import streamlit as st
from pdf_extractor import get_pdf_text
from knowledge_base import create_faiss_index, search_faiss
from chatbot import generate_response
try:
    import fitz  # PyMuPDF
    print("✅ PyMuPDF (fitz) is installed successfully!")
except ImportError as e:
    print(f"❌ ImportError: {e}")

st.title("المساعد الرقمي")

# Step 1: Upload a PDF
uploaded_file = st.file_uploader("📂 Upload a PDF", type=["pdf"])
if uploaded_file:
    with open("temp.pdf", "wb") as f:
        f.write(uploaded_file.getbuffer())
    
    st.success("✅ PDF uploaded successfully!")
    
    # Step 2: Extract text from PDF
    st.info("🔄 Extracting text from PDF...")
    pdf_text = get_pdf_text("temp.pdf")  # Load the PDF and extract text
    texts = pdf_text.split("\n")  # Split text into paragraphs
    faiss_index, stored_texts = create_faiss_index(texts)  # Store in FAISS
    
    st.success("✅ Knowledge base created!")

    # Step 3: Chat Interface
    st.header("💬 Chat with Your PDF")
    user_query = st.text_input("Ask a question:")
    if user_query:
        st.info("🔍 Searching for relevant information...")
        relevant_texts = search_faiss(faiss_index, stored_texts, user_query, top_k=3)
        
        st.success("✅ Found relevant content!")
        context = "\n".join(relevant_texts)
        
        st.info("🤖 Generating answer...")
        answer = generate_response(context, user_query)
        
        st.write("**Chatbot Answer:**", answer)
        
import os  
import streamlit as st  

# Your existing Streamlit code goes here

if __name__ == "__main__":  
    port = int(os.environ.get("PORT", 7860))  # Default Hugging Face Spaces port