled_urls = crawl("https://docs.nvidia.com/cuda/", max_depth=1, delay=0.1)
        st.write(f"Processed {len(crawled_data)} pages.")
        
        for url, text in crawled_data:
            chunks = chunk_text(text, max_chunk_size=1024)
            insert_chunks(storage, chunks, [url] * len(chunks))
        st.success("Crawling and processing completed.")

# Asking questions
query = st.text_input("Enter your question about CUDA:")
if query:
    with st.spinner('Searching for an answer...'):
        answer, source_url = get_answer(storage, qa_system, query)
        st.write("**Answer:**")
        st.write(answer)
        st.write("**Source:**")
        st.write(source_url)