app.py

import streamlit as st
import base64
import os
import random
from PyPDF2 import PdfReader
import threading
import time
import hashlib
from datetime import datetime
import json
import asyncio
import edge_tts

# Patch asyncio for nested event loops
import nest_asyncio
nest_asyncio.apply()

# Character definitions with emojis
CHARACTERS = {
    "Aria": {"emoji": "🌸", "voice": "en-US-AriaNeural"},
    "Jenny": {"emoji": "🎶", "voice": "en-US-JennyNeural"},
    "Sonia": {"emoji": "🌺", "voice": "en-GB-SoniaNeural"},
    "Natasha": {"emoji": "🌌", "voice": "en-AU-NatashaNeural"},
    "Clara": {"emoji": "🌷", "voice": "en-CA-ClaraNeural"},
    "Guy": {"emoji": "🌟", "voice": "en-US-GuyNeural"},
    "Ryan": {"emoji": "🛠️", "voice": "en-GB-RyanNeural"},
    "William": {"emoji": "🎻", "voice": "en-AU-WilliamNeural"},
    "Liam": {"emoji": "🌟", "voice": "en-CA-LiamNeural"}
}

# Available English voices for Edge TTS
EDGE_TTS_VOICES = list(CHARACTERS.values())[0]["voice"]

# Initialize session state
if 'tts_voice' not in st.session_state:
    st.session_state['tts_voice'] = random.choice(list(CHARACTERS.values()))["voice"]
if 'character' not in st.session_state:
    st.session_state['character'] = random.choice(list(CHARACTERS.keys()))
if 'history' not in st.session_state:
    st.session_state['history'] = []

class AudioProcessor:
    def __init__(self):
        self.cache_dir = "audio_cache"
        self.markdown_dir = "markdown_files"
        self.log_file = "history_log.md"
        os.makedirs(self.cache_dir, exist_ok=True)
        os.makedirs(self.markdown_dir, exist_ok=True)
        self.metadata = self._load_metadata()

    def _load_metadata(self):
        metadata_file = os.path.join(self.cache_dir, "metadata.json")
        return json.load(open(metadata_file)) if os.path.exists(metadata_file) else {}

    def _save_metadata(self):
        metadata_file = os.path.join(self.cache_dir, "metadata.json")
        with open(metadata_file, 'w') as f:
            json.dump(self.metadata, f)

    def _log_action(self, action, details):
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        with open(self.log_file, 'a', encoding='utf-8') as f:
            f.write(f"[{timestamp}] {action}: {details}\n")
        st.session_state['history'].append(f"[{timestamp}] {action}: {details}")

    async def create_audio(self, text, voice, character):
        cache_key = hashlib.md5(f"{text}:{voice}".encode()).hexdigest()
        cache_path = os.path.join(self.cache_dir, f"{cache_key}.mp3")

        if cache_key in self.metadata and os.path.exists(cache_path):
            return open(cache_path, 'rb').read()

        # Clean text for speech
        text = text.replace("\n", " ").replace("</s>", " ").strip()
        if not text:
            return None

        # Generate audio with edge_tts
        communicate = edge_tts.Communicate(text, voice)
        await communicate.save(cache_path)

        # Save markdown file
        timestamp = datetime.now().strftime("%I%M %p %m%d%Y")
        title_words = ' '.join(text.split()[:10])
        filename = f"{timestamp} {character} {title_words}.md"
        filepath = os.path.join(self.markdown_dir, filename)
        with open(filepath, 'w', encoding='utf-8') as f:
            f.write(f"# {title_words}\n\n**Character:** {character}\n**Voice:** {voice}\n\n{text}")
        
        # Log action
        self._log_action("Text to Audio", f"Created audio for '{title_words}' with {character} ({voice})")

        # Update metadata
        self.metadata[cache_key] = {
            'timestamp': datetime.now().isoformat(),
            'text_length': len(text),
            'voice': voice,
            'character': character,
            'markdown_file': filename
        }
        self._save_metadata()

        return open(cache_path, 'rb').read()

def get_download_link(bin_data, filename, size_mb=None):
    b64 = base64.b64encode(bin_data).decode()
    size_str = f"({size_mb:.1f} MB)" if size_mb else ""
    return f'''
        <div class="download-container">
            <a href="data:audio/mpeg;base64,{b64}" 
               download="{filename}" class="download-link">📥 {filename}</a>
            <div class="file-info">{size_str}</div>
        </div>
    '''

def process_pdf(pdf_file, max_pages, voice, character, audio_processor):
    reader = PdfReader(pdf_file)
    total_pages = min(len(reader.pages), max_pages)
    texts, audios = [], {}

    async def process_page(i, text):
        audio_data = await audio_processor.create_audio(text, voice, character)
        audios[i] = audio_data

    # Extract text and start audio processing
    for i in range(total_pages):
        text = reader.pages[i].extract_text()
        texts.append(text)
        # Process audio in background
        threading.Thread(
            target=lambda: asyncio.run(process_page(i, text))
        ).start()

    return texts, audios, total_pages

def main():
    st.set_page_config(page_title="📚PDF 🪄Text to 🗣️Speech 🤖Transformer", page_icon="📚", layout="wide")

    # Apply styling
    st.markdown("""
        <style>
        .download-link {
            color: #1E90FF;
            text-decoration: none;
            padding: 8px 12px;
            margin: 5px;
            border: 1px solid #1E90FF;
            border-radius: 5px;
            display: inline-block;
            transition: all 0.3s ease;
        }
        .download-link:hover {
            background-color: #1E90FF;
            color: white;
        }
        .file-info {
            font-size: 0.8em;
            color: gray;
            margin-top: 4px;
        }
        </style>
    """, unsafe_allow_html=True)

    # Initialize processor
    audio_processor = AudioProcessor()

    # Sidebar settings
    st.sidebar.title(f"{CHARACTERS[st.session_state['character']]['emoji']} Character Name: {st.session_state['character']}")
    
    # Voice selection UI
    st.sidebar.markdown("### 🎤 Voice Settings")
    selected_voice = st.sidebar.selectbox(
        "👄 Select TTS Voice:",
        options=[char["voice"] for char in CHARACTERS.values()],
        index=[char["voice"] for char in CHARACTERS.values()].index(st.session_state['tts_voice']),
        key="voice_select"
    )
    selected_character = next(char for char, info in CHARACTERS.items() if info["voice"] == selected_voice)
    
    st.sidebar.markdown("""
    # 🎙️ Voice Character Agent Selector 🎭
    *Female Voices*:
    - 🌸 **Aria** – Elegant, creative storytelling  
    - 🎶 **Jenny** – Friendly, conversational  
    - 🌺 **Sonia** – Bold, confident  
    - 🌌 **Natasha** – Sophisticated, mysterious  
    - 🌷 **Clara** – Cheerful, empathetic  

    *Male Voices*:
    - 🌟 **Guy** – Authoritative, versatile  
    - 🛠️ **Ryan** – Approachable, casual  
    - 🎻 **William** – Classic, scholarly  
    - 🌟 **Liam** – Energetic, engaging
    """)

    if selected_voice != st.session_state['tts_voice'] or selected_character != st.session_state['character']:
        st.session_state['tts_voice'] = selected_voice
        st.session_state['character'] = selected_character
        audio_processor._log_action("Voice Change", f"Changed to {selected_character} ({selected_voice})")
        st.rerun()

    # Markdown file history
    st.sidebar.markdown("### 📜 History")
    md_files = [f for f in os.listdir(audio_processor.markdown_dir) if f.endswith('.md') and f != 'README.md']
    for md_file in md_files:
        col1, col2, col3 = st.sidebar.columns([3, 1, 1])
        with col1:
            if st.button(f"👁️ {md_file}", key=f"view_{md_file}"):
                with open(os.path.join(audio_processor.markdown_dir, md_file), 'r', encoding='utf-8') as f:
                    st.session_state['current_md'] = f.read()
                    audio_processor._log_action("View File", f"Viewed {md_file}")
        with col2:
            if st.button("🗑️", key=f"delete_{md_file}"):
                os.remove(os.path.join(audio_processor.markdown_dir, md_file))
                audio_processor._log_action("Delete File", f"Deleted {md_file}")
                st.rerun()
        with col3:
            st.write("")

    # History log
    st.sidebar.markdown("### 📋 Action History")
    for entry in st.session_state['history']:
        st.sidebar.write(entry)

    # Main interface
    st.markdown("<h1>📚 PDF to Audio Converter 🎧</h1>", unsafe_allow_html=True)

    # Display current markdown if selected
    if 'current_md' in st.session_state:
        st.markdown(st.session_state['current_md'])

    col1, col2 = st.columns(2)
    with col1:
        uploaded_file = st.file_uploader("Choose a PDF file", "pdf")
    with col2:
        max_pages = st.slider('Select pages to process', min_value=1, max_value=100, value=10)

    if uploaded_file:
        progress_bar = st.progress(0)
        status = st.empty()

        with st.spinner('Processing PDF...'):
            texts, audios, total_pages = process_pdf(
                uploaded_file, max_pages, 
                st.session_state['tts_voice'], 
                st.session_state['character'], 
                audio_processor
            )

            for i, text in enumerate(texts):
                with st.expander(f"Page {i+1}", expanded=i==0):
                    st.markdown(text)

                    # Wait for audio processing
                    while i not in audios:
                        time.sleep(0.1)
                    if audios[i]:
                        st.audio(audios[i], format='audio/mp3')

                # Add download link
                if audios[i]:
                    size_mb = len(audios[i]) / (1024 * 1024)
                    st.sidebar.markdown(
                        get_download_link(audios[i], f'page_{i+1}.mp3', size_mb),
                        unsafe_allow_html=True
                    )

                progress_bar.progress((i + 1) / total_pages)
                status.text(f"Processing page {i+1}/{total_pages}")

        st.success(f"✅ Successfully processed {total_pages} pages!")
        audio_processor._log_action("PDF Processed", f"Processed {uploaded_file.name} ({total_pages} pages)")

    # Text to Audio section
    st.markdown("### ✍️ Text to Audio")
    prompt = st.text_area("Enter text to convert to audio", height=200)

    if prompt:
        with st.spinner('Converting text to audio...'):
            audio_data = asyncio.run(audio_processor.create_audio(
                prompt, 
                st.session_state['tts_voice'], 
                st.session_state['character']
            ))
            if audio_data:
                st.audio(audio_data, format='audio/mp3')

                size_mb = len(audio_data) / (1024 * 1024)
                st.sidebar.markdown("### 🎵 Custom Audio")
                st.sidebar.markdown(
                    get_download_link(audio_data, 'custom_text.mp3', size_mb),
                    unsafe_allow_html=True
                )

    # Cache management
    if st.sidebar.button("Clear Cache"):
        for file in os.listdir(audio_processor.cache_dir):
            os.remove(os.path.join(audio_processor.cache_dir, file))
        audio_processor.metadata = {}
        audio_processor._save_metadata()
        audio_processor._log_action("Clear Cache", "Cleared audio cache")
        st.sidebar.success("Cache cleared successfully!")

if __name__ == "__main__":
    main()