import streamlit as st
import os
import tempfile
import fitz  # PyMuPDF for PDFs
import docx
import openpyxl
import faiss

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
from langchain.llms import Groq
from langchain.chains import RetrievalQA

# Load LLM (API key from Hugging Face secrets)
llm = Groq(
    model="llama3-8b-8192",
    api_key=os.getenv("GROQ_API_KEY")
)

# Embeddings model
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# File readers
def read_pdf(file_path):
    text = ""
    doc = fitz.open(file_path)
    for page in doc:
        text += page.get_text()
    return text

def read_docx(file_path):
    doc = docx.Document(file_path)
    return "\n".join([p.text for p in doc.paragraphs])

def read_excel(file_path):
    wb = openpyxl.load_workbook(file_path, data_only=True)
    text = ""
    for sheet in wb.sheetnames:
        ws = wb[sheet]
        for row in ws.iter_rows(values_only=True):
            text += " ".join([str(cell) for cell in row if cell is not None]) + "\n"
    return text

def process_file(uploaded_file):
    suffix = uploaded_file.name.split(".")[-1]
    with tempfile.NamedTemporaryFile(delete=False, suffix="." + suffix) as tmp_file:
        tmp_file.write(uploaded_file.read())
        tmp_path = tmp_file.name

    if suffix.lower() == "pdf":
        return read_pdf(tmp_path)
    elif suffix.lower() == "docx":
        return read_docx(tmp_path)
    elif suffix.lower() == "xlsx":
        return read_excel(tmp_path)
    else:
        return "Unsupported file type."

# Streamlit App
st.set_page_config(page_title="DocuQuery AI", layout="centered")
st.title("📄 DocuQuery AI")
st.markdown("Upload a document (PDF, Word, or Excel) and ask questions about its content using LLaMA3.")

uploaded_file = st.file_uploader("Upload your document", type=["pdf", "docx", "xlsx"])

if uploaded_file:
    st.success("✅ File uploaded successfully.")
    with st.spinner("Reading and processing file..."):
        raw_text = process_file(uploaded_file)

    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    docs = [Document(page_content=chunk) for chunk in splitter.split_text(raw_text)]

    with st.spinner("Indexing document with FAISS..."):
        db = FAISS.from_documents(docs, embedding_model)
        retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})
        qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)

    st.success("📚 Document indexed. Ask your question below!")

    user_query = st.text_input("❓ Ask something about the document:")
    if user_query:
        with st.spinner("Generating answer..."):
            response = qa_chain.run(user_query)
            st.markdown(f"**💬 Answer:** {response}")