app.py

import streamlit as st
import tempfile
import os
import torch
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, AutoTokenizer, AutoModelForSeq2SeqLM
import librosa
import numpy as np
import ffmpeg
import time
import json
import psutil

st.set_page_config(layout="wide")

# CSS Styling
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap');

.stApp {
    background-color: #ffffff;
    font-family: 'Poppins', sans-serif;
    color: #1a1a1a;
}

/* Hide Streamlit's default elements */
[data-testid="stToolbar"], [data-testid="stDecoration"], [data-testid="stStatusWidget"] {
    display: none;
}

/* Header */
.header {
    background: #ffffff;
    padding: 1rem 2rem;
    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
    display: flex;
    justify-content: space-between;
    align-items: center;
    position: sticky;
    top: 0;
    z-index: 100;
}
.logo img {
    height: 60px;
    width: auto;
}
.navbar {
    list-style: none;
    display: flex;
    gap: 1.5rem;
    margin: 0;
}
.navbar li a {
    text-decoration: none;
    font-size: 28px;
    font-weight: bold;
    color: #060404;
    position: relative;
    padding: 10px 15px;
    transition: text-shadow 0.3s ease-in-out;
    text-shadow: 5px 5px 12px rgba(0, 0, 0, 0.5);
}
.navbar li a:hover {
    color: #ff6f61;
}

/* Hero Section */
.hero {
    background: linear-gradient(to right, #2b5876, #4e4376);
    background-size: cover;
    color: #ffffff;
    padding: 2rem 2rem;
    border-radius: 1rem;
    text-align: center;
    margin: 2rem 0;
    max-height: 200px;
}
.hero h1 {
    font-size: 2.5rem;
    font-weight: 700;
    margin-bottom: 0.5rem;
}
.hero p {
    font-size: 1.2rem;
    font-weight: 300;
}

/* Feature Section */
.feature-box {
    display: flex;
    justify-content: center;
    gap: 1.5rem;
    margin: 3rem 0;
    flex-wrap: wrap;
}
.feature {
    background: #f8f9fa;
    padding: 1.5rem;
    border-radius: 1rem;
    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
    width: 200px;
    text-align: center;
    transition: transform 0.3s ease, box-shadow 0.3s ease;
}
.feature:hover {
    transform: translateY(-8px) scale(1.03);
    box-shadow: 0 12px 24px rgba(0, 0, 0, 0.25);
    transition: all 0.3s ease;
    border: 1px solid rgba(0, 0, 0, 0.1);
    background-color: #fff;
    filter: brightness(1.05);
    z-index: 10;
}
.feature i {
    font-size: 1.5rem;
    color: #2196f3;
    margin-bottom: 0.5rem;
}

/* Plans Section */
.plans {
    padding: 3rem 2rem;
    background: #f1f4f8;
    border-radius: 1rem;
}
.plan-box {
    display: flex;
    justify-content: center;
    gap: 1.5rem;
    flex-wrap: wrap;
}
.plan {
    background: #ffffff;
    padding: 2rem;
    border-radius: 1rem;
    width: 250px;
    text-align: center;
    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
    transition: transform 0.3s ease, box-shadow 0.3s ease;
    border-top: 4px solid #28a745;
    height: 290px;
    padding-top: 10px;
}
.plan:hover {
    transform: translateY(-5px);
    box-shadow: 0 6px 15px rgba(0, 0, 0, 0.15);
}
.plan h3 {
    font-size: 1.5rem;
    margin-bottom: 0.5rem;
}
.plan.free { border-top: 4px solid #28a745; }
.plan.premium { border-top: 4px solid #ff6f61; }
.plan.business { border-top: 4px solid #2196f3; }

/* Buttons */
.stButton>button {
    background: linear-gradient(135deg, #ff6f61, #ff8a65) !important;
    color: #ffffff !important;
    font-weight: 600 !important;
    padding: 0.75rem 1.5rem !important;
    border-radius: 0.5rem !important;
    border: none !important;
    transition: transform 0.2s ease, box-shadow 0.2s ease !important;
}
.stButton>button:hover {
    transform: translateY(-2px) !important;
    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2) !important;
}

/* File Uploader */
.uploadedFile {
    border: 2px dashed #2196f3;
    border-radius: 1rem;
    padding: 2rem;
    background: #f8f9fa;
    margin: 2rem 0;
}

/* Progress Bar */
.stProgress > div > div {
    background: linear-gradient(90deg, #2196f3, #4fc3f7) !important;
}

/* Text Area */
.stTextArea textarea {
    border-radius: 0.5rem;
    border: 1px solid #e0e0e0;
    padding: 1rem;
    font-family: 'Poppins', sans-serif;
}

/* Video player styling */
video {
    display: block;
    width: 350px !important;
    height: 500px !important;
    object-fit: contain;
    margin: 0 auto;
    border: 3px solid #2196f3;
    border-radius: 8px;
}

/* Footer */
footer {
    background: #1a1a1a;
    color: #ffffff;
    padding: 3rem 2rem;
    margin-top: 3rem;
    border-radius: 1rem 1rem 0 0;
}
.footer-container {
    display: flex;
    justify-content: space-around;
    gap: 2rem;
    flex-wrap: wrap;
}
.footer-section h4 {
    font-size: 1.8rem;
    margin-bottom: 1rem;
}
.footer-section ul {
    list-style: none;
    padding: 0;
}
.footer-section ul li a {
    color: #bbbbbb;
    text-decoration: none;
    font-size: 1.6rem;
    transition: color 0.3s ease;
}
.footer-section ul li a:hover {
    color: #ff6f61;
}
.footer-bottom {
    margin-top: 2rem;
    font-size: 0.9rem;
}

/* Responsive Design */
@media (max-width: 768px) {
    .header {
        flex-direction: column;
        gap: 1rem;
    }
    .navbar {
        flex-direction: column;
        gap: 0.5rem;
    }
    .hero h1 {
        font-size: 1.8rem;
    }
    .hero p {
        font-size: 1rem;
    }
    .feature, .plan {
        width: 100%;
        max-width: 300px;
    }
}
</style>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">
""", unsafe_allow_html=True)

# Function Definitions
def format_time(seconds):
    minutes = int(seconds // 60)
    secs = int(seconds % 60)
    return f"{minutes}:{secs:02d}"

def seconds_to_srt_time(seconds):
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    millis = int((seconds - int(seconds)) * 1000)
    return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"

class TranscriptionProgress:
    def __init__(self):
        self.progress_bar = None
        self.status_text = None
    def init_progress(self):
        self.progress_bar = st.progress(0.0)
        self.status_text = st.empty()
    def update(self, progress: float, status: str):
        progress = max(0.0, min(1.0, progress))
        if self.progress_bar is not None:
            self.progress_bar.progress(progress)
        if self.status_text is not None:
            self.status_text.text(status)

@st.cache_resource
def load_model(language='en', summarizer_type='bart'):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    if language == 'ur':
        processor = AutoProcessor.from_pretrained("GogetaBlueMUI/whisper-medium-ur-fleurs-v2")
        model = AutoModelForSpeechSeq2Seq.from_pretrained("GogetaBlueMUI/whisper-medium-ur-fleurs-v2").to(device)
    else:
        processor = AutoProcessor.from_pretrained("openai/whisper-small")
        model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small").to(device)
    if device.type == "cuda":
        model = model.half()
    if summarizer_type == 'bart':
        sum_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
        sum_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn").to(device)
    else:
        sum_tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-large-book-summary")
        sum_model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-large-book-summary").to(device)
    return processor, model, sum_tokenizer, sum_model, device

def split_audio_into_chunks(audio, sr, chunk_duration):
    chunk_samples = int(chunk_duration * sr)
    chunks = [audio[start:start + chunk_samples] for start in range(0, len(audio), chunk_samples)]
    return chunks

def transcribe_audio(audio, sr, processor, model, device, start_time, language, task="transcribe"):
    inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
    input_features = inputs.input_features.to(device)
    if model.dtype == torch.float16:
        input_features = input_features.half()
    generate_kwargs = {
        "task": task,
        "language": "urdu" if language == "ur" else language,
        "max_new_tokens": 128,
        "return_timestamps": True
    }
    try:
        with torch.no_grad():
            outputs = model.generate(input_features, **generate_kwargs)
        text = processor.decode(outputs[0], skip_special_tokens=True)
        return [(text, start_time, start_time + len(audio) / sr)]
    except Exception as e:
        st.error(f"Transcription error: {str(e)}")
        return [(f"Error: {str(e)}", start_time, start_time + len(audio) / sr)]

def process_chunks(chunks, sr, processor, model, device, language, chunk_duration, task="transcribe", transcript_file="temp_transcript.json"):
    transcript = []
    chunk_start = 0
    total_chunks = len(chunks)
    progress_bar = st.progress(0)
    status_text = st.empty()
    if os.path.exists(transcript_file):
        os.remove(transcript_file)
    for i, chunk in enumerate(chunks):
        status_text.text(f"Processing chunk {i+1}/{total_chunks}...")
        try:
            memory = psutil.virtual_memory()
            st.write(f"Memory usage: {memory.percent}% (Chunk {i+1}/{total_chunks})")
            chunk_transcript = transcribe_audio(chunk, sr, processor, model, device, chunk_start, language, task)
            transcript.extend(chunk_transcript)
            with open(transcript_file, "w", encoding="utf-8") as f:
                json.dump(transcript, f, ensure_ascii=False)
            chunk_start += chunk_duration
            progress_bar.progress((i + 1) / total_chunks)
        except Exception as e:
            st.error(f"Error processing chunk {i+1}: {str(e)}")
            break
    status_text.text("Processing complete!")
    progress_bar.empty()
    return transcript

def summarize_text(text, tokenizer, model, device, summarizer_type='bart'):
    if summarizer_type == 'bart':
        max_input_length = 1024
        max_summary_length = 150
        chunk_size = 512
    else:
        max_input_length = 16384
        max_summary_length = 512
        chunk_size = 8192
    inputs = tokenizer(text, return_tensors="pt", truncation=False)
    input_ids = inputs["input_ids"].to(device)
    num_tokens = input_ids.shape[1]
    st.write(f"Number of tokens in input: {num_tokens}")
    if num_tokens < 50:
        return "Transcript too short to summarize effectively."
    try:
        summaries = []
        if num_tokens <= max_input_length:
            truncated_inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_input_length).to(device)
            with torch.no_grad():
                summary_ids = model.generate(truncated_inputs["input_ids"], num_beams=4, max_length=max_summary_length, min_length=50, early_stopping=True, temperature=0.7)
            summaries.append(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
        else:
            st.write(f"Transcript exceeds {max_input_length} tokens. Processing in chunks...")
            tokens = input_ids[0].tolist()
            for i in range(0, num_tokens, chunk_size):
                chunk_tokens = tokens[i:i + chunk_size]
                chunk_input_ids = torch.tensor([chunk_tokens]).to(device)
                with torch.no_grad():
                    summary_ids = model.generate(chunk_input_ids, num_beams=4, max_length=max_summary_length // 2, min_length=25, early_stopping=True, temperature=0.7)
                summaries.append(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
            combined_summary = " ".join(summaries)
            combined_inputs = tokenizer(combined_summary, return_tensors="pt", truncation=True, max_length=max_input_length).to(device)
            with torch.no_grad():
                final_summary_ids = model.generate(combined_inputs["input_ids"], num_beams=4, max_length=max_summary_length, min_length=50, early_stopping=True, temperature=0.7)
            summaries = [tokenizer.decode(final_summary_ids[0], skip_special_tokens=True)]
        return " ".join(summaries)
    except Exception as e:
        st.error(f"Summarization error: {str(e)}")
        return f"Error: {str(e)}"

def save_uploaded_file(uploaded_file):
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file:
            tmp_file.write(uploaded_file.read())
            return tmp_file.name
    except Exception as e:
        st.error(f"Error saving uploaded file: {str(e)}")
        return None

def merge_intervals(intervals):
    if not intervals:
        return []
    intervals.sort(key=lambda x: x[0])
    merged = [intervals[0]]
    for current in intervals[1:]:
        previous = merged[-1]
        if previous[1] >= current[0]:
            merged[-1] = (previous[0], max(previous[1], current[1]))
        else:
            merged.append(current)
    return merged

def create_edited_video(video_path, transcript, keep_indices):
    try:
        intervals_to_keep = [(transcript[i][1], transcript[i][2]) for i in keep_indices]
        merged_intervals = merge_intervals(intervals_to_keep)
        temp_files = []
        for j, (start, end) in enumerate(merged_intervals):
            temp_file = f"temp_{j}.mp4"
            ffmpeg.input(video_path, ss=start, to=end).output(temp_file, c='copy').run(overwrite_output=True, quiet=True)
            temp_files.append(temp_file)
        with open("list.txt", "w") as f:
            for temp_file in temp_files:
                f.write(f"file '{temp_file}'\n")
        edited_video_path = "edited_video.mp4"
        ffmpeg.input('list.txt', format='concat', safe=0).output(edited_video_path, c='copy').run(overwrite_output=True, quiet=True)
        for temp_file in temp_files:
            if os.path.exists(temp_file):
                os.remove(temp_file)
        if os.path.exists("list.txt"):
            os.remove("list.txt")
        return edited_video_path
    except Exception as e:
        st.error(f"Error creating edited video: {str(e)}")
        return None

def generate_srt(transcript, include_timeframe=True):
    srt_content = ""
    for text, start, end in transcript:
        if include_timeframe:
            start_time = seconds_to_srt_time(start)
            end_time = seconds_to_srt_time(end)
            srt_content += f"{start_time} --> {end_time}\n{text}\n\n"
        else:
            srt_content += f"{text}\n\n"
    return srt_content

# Main Function
def main():
    st.markdown("""
    <div class="header">
        <div class="logo">
            <img src="https://i.postimg.cc/wvFfzx5h/VIDEpp.png">
        </div>
        <ul class="navbar">
            <li><a href="#home">Home</a></li>
            <li><a href="#upload">Upload Video</a></li>
            <li><a href="#about">About Us</a></li>
            <li><a href="#contact">Contact Us</a></li>
        </ul>
    </div>
    """, unsafe_allow_html=True)

    st.markdown("""
    <div id="home" class="hero">
        <h2>VidEp – Revolutionizing Video Subtitle Editing with AI</h2>
        <p>Upload, transcribe, edit subtitles, and summarize videos effortlessly.</p>
    </div>
    """, unsafe_allow_html=True)

    # Initialize session state
    if 'app_state' not in st.session_state:
        st.session_state['app_state'] = 'upload'
    if 'video_path' not in st.session_state:
        st.session_state['video_path'] = None
    if 'primary_transcript' not in st.session_state:
        st.session_state['primary_transcript'] = None
    if 'english_transcript' not in st.session_state:
        st.session_state['english_transcript'] = None
    if 'english_summary' not in st.session_state:
        st.session_state['english_summary'] = None
    if 'language' not in st.session_state:
        st.session_state['language'] = None
    if 'language_code' not in st.session_state:
        st.session_state['language_code'] = None
    if 'translate_to_english' not in st.session_state:
        st.session_state['translate_to_english'] = False
    if 'summarizer_type' not in st.session_state:
        st.session_state['summarizer_type'] = None
    if 'summary_generated' not in st.session_state:
        st.session_state['summary_generated'] = False
    if 'current_time' not in st.session_state:
        st.session_state['current_time'] = 0
    if 'edited_video_path' not in st.session_state:
        st.session_state['edited_video_path'] = None
    if 'search_query' not in st.session_state:
        st.session_state['search_query'] = ""
    if 'show_timeframe' not in st.session_state:
        st.session_state['show_timeframe'] = True

    if st.session_state['app_state'] == 'upload':
        st.markdown("<div id='upload'></div>", unsafe_allow_html=True)
        st.markdown("<h3 style='text-align: center; color: black;'>Upload Your Video</h3>", unsafe_allow_html=True)
        with st.form(key="upload_form"):
            uploaded_file = st.file_uploader("Choose a video file", type=["mp4"], label_visibility="collapsed")
            if st.form_submit_button("Upload") and uploaded_file:
                video_path = save_uploaded_file(uploaded_file)
                if video_path:
                    st.session_state['video_path'] = video_path
                    st.session_state['app_state'] = 'processing'
                    st.write(f"Uploaded file: {uploaded_file.name}")
                    st.rerun()

    if st.session_state['app_state'] == 'processing':
        with st.form(key="processing_form"):
            language = st.selectbox("Select language", ["English", "Urdu"], key="language_select")
            language_code = "en" if language == "English" else "ur"
            st.session_state['language'] = language
            st.session_state['language_code'] = language_code
            chunk_duration = st.number_input("Duration per chunk (seconds):", min_value=1.0, step=0.1, value=10.0)
            if language_code == "ur":
                translate_to_english = st.checkbox("Generate English translation", key="translate_checkbox")
                st.session_state['translate_to_english'] = translate_to_english
            else:
                st.session_state['translate_to_english'] = False
            if st.form_submit_button("Process"):
                with st.spinner("Processing video..."):
                    start_time = time.time()
                    try:
                        st.write("Extracting audio...")
                        audio_path = "processed_audio.wav"
                        ffmpeg.input(st.session_state['video_path']).output(audio_path, ac=1, ar=16000).run(overwrite_output=True, quiet=True)
                        audio, sr = librosa.load(audio_path, sr=16000)
                        audio = np.nan_to_num(audio, nan=0.0, posinf=0.0, neginf=0.0)
                        audio_duration = len(audio) / sr
                        st.write(f"Audio duration: {audio_duration:.2f} seconds")
                        if audio_duration < 5:
                            st.error("Audio too short (< 5s). Upload a longer video.")
                            return
                        summarizer_type = 'bart' if audio_duration <= 300 else 'led'
                        st.write(f"Using summarizer: {summarizer_type}")
                        st.session_state['summarizer_type'] = summarizer_type
                        st.write("Loading models...")
                        processor, model, sum_tokenizer, sum_model, device = load_model(language_code, summarizer_type)
                        st.write("Splitting audio into chunks...")
                        chunks = split_audio_into_chunks(audio, sr, chunk_duration)
                        st.write(f"Number of chunks: {len(chunks)}")
                        st.write("Transcribing audio...")
                        primary_transcript = process_chunks(chunks, sr, processor, model, device, language_code, chunk_duration, task="transcribe", transcript_file="temp_primary_transcript.json")
                        english_transcript = None
                        if st.session_state['translate_to_english'] and language_code == "ur":
                            st.write("Translating to English...")
                            processor, model, _, _, device = load_model('en', summarizer_type)
                            english_transcript = process_chunks(chunks, sr, processor, model, device, 'ur', chunk_duration, task="translate", transcript_file="temp_english_transcript.json")
                        st.session_state.update({
                            'primary_transcript': primary_transcript,
                            'english_transcript': english_transcript,
                            'summary_generated': False,
                            'app_state': 'results'
                        })
                        st.write("Processing completed successfully!")
                        st.rerun()
                    except Exception as e:
                        st.error(f"Processing failed: {str(e)}")
                    finally:
                        if os.path.exists(audio_path):
                            os.remove(audio_path)
                        for temp_file in ["temp_primary_transcript.json", "temp_english_transcript.json"]:
                            if os.path.exists(temp_file):
                                os.remove(temp_file)

    if st.session_state['app_state'] == 'results':
        st.markdown('<div style="display: flex; justify-content: center;">', unsafe_allow_html=True)
        st.video(st.session_state['video_path'], start_time=st.session_state['current_time'])
        st.markdown('</div>', unsafe_allow_html=True)
        
        st.session_state['show_timeframe'] = st.checkbox("Show timeframe in transcript", value=st.session_state['show_timeframe'])
        st.markdown("### Search Subtitles")
        
        # Callback to handle search query updates
        def update_search_query():
            st.session_state['search_query'] = st.session_state.get('search_input', '').lower().strip()
        
        # Text input with on_change callback
        st.text_input("Search subtitles...", value=st.session_state['search_query'], key="search_input", on_change=update_search_query)

        # Primary Transcript
        st.markdown(f"### {st.session_state['language']} Transcript")
        primary_matches = 0
        for text, start, end in st.session_state['primary_transcript']:
            display_text = text.lower()  # Case-insensitive comparison
            if not st.session_state['search_query'] or st.session_state['search_query'] in display_text:
                primary_matches += 1
                label = f"[{format_time(start)} - {format_time(end)}] {text}" if st.session_state['show_timeframe'] else text
                if st.button(label, key=f"primary_{start}"):
                    st.session_state['current_time'] = start
                    st.rerun()
        if primary_matches == 0 and st.session_state['search_query']:
            st.info("No matches found in primary transcript for the search query.")

        # English Transcript
        if st.session_state['english_transcript']:
            st.markdown("### English Translation")
            english_matches = 0
            for text, start, end in st.session_state['english_transcript']:
                display_text = text.lower()  # Case-insensitive comparison
                if not st.session_state['search_query'] or st.session_state['search_query'] in display_text:
                    english_matches += 1
                    label = f"[{format_time(start)} - {format_time(end)}] {text}" if st.session_state['show_timeframe'] else text
                    if st.button(label, key=f"english_{start}"):
                        st.session_state['current_time'] = start
                        st.rerun()
            if english_matches == 0 and st.session_state['search_query']:
                st.info("No matches found in English transcript for the search query.")

        # Summary Generation
        if (st.session_state['language_code'] == 'en' or st.session_state['translate_to_english']) and not st.session_state['summary_generated']:
            if st.button("Generate Summary"):
                with st.spinner("Generating summary..."):
                    try:
                        _, _, sum_tokenizer, sum_model, device = load_model(st.session_state['language_code'], st.session_state['summarizer_type'])
                        full_text = " ".join([text for text, _, _ in (st.session_state['english_transcript'] or st.session_state['primary_transcript'])])
                        english_summary = summarize_text(full_text, sum_tokenizer, sum_model, device, st.session_state['summarizer_type'])
                        st.session_state['english_summary'] = english_summary
                        st.session_state['summary_generated'] = True
                    except Exception as e:
                        st.error(f"Summary generation failed: {str(e)}")
        if st.session_state['english_summary'] and st.session_state['summary_generated']:
            st.markdown("### Summary")
            st.write(st.session_state['english_summary'])

        # Download Subtitles
        st.markdown("### Download Subtitles")
        include_timeframe = st.checkbox("Include timeframe in subtitles", value=True)
        transcript_to_download = st.session_state['primary_transcript'] or st.session_state['english_transcript']
        if transcript_to_download:
            srt_content = generate_srt(transcript_to_download, include_timeframe)
            st.download_button(label="Download Subtitles (SRT)", data=srt_content, file_name="subtitles.srt", mime="text/plain")

        # Edit Subtitles
        st.markdown("### Edit Subtitles")
        transcript_to_edit = st.session_state['primary_transcript'] or st.session_state['english_transcript']
        if transcript_to_edit and st.button("Delete Subtitles"):
            st.session_state['app_state'] = 'editing'
            st.rerun()

    if st.session_state['app_state'] == 'editing':
        st.markdown("### Delete Subtitles")
        transcript_to_edit = st.session_state['primary_transcript'] or st.session_state['english_transcript']
        for i, (text, start, end) in enumerate(transcript_to_edit):
            st.write(f"{i}: [{format_time(start)} - {format_time(end)}] {text}")
        indices_input = st.text_input("Enter the indices of subtitles to delete (comma-separated, e.g., 0,1,3):")
        if st.button("Confirm Deletion"):
            try:
                delete_indices = [int(idx.strip()) for idx in indices_input.split(',') if idx.strip()]
                delete_indices = [idx for idx in delete_indices if 0 <= idx < len(transcript_to_edit)]
                keep_indices = [i for i in range(len(transcript_to_edit)) if i not in delete_indices]
                if not keep_indices:
                    st.error("All subtitles are deleted. No video to generate.")
                else:
                    edited_video_path = create_edited_video(st.session_state['video_path'], transcript_to_edit, keep_indices)
                    if edited_video_path:
                        st.session_state['edited_video_path'] = edited_video_path
                        st.session_state['app_state'] = 'results'
                        st.rerun()
            except ValueError:
                st.error("Invalid input. Please enter comma-separated integers.")
            except Exception as e:
                st.error(f"Error during video editing: {str(e)}")
        if st.button("Cancel Deletion"):
            st.session_state['app_state'] = 'results'
            st.rerun()

    if st.session_state['app_state'] == 'results' and st.session_state['edited_video_path']:
        st.markdown("### Edited Video")
        st.markdown('<div style="display: flex; justify-content: center;">', unsafe_allow_html=True)
        st.video(st.session_state['edited_video_path'])
        st.markdown('</div>', unsafe_allow_html=True)
        with open(st.session_state['edited_video_path'], "rb") as file:
            st.download_button(label="Download Edited Video", data=file, file_name="edited_video.mp4", mime="video/mp4")

    if st.session_state.get('video_path') and st.button("Reset"):
        if st.session_state['video_path'] and os.path.exists(st.session_state['video_path']):
            os.remove(st.session_state['video_path'])
        if st.session_state['edited_video_path'] and os.path.exists(st.session_state['edited_video_path']):
            os.remove(st.session_state['edited_video_path'])
        st.session_state.clear()
        st.rerun()

    st.markdown("""
    <div style='text-align: center;'>
        <h2 style='color: black'>Why VidEp Stands Out</h2>
    </div>
    <div class="feature-box">
        <div class="feature"><i class="fas fa-cloud-upload-alt"></i><br>Cloud Upload</div>
        <div class="feature"><i class="fas fa-search"></i><br>Smart Search</div>
        <div class="feature"><i class="fas fa-edit"></i><br>Easy Editing</div>
        <div class="feature"><i class="fas fa-file-alt"></i><br>AI Summary</div>
    </div>
    """, unsafe_allow_html=True)

    st.markdown("""
    <div id="about" class="about-section" style="padding: 3rem 2rem; background: #f8f9fa; border-radius: 1rem; margin: 2rem 0;">
        <h2 style="text-align: center; color: black; margin-bottom: 2rem;">About VidEp</h2>
        <div style="display: flex; align-items: center; gap: 2rem; flex-wrap: wrap;">
            <div style="flex: 1; min-width: 300px;">
                <img src="https://i.postimg.cc/g0z3WVgT/about.jpg" style="width: 100%; height: auto; border-radius: 1rem;" alt="About VidEp">
            </div>
            <div style="flex: 2; min-width: 300px;">
                <h3 style="color:grey;">Our Mission</h3>
                <p>VidEp aims to revolutionize how creators and professionals work with video content by providing state-of-the-art AI-powered tools for transcription, translation, and summarization.</p>
                <h3 style="color:grey;">What We Do</h3>
                <p>Our platform combines the latest advancements in speech recognition and natural language processing to automatically transcribe videos in multiple languages, generate accurate translations, and create concise summaries of content.</p>
                <h3 style="color:grey;">Why Choose Us</h3>
                <ul>
                    <li>Advanced AI models for superior accuracy</li>
                    <li>Multi-language support including English and Urdu</li>
                    <li>Easy-to-use interface for editing and managing subtitles</li>
                    <li>Smart search functionality to quickly find content</li>
                    <li>Seamless video editing based on transcripts</li>
                </ul>
            </div>
        </div>
    </div>
    """, unsafe_allow_html=True)

    st.markdown("""
    <div id="contact" class="contact-section" style="padding: 3rem 2rem; background: #f1f4f8; border-radius: 1rem; margin: 2rem 0;">
        <h2 style="text-align: center; color: black; margin-bottom: 2rem;">Contact Us</h2>
        <div style="max-width: 600px; margin: 0 auto;">
            <div style="margin-bottom: 1rem;">
                <label for="email" style="display: block; margin-bottom: 0.5rem; font-weight: 500;">Email</label>
                <input type="email" id="email" placeholder="Your email address" style="width: 100%; padding: 0.75rem; border-radius: 0.5rem; border: 1px solid #e0e0e0;">
            </div>
            <div style="margin-bottom: 1rem;">
                <label for="message" style="display: block; margin-bottom: 0.5rem; font-weight: 500;">Message</label>
                <textarea id="message" rows="5" placeholder="Your message" style="width: 100%; padding: 0.75rem; border-radius: 0.5rem; border: 1px solid #e0e0e0;"></textarea>
            </div>
            <button onclick="alert('Message sent successfully!')" style="background: linear-gradient(135deg, #ff6f61, #ff8a65); color: white; font-weight: 600; padding: 0.75rem 1.5rem; border-radius: 0.5rem; border: none; cursor: pointer; width: 100%;">Send Message</button>
        </div>
    </div>
    """, unsafe_allow_html=True)

    st.markdown("""
    <div class="plans">
        <h2 style="text-align: center; margin-bottom: 2rem; color: black;">Choose Your Plan</h2>
        <div class="plan-box">
            <div class="plan free" style="background: linear-gradient(135deg, #299f45, #185726); padding-bottom: 0px">
                <h3 style="color: white;">Free</h3>
                <p><strong>$0</strong> / month</p>
                <p>Basic video transcription</p>
                <p>English only</p>
                <p>Max 5 minutes video</p>
                <p>No summarization</p>
            </div>
            <div class="plan premium" style="background-color:#a32b2d">
                <h3 style="color: white;">Premium</h3>
                <p><strong>$19</strong> / month</p>
                <p>Advanced transcription</p>
                <p>Multiple languages</p>
                <p>Max 30 minutes video</p>
                <p>AI summarization</p>
            </div>
            <div class="plan business" style="background-color:#396ca3">
                <h3 style="color: white;">Business</h3>
                <p><strong>$49</strong> / month</p>
                <p>Enterprise-grade transcription</p>
                <p>All languages</p>
                <p>Unlimited video length</p>
            </div>
        </div>
    </div>
    """, unsafe_allow_html=True)

    st.markdown("""
    <footer>
        <div class="footer-container">
            <div class="footer-section">
                <h4 style="margin-left:20px">Company Info</h4>
                <ul>
                    <li><a href="#about-us">About Us</a></li>
                    <li><a href="#privacy">Privacy Policy</a></li>
                    <li><a href="#terms">Terms</a></li>
                </ul>
            </div>
            <div class="footer-section">
                <h4 style="margin-left:20px">Links</h4>
                <ul>
                    <li><a href="#home">Home</a></li>
                    <li><a href="#upload">Upload</a></li>
                    <li><a href="#about">About</a></li>
                    <li><a href="#contact">Contact</a></li>
                </ul>
            </div>
            <div class="footer-section">
                <h4 style="margin-left:20px">Legal</h4>
                <ul>
                    <li><a href="#">Terms of Service</a></li>
                    <li><a href="#">Privacy Policy</a></li>
                    <li><a href="#">Cookie Policy</a></li>
                </ul>
            </div>
        </div>
        <div class="footer-bottom" style="justify-content: center; text-align: center; border-top: 1px solid white; padding-top:20px; padding-bottom: 10px;">
            <p style="font-size: 20px">© 2025 VidEp. All rights reserved.</p>
        </div>
    </footer>

    <script>
    document.addEventListener('DOMContentLoaded', function() {
        const navLinks = document.querySelectorAll('.navbar a');
        navLinks.forEach(link => {
            link.addEventListener('click', function(e) {
                e.preventDefault();
                const targetId = this.getAttribute('href');
                const targetElement = document.querySelector(targetId);
                if (targetElement) {
                    targetElement.scrollIntoView({behavior: 'smooth'});
                }
            });
        });
    });
    </script>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()