import streamlit as st
from PyPDF2 import PdfReader
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# App configuration
st.set_page_config(page_title="PDF Chatbot", layout="wide")
st.markdown(
    """
    <style>
    body {
        background: linear-gradient(90deg, rgba(255,224,230,1) 0%, rgba(224,255,255,1) 50%, rgba(224,240,255,1) 100%);
        color: #000;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# Title and "Created by" section
st.markdown("<h1 style='text-align: center; color: #FF69B4;'>📄 PDF RAG Chatbot</h1>", unsafe_allow_html=True)
st.markdown(
    "<h4 style='text-align: center;'>Created by: <a href='https://www.linkedin.com/in/datascientisthameshraj/' style='color:#FF4500;'>Engr. Hamesh Raj</a></h4>",
    unsafe_allow_html=True
)

# Sidebar for PDF file upload
uploaded_files = st.sidebar.file_uploader("Upload PDF files", type="pdf", accept_multiple_files=True)

# Query input
query = st.text_input("Ask a question about the uploaded PDFs:")

# Initialize session state to store conversation
if "conversation" not in st.session_state:
    st.session_state.conversation = []

# Function to extract text from PDFs
def extract_text_from_pdfs(files):
    pdf_text = ""
    for file in files:
        reader = PdfReader(file)
        for page_num in range(len(reader.pages)):
            page = reader.pages[page_num]
            pdf_text += page.extract_text() + "\n"
    return pdf_text

# Load model and tokenizer
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG")
    model = AutoModelForCausalLM.from_pretrained(
        "himmeow/vi-gemma-2b-RAG",
        device_map="auto",
        torch_dtype=torch.bfloat16
    )
    if torch.cuda.is_available():
        model.to("cuda")
    return tokenizer, model

# Process and respond to user query
if st.button("Submit"):
    if uploaded_files and query:
        pdf_text = extract_text_from_pdfs(uploaded_files)
        tokenizer, model = load_model()

        prompt = """
        ### Instruction and Input:
        Based on the following context/document:
        {}
        Please answer the question: {}

        ### Response:
        {}
        """

        input_text = prompt.format(pdf_text, query, " ")
        input_ids = tokenizer(input_text, return_tensors="pt")

        if torch.cuda.is_available():
            input_ids = input_ids.to("cuda")

        outputs = model.generate(
            **input_ids,
            max_new_tokens=500,
            no_repeat_ngram_size=5,
        )

        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

        # Store the conversation
        st.session_state.conversation.insert(0, {"question": query, "answer": answer})

# Display conversation
if st.session_state.conversation:
    st.markdown("## Previous Conversations")
    for qa in st.session_state.conversation:
        st.markdown(f"**Q: {qa['question']}**")
        st.markdown(f"**A: {qa['answer']}**")
        st.markdown("---")