app.py

import streamlit as st
from database import db  # Import Firestore client
import html
import re
import numpy as np
import torch
from transformers import pipeline
import soundfile as sf

def get_text_from_firebase(text_id):
    """Retrieve text from Firebase Firestore."""
    try:
        doc = db.collection("texts").document(text_id).get()
        if doc.exists:
            return doc.to_dict()["content"]
        else:
            return None
    except Exception as e:
        st.error(f"Error retrieving text from Firebase: {e}")
        return None

def process_text(text):
    """Clean and structure the extracted text."""
    text = re.sub(r'\n\s*\n', '\n\n', text)
    text = re.sub(r'^(\s*•\s+)(.*)$', r'<li>\2</li>', text, flags=re.MULTILINE)
    text = re.sub(r'(<li>.*</li>\n)+', r'<ul>\g<0></ul>\n', text)
    text = re.sub(r'^([A-Z][A-Z\s]+:?)\s*$', r'<h3 class="heading">\1</h3>', text, flags=re.MULTILINE)
    text = re.sub(r'(https?://\S+)', r'<a href="\1" target="_blank">\1</a>', text)

    paragraphs = text.split('\n\n')
    processed = []

    for p in paragraphs:
        p = p.strip()
        if p:
            if p.startswith('<ul>') or p.startswith('<h3'):
                processed.append(p)
            else:
                processed.append(f'<p>{p}</p>')

    return '\n'.join(processed)

def text_to_speech(text):
    """Convert text to speech using Hugging Face's SpeechBrain model."""
    try:
        tts_pipeline = pipeline("text-to-speech", model="speechbrain/tts-tacotron2-ljspeech", device=0 if torch.cuda.is_available() else -1)
        output = tts_pipeline(text)

        audio_array = np.array(output["audio"])
        sample_rate = output["sampling_rate"]

        audio_file = "output.wav"
        sf.write(audio_file, audio_array, sample_rate)
        return audio_file
    except Exception as e:
        st.error(f"Error during text-to-speech conversion: {e}")
        return None

def main():
    st.title("Accessibility-Optimized Viewer")

    # Ask user for Document ID
    text_id = st.text_input("Enter Document ID to view text:")

    if text_id:
        text = get_text_from_firebase(text_id)
        if text:
            col1, col2, col3 = st.columns(3)
            with col1:
                font_size = st.slider("Font Size", 8, 48, 16)
                line_height = st.slider("Line Height", 1.0, 2.5, 1.5)
            with col2:
                font_color = st.color_picker("Text Color", "#000000")
                bg_color = st.color_picker("Background Color", "#FFFFFF")
            with col3:
                contrast_mode = st.checkbox("High Contrast Mode")
                dyslexia_font = st.checkbox("Dyslexia-Friendly Font")

            if contrast_mode:
                font_color = "#FFFFFF"
                bg_color = "#000000"

            font_family = "Arial" if not dyslexia_font else "OpenDyslexic, sans-serif"

            custom_css = f"""
            <style>
                .content {{
                    font-size: {font_size}px;
                    color: {font_color};
                    background-color: {bg_color};
                    line-height: {line_height};
                    font-family: {font_family};
                    padding: 20px;
                    border-radius: 10px;
                    margin: 10px 0;
                    white-space: pre-wrap;
                    word-wrap: break-word;
                    hyphens: auto;
                }}

                .content p {{
                    margin: 0.8em 0;
                }}

                .content ul {{
                    margin: 0.8em 20px;
                    padding-left: 20px;
                    list-style-type: disc;
                }}

                .content h3 {{
                    font-size: 1.2em;
                    margin: 1.2em 0 0.5em;
                    padding-bottom: 3px;
                    border-bottom: 2px solid {font_color};
                }}

                .content a {{
                    color: {font_color};
                    text-decoration: underline;
                    word-break: break-all;
                }}

                @keyframes highlight {{ 0% {{background: yellow;}} 100% {{background: transparent;}} }}
                .highlight {{ animation: highlight 2s; }}
            </style>
            """
            st.markdown(custom_css, unsafe_allow_html=True)

            processed_text = process_text(html.escape(text))
            st.markdown(f'<div class="content">{processed_text}</div>', unsafe_allow_html=True)

            with st.expander("More Accessibility Options"):
                col1, col2 = st.columns(2)
                with col1:
                    letter_spacing = st.slider("Letter Spacing (px)", -1, 5, 0)
                    word_spacing = st.slider("Word Spacing (px)", 0, 10, 0)
                with col2:
                    text_align = st.selectbox("Text Alignment", ["left", "justify", "center"])
                    text_transform = st.selectbox("Text Case", ["none", "uppercase", "lowercase"])

                st.markdown(f"""
                <style>
                    .content {{
                        letter-spacing: {letter_spacing}px;
                        word-spacing: {word_spacing}px;
                        text-align: {text_align};
                        text-transform: {text_transform};
                    }}
                </style>
                """, unsafe_allow_html=True)

            # **Text-to-Speech Button**
            if st.button("Read Aloud"):
                audio_file = text_to_speech(text)
                if audio_file:
                    st.audio(audio_file, format="audio/wav")

        else:
            st.error("Text not found. Please check the ID.")

if __name__ == "__main__":
    main()