app.py

import streamlit as st
from summarizer import summarize_text
from pdf_extractor import extract_text_from_pdf
from text_cleaner import  clean_text
from QA_chatbot import ask_question
import base64

# Set page title and icon
st.set_page_config(
    page_title="PDF Summarizer & Q&A Chatbot",
    page_icon=":book:",  # Emoji icon or you can use an image path
    layout="centered"  # Optional: can be "centered" or "wide"
)

# Function to load and encode the logo
def get_base64_of_bin_file(bin_file):
    with open(bin_file, 'rb') as f:
        data = f.read()
    return base64.b64encode(data).decode()

# Path to the logo
logo_path = "8943377.png"
logo_base64 = get_base64_of_bin_file(logo_path)

# Custom CSS
st.markdown(f"""
    <style>
        .main {{
            background-color: #e6f0ff; /* Light Blue */
        }}
        .stTextInput > div > div > input {{
            border: 2px solid #004080; /* Dark Blue */
        }}
        .stButton>button {{
            background-color: #004080; /* Dark Blue */
            color: white;
            border-radius: 5px;
            border: 2px solid #004080; /* Dark Blue */
        }}
        .stButton>button:hover {{
            background-color: #003366; /* Darker Blue */
            border: 2px solid #003366; /* Darker Blue */
        }}
        .header {{
            display: flex;
            align-items: center;
            justify-content: space-between;
            background-color: #b3d9ff; /* Light Blue Background for header */
            padding: 10px;
            border-radius: 5px;
            box-shadow: 0 2px 5px rgba(0,0,0,0.2);
        }}
        .logo {{
            width: 100px;
        }}
    </style>
""", unsafe_allow_html=True)

# App title with logo
st.markdown(f"""
    <div class="header">
        <h2>PDF Text Summarization and Q&A Chatbot</h2>
        <img src="data:image/png;base64,{logo_base64}" class="logo">
    </div>
    <hr>
""", unsafe_allow_html=True)

# File uploader
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])

if uploaded_file is not None:
    raw_text = extract_text_from_pdf(uploaded_file)
    cleaned_text = clean_text(raw_text)
    
    # Display extracted text
    st.subheader("Extracted Text")
    st.text_area("Extracted Text", cleaned_text, height=300)
    
    # Summarization section
    if st.button("Summarize"):
        summary = summarize_text(cleaned_text)
        st.subheader("Summary")
        st.success(summary)
    
    # Q&A section
    st.subheader("Ask Questions About the PDF")
    question = st.text_input("Enter your question:")
    if question:
        answer = ask_question(question, cleaned_text)
        st.subheader("Answer")
        st.info(answer)