app.py

import streamlit as st
from pathlib import Path
import torch
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from PIL import Image, ImageDraw, ImageFont
import tempfile
import os
from moviepy.editor import *
import numpy as np
from gtts import gTTS
import textwrap
from concurrent.futures import ThreadPoolExecutor
import io
import unicodedata
import re
import requests
import random
import logging
import time
from typing import Optional, List, Dict, Tuple
from bs4 import BeautifulSoup
import requests
from io import BytesIO

class ImageScraper:
    def __init__(self):
        self.PIXABAY_API_KEY = "48069976-37e20099248207cee12385560"
        self.headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        }
        self.temp_dir = Path(tempfile.mkdtemp())
        
        # Initialize keyword extractor model
        try:
            self.keyword_model = pipeline(
                "text-classification",
                model="facebook/bart-large-mnli",
                device=0 if torch.cuda.is_available() else -1
            )
        except Exception as e:
            print(f"Failed to load keyword model: {e}")
            self.keyword_model = None

    def extract_keywords(self, text: str) -> List[Dict[str, str]]:
        """Extract relevant keywords and categories from text using AI"""
        keywords = []
        
        try:
            # Define candidate labels for classification
            candidate_labels = [
                "technology", "science", "education", "business",
                "health", "nature", "people", "urban", "abstract",
                "sports", "food", "travel", "architecture", "art",
                "music", "fashion", "medical", "industrial", "space",
                "environmental", "historical", "cultural", "professional"
            ]
            
            # Use model to classify text against each label
            if self.keyword_model:
                results = self.keyword_model(text, candidate_labels, multi_label=True)
                
                # Filter results with high confidence
                for score, label in zip(results['scores'], results['labels']):
                    if score > 0.3:  # Confidence threshold
                        keywords.append({
                            'keyword': label,
                            'confidence': score,
                            'category': self.categorize_keyword(label)
                        })
            
            # Extract additional keywords using NLP
            additional_keywords = self.extract_noun_phrases(text)
            for keyword in additional_keywords:
                keywords.append({
                    'keyword': keyword,
                    'confidence': 0.5,
                    'category': 'content_specific'
                })
            
            # Sort by confidence
            keywords = sorted(keywords, key=lambda x: x['confidence'], reverse=True)
            
            return keywords
            
        except Exception as e:
            print(f"Keyword extraction error: {e}")
            return self.get_fallback_keywords()

    def extract_noun_phrases(self, text: str) -> List[str]:
        """Extract important noun phrases from text"""
        words = text.lower().split()
        phrases = []
        
        # Common adjectives that might indicate important concepts
        adjectives = {'digital', 'smart', 'modern', 'advanced', 'innovative', 
                     'technical', 'professional', 'creative', 'strategic'}
                     
        for i in range(len(words)-1):
            if words[i] in adjectives:
                phrases.append(f"{words[i]} {words[i+1]}")
                
        return list(set(phrases))

    def categorize_keyword(self, keyword: str) -> str:
        """Categorize keyword into general themes"""
        categories = {
            'technical': {'technology', 'digital', 'software', 'computer', 'cyber'},
            'scientific': {'science', 'research', 'laboratory', 'experiment'},
            'business': {'business', 'professional', 'corporate', 'office'},
            'educational': {'education', 'learning', 'teaching', 'academic'},
            'creative': {'art', 'design', 'creative', 'innovation'},
        }
        
        for category, terms in categories.items():
            if any(term in keyword.lower() for term in terms):
                return category
        return 'general'


    def extract_key_topics(self, script: str) -> List[str]:
        """Extract key topics from a long text prompt with improved accuracy"""
        try:
            # Define relevant categories for VaultGenix
            categories = {
                'security': ['security', 'encryption', 'protection', 'privacy', 'safe', 'secure'],
                'digital': ['digital', 'online', 'virtual', 'cyber', 'electronic'],
                'legacy': ['legacy', 'inheritance', 'heir', 'posthumous', 'estate'],
                'management': ['management', 'planning', 'organization', 'control', 'administration'],
                'technology': ['AI', 'artificial intelligence', 'technology', 'platform', 'system'],
                'family': ['family', 'heir', 'custodian', 'relative', 'loved ones']
            }
            
            # Process text
            text = script.lower()
            found_topics = set()
            
            # Extract single-word matches
            words = text.split()
            for category, terms in categories.items():
                for term in terms:
                    if term in text:
                        found_topics.add(term)
                        found_topics.add(category)
            
            # Extract meaningful phrases
            important_phrases = [
                'digital legacy',
                'legacy management',
                'digital security',
                'data protection',
                'artificial intelligence',
                'digital estate',
                'digital identity',
                'secure platform',
                'family protection',
                'digital inheritance'
            ]
            
            for phrase in important_phrases:
                if phrase in text:
                    found_topics.add(phrase)
            
            # Combine related topics
            combined_topics = []
            for topic in found_topics:
                # Create meaningful combinations
                if topic in ['digital', 'secure', 'smart', 'AI']:
                    related = ['legacy', 'security', 'protection', 'management']
                    for rel in related:
                        if rel in found_topics:
                            combined_topics.append(f"{topic} {rel}")
            
            # Add combined topics to results
            found_topics.update(combined_topics)
            
            # Prioritize topics
            priority_topics = [
                topic for topic in found_topics 
                if any(key in topic for key in ['digital', 'security', 'legacy', 'AI'])
            ]
            
            # Ensure we have enough topics
            if len(priority_topics) < 3:
                priority_topics.extend(['digital security', 'legacy management', 'data protection'][:3 - len(priority_topics)])
            
            return list(set(priority_topics))[:5]  # Return top 5 unique topics
            
        except Exception as e:
            print(f"Topic extraction error: {e}")
            return ['digital security', 'legacy management', 'data protection']

    def get_images_for_keyword(self, keyword: str) -> List[Dict[str, str]]:
        """Get images for a specific keyword with improved relevance"""
        try:
            # Enhance keyword for better search results
            enhanced_keywords = {
                'digital': 'digital technology security',
                'security': 'cybersecurity protection',
                'legacy': 'digital legacy inheritance',
                'management': 'digital management system',
                'AI': 'artificial intelligence technology',
                'protection': 'data protection security'
            }
            
            search_term = enhanced_keywords.get(keyword, keyword)
            
            base_url = "https://pixabay.com/api/"
            params = {
                'key': self.PIXABAY_API_KEY,
                'q': search_term,
                'image_type': 'photo',
                'per_page': 5,
                'safesearch': True,
                'lang': 'en',
                'category': 'technology',  # Focus on technology category
                'orientation': 'horizontal'  # Better for video
            }
            
            response = requests.get(base_url, params=params, headers=self.headers)
            
            if response.status_code == 200:
                data = response.json()
                if 'hits' in data and data['hits']:
                    return [{
                        'url': img['largeImageURL'],
                        'keyword': keyword,
                        'relevance': 'Primary match' if keyword.lower() in img['tags'].lower() else 'Related',
                        'tags': img['tags']
                    } for img in data['hits']]
            return []
            
        except Exception as e:
            print(f"Error fetching images for keyword {keyword}: {e}")
            return []

    def get_pixabay_images(self, query: str) -> List[str]:
        """Get images from Pixabay API with enhanced error handling"""
        try:
            # Clean and encode the query
            clean_query = query.replace(' ', '+').strip()
            base_url = "https://pixabay.com/api/"
            
            params = {
                'key': self.PIXABAY_API_KEY,
                'q': clean_query,
                'image_type': 'photo',
                'per_page': 20,
                'safesearch': True,
                'lang': 'en'
            }
            
            response = requests.get(base_url, params=params, headers=self.headers)
            
            # Debug logging
            print(f"Pixabay API URL: {response.url}")
            print(f"Response status: {response.status_code}")
            
            if response.status_code == 200:
                data = response.json()
                print(f"Total hits: {data.get('totalHits', 0)}")
                if 'hits' in data and data['hits']:
                    image_urls = [img['largeImageURL'] for img in data['hits']]
                    print(f"Found {len(image_urls)} images")
                    return image_urls
                else:
                    print("No images found in response")
                    return self.get_stock_images()
            else:
                print(f"Pixabay API error: Status code {response.status_code}")
                return self.get_stock_images()
                
        except Exception as e:
            print(f"Exception in get_pixabay_images: {str(e)}")
            return self.get_stock_images()

    def get_stock_images(self) -> List[str]:
        """Return preset stock images as fallback"""
        return [
            "https://images.pexels.com/photos/60504/security-protection-anti-virus-software-60504.jpeg",
            "https://images.pexels.com/photos/5380642/pexels-photo-5380642.jpeg",
            "https://images.pexels.com/photos/2582937/pexels-photo-2582937.jpeg",
            "https://images.pexels.com/photos/7319074/pexels-photo-7319074.jpeg",
            "https://images.pexels.com/photos/4164418/pexels-photo-4164418.jpeg",
            "https://images.pexels.com/photos/3861969/pexels-photo-3861969.jpeg",
            "https://images.pexels.com/photos/5473298/pexels-photo-5473298.jpeg",
            "https://images.pexels.com/photos/4348401/pexels-photo-4348401.jpeg",
            "https://images.pexels.com/photos/8386440/pexels-photo-8386440.jpeg",
            "https://images.pexels.com/photos/5473950/pexels-photo-5473950.jpeg"
        ]

    def get_images(self, query: str, num_images: int = 15) -> Dict[str, List[Dict[str, str]]]:
        """Get images with AI-driven selection and ranking"""
        try:
            # Initialize result structure
            result = {
                'primary': [],
                'secondary': [],
                'general': []
            }
            
            # Extract and analyze keywords using AI
            keywords = self.extract_key_topics(query)
            print(f"AI extracted keywords: {keywords}")
            
            # Score and rank keywords based on relevance to query
            keyword_scores = self.score_keywords(query, keywords)
            ranked_keywords = sorted(keyword_scores.items(), key=lambda x: x[1], reverse=True)
            
            # Fetch and analyze images for each keyword
            all_images = []
            for keyword, score in ranked_keywords:
                images = self.get_images_for_keyword(keyword)
                for img in images:
                    img['relevance_score'] = score * self.analyze_image_relevance(img, query)
                    all_images.append(img)
            
            # Sort images by relevance score
            sorted_images = sorted(all_images, key=lambda x: x['relevance_score'], reverse=True)
            
            # Distribute images across categories
            total_images = min(len(sorted_images), num_images)
            primary_count = total_images // 2
            secondary_count = total_images // 3
            
            result['primary'] = sorted_images[:primary_count]
            result['secondary'] = sorted_images[primary_count:primary_count + secondary_count]
            result['general'] = sorted_images[primary_count + secondary_count:total_images]
            
            # If no images found, use stock images
            if not any(result.values()):
                stock_images = self.get_stock_images()
                result['general'] = [{
                    'url': url,
                    'keyword': 'technology',
                    'relevance': 'Fallback',
                    'tags': 'technology',
                    'relevance_score': 0.5
                } for url in stock_images[:num_images]]
            
            return result
            
        except Exception as e:
            print(f"Error in get_images: {str(e)}")
            return self.get_fallback_images(num_images)
    
    def score_keywords(self, query: str, keywords: List[str]) -> Dict[str, float]:
        """Score keywords based on relevance to query"""
        scores = {}
        query_words = set(query.lower().split())
        
        for keyword in keywords:
            score = 0.0
            keyword_words = set(keyword.lower().split())
            
            # Direct word match
            word_matches = len(keyword_words.intersection(query_words))
            score += word_matches * 0.3
            
            # Contextual relevance
            context_terms = {
                'digital': 0.8,
                'security': 0.7,
                'legacy': 0.9,
                'protection': 0.6,
                'management': 0.5,
                'AI': 0.8,
                'technology': 0.6
            }
            
            for term, weight in context_terms.items():
                if term in keyword.lower():
                    score += weight
            
            scores[keyword] = min(score, 1.0)  # Normalize to 0-1
        
        return scores
    
    def analyze_image_relevance(self, image: Dict[str, str], query: str) -> float:
        """Analyze image relevance based on tags and metadata"""
        score = 0.0
        
        # Analyze tags
        tags = set(image['tags'].lower().split(','))
        query_words = set(query.lower().split())
        
        # Tag matching
        matching_tags = len(tags.intersection(query_words))
        score += matching_tags * 0.2
        
        # Context relevance
        relevant_terms = {
            'technology': 0.3,
            'digital': 0.3,
            'security': 0.3,
            'business': 0.2,
            'professional': 0.2,
            'modern': 0.1
        }
        
        for term, weight in relevant_terms.items():
            if term in tags:
                score += weight
        
        return min(score, 1.0)  # Normalize to 0-1
                
                
    def get_fallback_keywords(self) -> List[Dict[str, str]]:
        """Return fallback keywords if AI extraction fails"""
        return [
            {'keyword': 'technology', 'confidence': 1.0, 'category': 'technical'},
            {'keyword': 'business', 'confidence': 0.8, 'category': 'business'},
            {'keyword': 'professional', 'confidence': 0.8, 'category': 'business'},
            {'keyword': 'digital', 'confidence': 0.7, 'category': 'technical'}
        ]

    def verify_image_url(self, url: str) -> bool:
        """Verify if an image URL is accessible"""
        try:
            response = requests.head(url, timeout=5)
            return response.status_code == 200
        except:
            return False

    def generate_fallback_audio(self, script: str) -> AudioFileClip:
        """Generate fallback audio using gTTS"""
        try:
            audio_path = self.temp_dir / "voice.mp3"
            tts = gTTS(text=script, lang='en', slow=False)
            tts.save(str(audio_path))
            return AudioFileClip(str(audio_path))
        except Exception as e:
            print(f"Fallback audio generation failed: {e}")
            duration = len(script.split()) * 0.3
            return AudioFileClip(duration=duration)

    def scrape_pexels(self, query: str) -> List[str]:
        urls = []
        try:
            url = f"https://www.pexels.com/search/{query.replace(' ', '%20')}/"
            response = requests.get(url, headers=self.headers)
            soup = BeautifulSoup(response.text, 'html.parser')
            
            # Updated selector to target image sources
            for img in soup.find_all('img', {'data-image-width': True}):
                if img.get('src') and 'photos' in img['src']:
                    urls.append(img['src'])
        except Exception as e:
            print(f"Pexels scraping error: {e}")
        return urls

    def scrape_unsplash(self, query: str) -> List[str]:
        urls = []
        try:
            url = f"https://unsplash.com/s/photos/{query.replace(' ', '-')}"
            response = requests.get(url, headers=self.headers)
            soup = BeautifulSoup(response.text, 'html.parser')
            
            # Updated selector for Unsplash
            for img in soup.find_all('img', {'srcset': True}):
                src = img.get('src')
                if src and 'images.unsplash.com' in src:
                    urls.append(src)
        except Exception as e:
            print(f"Unsplash scraping error: {e}")
        return urls

class EnhancedVideoGenerator:
    def __init__(self):
        try:
            self.setup_logging()
            self.setup_device()
            self.initialize_models()
            self.setup_workspace()
            self.load_assets()
            self.setup_themes()
            self.image_scraper = ImageScraper()
        except Exception as e:
            logging.error(f"Initialization failed: {str(e)}")
            raise RuntimeError("Failed to initialize video generator")

            self.ELEVEN_LABS_API_KEY = "sk_acdad9d2d82d504bddbe5ed4aa290ca772c106aed5b128ba"  # Replace with your key
            

    def setup_logging(self):
        """Configure logging for the application"""
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler('video_generator.log'),
                logging.StreamHandler()
            ]
        )
        self.logger = logging.getLogger(__name__)

    def setup_device(self):
        """Set up computing device (CPU/GPU)"""
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.logger.info(f"Using device: {self.device}")

    def initialize_models(self):
        """Initialize all AI models"""
        try:
            # Text generation model initialization with error handling
            try:
                self.text_generator = pipeline(
                    'text-generation',
                    model='gpt2',
                    device=0 if self.device == "cuda" else -1
                )
            except Exception as e:
                self.logger.warning(f"Text generator initialization failed: {str(e)}")
                self.text_generator = None
    
            # Skip the StableDiffusion model initialization as it requires additional setup
            self.image_model = None
            
            # Initialize stability API attribute
            self.stability_api = None
    
        except Exception as e:
            self.logger.error(f"Model initialization failed: {str(e)}")
            # Don't raise exception, allow initialization with degraded functionality
            pass

    def setup_workspace(self):
        """Set up working directory and resources"""
        self.temp_dir = Path(tempfile.mkdtemp())
        self.asset_dir = self.temp_dir / "assets"
        self.asset_dir.mkdir(exist_ok=True)

    def setup_themes(self):
        """Set up visual themes"""
        self.themes = {
            'Professional': {
                'bg': (240, 240, 240),
                'accent': (0, 120, 212),
                'text': (33, 33, 33)
            },
            'Creative': {
                'bg': (255, 250, 240),
                'accent': (255, 123, 0),
                'text': (51, 51, 51)
            },
            'Educational': {
                'bg': (248, 249, 250),
                'accent': (40, 167, 69),
                'text': (33, 37, 41)
            }
        }

    def load_assets(self):
        """Load visual assets and fonts"""
        try:
            # Try multiple font options
            font_options = [
                "arial.ttf",
                "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
                "/System/Library/Fonts/Helvetica.ttc"
            ]
            
            for font_path in font_options:
                try:
                    self.font = ImageFont.truetype(font_path, 40)
                    break
                except OSError:
                    continue
            else:
                self.font = ImageFont.load_default()
                self.logger.warning("Using default font - custom font loading failed")

        except Exception as e:
            self.logger.error(f"Asset loading failed: {str(e)}")

    def generate_visual_assets(self, script: str, style: str) -> List[Dict]:
        """Generate relevant visual assets based on script content"""
        try:
            # Extract key topics from script
            topics = self.extract_key_topics(script)
            
            assets = []
            for topic in topics:
                # Generate AI image
                image = self.generate_ai_image(topic, style)
                if image:
                    assets.append({
                        'type': 'image',
                        'data': image,
                        'topic': topic
                    })

            return assets

        except Exception as e:
            self.logger.error(f"Visual asset generation failed: {str(e)}")
            return []

    def create_enhanced_frame(
        self,
        text: str,
        theme: dict,
        frame_number: int,
        total_frames: int,
        background_image: Optional[Image.Image] = None,
        size: Tuple[int, int] = (1920, 1080)  # Upgraded to 1080p
    ) -> np.ndarray:
        """Create a visually enhanced frame with background, text, and effects"""
        try:
            # Create base frame
            if background_image:
                # Resize and crop background to fit
                bg = background_image.resize(size, Image.LANCZOS)
                frame = np.array(bg)
            else:
                frame = np.full((size[1], size[0], 3), theme['bg'], dtype=np.uint8)

            # Convert to PIL Image for drawing
            img = Image.fromarray(frame)
            draw = ImageDraw.Draw(img, 'RGBA')

            # Add subtle gradient overlay
            overlay = Image.new('RGBA', size, (0, 0, 0, 0))
            overlay_draw = ImageDraw.Draw(overlay)
            overlay_draw.rectangle(
                [0, 0, size[0], size[1]],
                fill=(255, 255, 255, 100)  # Semi-transparent white
            )
            img = Image.alpha_composite(img.convert('RGBA'), overlay)

            # Add text with improved styling
            text = self.clean_text(text)
            wrapped_text = textwrap.fill(text, width=50)
            
            # Calculate text position
            text_bbox = draw.textbbox((0, 0), wrapped_text, font=self.font)
            text_width = text_bbox[2] - text_bbox[0]
            text_height = text_bbox[3] - text_bbox[1]
            text_x = (size[0] - text_width) // 2
            text_y = size[1] - text_height - 100  # Position at bottom

            # Draw text background
            padding = 20
            draw.rectangle(
                [
                    text_x - padding,
                    text_y - padding,
                    text_x + text_width + padding,
                    text_y + text_height + padding
                ],
                fill=(0, 0, 0, 160)  # Semi-transparent black
            )

            # Draw text
            draw.text(
                (text_x, text_y),
                wrapped_text,
                fill=(255, 255, 255, 255),
                font=self.font
            )

            # Add progress bar with animation
            self.draw_animated_progress_bar(
                draw,
                frame_number,
                total_frames,
                size,
                theme
            )

            return np.array(img)

        except Exception as e:
            self.logger.error(f"Frame creation failed: {str(e)}")
            # Return fallback frame
            return np.full((size[1], size[0], 3), theme['bg'], dtype=np.uint8)

    def draw_animated_progress_bar(
        self,
        draw: ImageDraw.Draw,
        frame_number: int,
        total_frames: int,
        size: Tuple[int, int],
        theme: dict
    ):
        """Draw an animated progress bar with effects"""
        try:
            progress = frame_number / total_frames
            bar_width = int(size[0] * 0.8)  # 80% of screen width
            bar_height = 6
            x_offset = (size[0] - bar_width) // 2
            y_position = size[1] - 40

            # Draw background bar
            draw.rectangle(
                [x_offset, y_position, x_offset + bar_width, y_position + bar_height],
                fill=(200, 200, 200, 160)
            )

            # Draw progress with gradient effect
            progress_width = int(bar_width * progress)
            for x in range(progress_width):
                alpha = int(255 * (x / bar_width))  # Gradient effect
                draw.line(
                    [x_offset + x, y_position, x_offset + x, y_position + bar_height],
                    fill=(theme['accent'][0], theme['accent'][1], theme['accent'][2], alpha)
                )

            # Add animated highlight
            highlight_pos = x_offset + progress_width
            if highlight_pos < x_offset + bar_width:
                draw.rectangle(
                    [highlight_pos-2, y_position-1, highlight_pos+2, y_position + bar_height+1],
                    fill=(255, 255, 255, 200)
                )

        except Exception as e:
            self.logger.error(f"Progress bar drawing failed: {str(e)}")

    def generate_voice_over(self, script: str) -> AudioFileClip:
        try:
            # Try ElevenLabs first
            audio_path = self.temp_dir / "voice.mp3"
            
            headers = {
                "xi-api-key": self.ELEVEN_LABS_API_KEY,
                "Content-Type": "application/json"
            }
            
            data = {
                "text": script,
                "model_id": "eleven_monolingual_v1",
                "voice_settings": {
                    "stability": 0.75,
                    "similarity_boost": 0.75
                }
            }
            
            response = requests.post(
                "https://api.elevenlabs.io/v1/text-to-speech/21m00Tcm4TlvDq8ikWAM",
                headers=headers,
                json=data
            )
            
            if response.status_code == 200:
                with open(audio_path, "wb") as f:
                    f.write(response.content)
            else:
                # Fallback to Azure TTS
                speech_config = speechsdk.SpeechConfig(
                    subscription=self.AZURE_SPEECH_KEY,
                    region=self.AZURE_REGION
                )
                speech_config.speech_synthesis_voice_name = "en-US-JennyNeural"
                
                synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config)
                result = synthesizer.speak_text_async(script).get()
                
                if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
                    with open(audio_path, "wb") as f:
                        f.write(result.audio_data)
            
            return AudioFileClip(str(audio_path))
            
        except Exception as e:
            print(f"Voice generation error: {e}")
            return self.generate_fallback_audio(script)

    def generate_subtitles(self, script: str, duration: int) -> str:
        words = script.split()
        words_per_second = len(words) / duration
        subtitle_path = self.temp_dir / "subtitles.srt"
        
        with open(subtitle_path, 'w') as f:
            current_time = 0
            words_per_subtitle = int(words_per_second * 3)  # 3 seconds per subtitle
            
            for i in range(0, len(words), words_per_subtitle):
                subtitle_words = words[i:i + words_per_subtitle]
                if subtitle_words:
                    start_time = self.format_time(current_time)
                    current_time += len(subtitle_words) / words_per_second
                    end_time = self.format_time(current_time)
                    
                    f.write(f"{i//words_per_subtitle + 1}\n")
                    f.write(f"{start_time} --> {end_time}\n")
                    f.write(f"{' '.join(subtitle_words)}\n\n")
        
        return str(subtitle_path)

    @staticmethod
    def format_time(seconds: float) -> str:
        hours = int(seconds // 3600)
        minutes = int((seconds % 3600) // 60)
        secs = int(seconds % 60)
        msecs = int((seconds - int(seconds)) * 1000)
        return f"{hours:02d}:{minutes:02d}:{secs:02d},{msecs:03d}"

    def create_video(self, script: str, style: str, duration: int, output_path: str, selected_images: List[str]) -> str:
        """Create video with selected images and improved error handling"""
        try:
            # Initialize progress tracking
            progress_bar = st.progress(0)
            status_text = st.empty()
            
            # Validate inputs
            if not selected_images:
                raise ValueError("No images selected. Please select at least one image.")
            
            if not script.strip():
                raise ValueError("Script cannot be empty.")
            
            # Generate voice-over
            status_text.text("Creating voice-over...")
            audio = self.generate_fallback_audio(script)  # Using fallback audio for reliability
            progress_bar.progress(20)
            
            # Process images
            status_text.text("Processing images...")
            processed_images = []
            
            for img_url in selected_images:
                try:
                    response = requests.get(img_url, timeout=10)
                    response.raise_for_status()
                    img = Image.open(BytesIO(response.content))
                    img = img.convert('RGB')
                    img = img.resize((1920, 1080), Image.Resampling.LANCZOS)
                    processed_images.append(img)
                except Exception as e:
                    print(f"Error processing image {img_url}: {e}")
                    continue
            
            if not processed_images:
                raise ValueError("Failed to process any of the selected images.")
            
            progress_bar.progress(40)
            
            # Create frames
            status_text.text("Generating frames...")
            frames = []
            fps = 30
            total_frames = int(duration * fps)
            frames_per_image = total_frames // len(processed_images)
            
            # Convert images to numpy arrays
            image_arrays = [np.array(img) for img in processed_images]
            
            # Generate frames with transitions
            frame_count = 0
            for idx, img_array in enumerate(image_arrays):
                # Calculate how many frames this image should appear
                if idx == len(image_arrays) - 1:
                    n_frames = total_frames - frame_count
                else:
                    n_frames = min(frames_per_image, total_frames - frame_count)
                
                # Add main frames
                for _ in range(n_frames):
                    frames.append(img_array)
                    frame_count += 1
                
                # Add transition frames to next image
                if idx < len(image_arrays) - 1:
                    next_img_array = image_arrays[idx + 1]
                    transition_frames = 15  # Number of transition frames
                    for t in range(transition_frames):
                        if frame_count < total_frames:
                            alpha = t / transition_frames
                            transition_frame = cv2.addWeighted(
                                img_array, 1 - alpha,
                                next_img_array, alpha, 0
                            )
                            frames.append(transition_frame)
                            frame_count += 1
            
            progress_bar.progress(70)
            
            # Create video with frames
            status_text.text("Compiling video...")
            clip = ImageSequenceClip(frames, fps=fps)
            
            # Add audio
            audio_duration = audio.duration
            video_duration = len(frames) / fps
            
            if audio_duration > video_duration:
                audio = audio.subclip(0, video_duration)
            elif audio_duration < video_duration:
                clip = clip.subclip(0, audio_duration)
            
            final_clip = clip.set_audio(audio)
            
            # Ensure output directory exists
            output_dir = os.path.dirname(output_path)
            if output_dir and not os.path.exists(output_dir):
                os.makedirs(output_dir)
            
            progress_bar.progress(90)
            
            # Write video file
            status_text.text("Saving video...")
            try:
                final_clip.write_videofile(
                    output_path,
                    fps=fps,
                    codec='libx264',
                    audio_codec='aac',
                    ffmpeg_params=['-pix_fmt', 'yuv420p'],  # Ensure compatibility
                    verbose=False,
                    logger=None
                )
            except Exception as e:
                raise RuntimeError(f"Failed to write video file: {str(e)}")
            
            progress_bar.progress(100)
            status_text.text("Video generation complete!")
            
            return output_path
            
        except Exception as e:
            error_msg = f"Video creation failed: {str(e)}"
            print(error_msg)  # For debugging
            raise RuntimeError(error_msg)
        finally:
            # Cleanup
            try:
                if 'clip' in locals():
                    clip.close()
                if 'final_clip' in locals():
                    final_clip.close()
                if 'audio' in locals():
                    audio.close()
            except Exception as e:
                print(f"Cleanup error: {e}")
    
        
            
    def generate_visual_assets(self, script: str, style: str) -> List[Dict]:
        """Generate relevant visual assets based on script content"""
        try:
            # Simplified asset generation for faster processing
            topics = self.extract_key_topics(script)[:3]  # Limit to 3 topics
            
            assets = []
            for topic in topics:
                # Create simple colored backgrounds instead of AI images
                img = Image.new('RGB', (1920, 1080), self.themes[style]['bg'])
                assets.append({
                    'type': 'image',
                    'data': img,
                    'topic': topic
                })
    
            return assets
    
        except Exception as e:
            self.logger.error(f"Visual asset generation failed: {str(e)}")
            return []

    @staticmethod
    def clean_text(text: str) -> str:
        """Clean and normalize text for display"""
        if not isinstance(text, str):
            text = str(text)
        
        # Normalize unicode characters
        text = unicodedata.normalize('NFKD', text)
        
        # Remove non-ASCII characters
        text = text.encode('ascii', 'ignore').decode('ascii')
        
        # Replace problematic characters
        replacements = {
            '–': '-',    # en dash
            '—': '-',    # em dash
            '"': '"',    # smart quotes
            '"': '"',    # smart quotes
            ''': "'",    # smart apostrophe
            ''': "'",    # smart apostrophe
            '…': '...',  # ellipsis
        }
        for old, new in replacements.items():
            text = text.replace(old, new)
        
        # Remove any remaining non-standard characters
        text = re.sub(r'[^\x00-\x7F]+', '', text)
        
        return text.strip()

    def generate_ai_image(self, prompt: str, style: str) -> Optional[Image.Image]:
        """Generate an AI image using Stability AI"""
        try:
            if not self.stability_api:
                return None

            # Enhance prompt based on style
            style_prompts = {
                'Professional': "professional, corporate, clean, modern",
                'Creative': "artistic, vibrant, innovative, dynamic",
                'Educational': "clear, informative, academic, detailed"
            }
            
            enhanced_prompt = f"{prompt}, {style_prompts.get(style, '')}, high quality, 4k"
            
            # Generate image
            response = self.stability_api.generate(
                prompt=enhanced_prompt,
                samples=1,
                width=1920,
                height=1080
            )
            
            if response and len(response) > 0:
                image_data = response[0].image
                return Image.open(io.BytesIO(image_data))
            
            return None

        except Exception as e:
            self.logger.error(f"AI image generation failed: {str(e)}")
            return None

    def cleanup(self):
        """Clean up temporary files and resources"""
        try:
            for file in self.temp_dir.glob('*'):
                try:
                    if file.is_file():
                        file.unlink()
                    elif file.is_dir():
                        import shutil
                        shutil.rmtree(file)
                except Exception as e:
                    self.logger.warning(f"Failed to delete {file}: {str(e)}")
            
            self.temp_dir.rmdir()
            
        except Exception as e:
            self.logger.error(f"Cleanup failed: {str(e)}")

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.cleanup()

# Streamlit UI Class
class VideoGeneratorUI:
    def __init__(self):
        self.generator = EnhancedVideoGenerator()
        self.setup_ui()

    def setup_ui(self):
        st.set_page_config(layout="wide")
        
        # Custom CSS
        st.markdown("""
            <style>
            .stApp {
                max-width: 1200px;
                margin: 0 auto;
            }
            .image-category {
                margin-top: 2rem;
                padding: 1rem;
                border-radius: 0.5rem;
                background: #f8f9fa;
            }
            .image-metadata {
                font-size: 0.8rem;
                color: #666;
                margin-top: 0.5rem;
            }
            .submit-btn {
                margin-top: 1rem;
                padding: 0.5rem 1rem;
            }
            </style>
        """, unsafe_allow_html=True)

        st.title("VaultGenix Video Generator")
        st.markdown("Create professional videos for your digital legacy management platform")
        
        with st.container():
            # Add form for prompt submission
            with st.form(key='prompt_form'):
                prompt = st.text_area("Enter your video script", height=200)
                submit_button = st.form_submit_button(label='Analyze Script & Find Images')
            
            if submit_button and prompt:
                # First show AI-selected images
                with st.spinner("AI analyzing script and selecting relevant images..."):
                    try:
                        # Get AI-selected images first
                        keywords = self.generator.image_scraper.extract_key_topics(prompt)
                        st.write("🤖 AI-detected keywords:", ", ".join(keywords))
                        
                        image_categories = self.generator.image_scraper.get_images(prompt)
                        
                        # Store selections in session state
                        if 'selected_images' not in st.session_state:
                            st.session_state.selected_images = []
                        
                        if image_categories and isinstance(image_categories, dict):
                            # Display AI-selected primary matches first
                            if 'primary' in image_categories and image_categories['primary']:
                                st.subheader("🎯 AI-Selected Most Relevant Images")
                                self.display_image_grid(image_categories['primary'])
                            
                            # Display secondary matches
                            if 'secondary' in image_categories and image_categories['secondary']:
                                st.subheader("🔄 AI-Selected Related Images")
                                self.display_image_grid(image_categories['secondary'])
                            
                            # Collect selected images
                            selected_images = []
                            for category in image_categories.values():
                                if isinstance(category, list):
                                    for img in category:
                                        key = f"img_{img['url']}"
                                        if st.session_state.get(key, False):
                                            selected_images.append(img['url'])
                            
                            st.session_state.selected_images = selected_images
                            
                            # Video generation section
                            if selected_images:
                                self.show_video_settings(prompt, selected_images)
                            else:
                                st.warning("Please select at least one image to generate the video.")
                        
                        else:
                            st.warning("No images found. Please try a different prompt.")
                            
                    except Exception as e:
                        st.error(f"An error occurred: {str(e)}")
                        print(f"Error in UI: {str(e)}")
    
    def display_image_grid(self, images: List[Dict[str, str]], cols: int = 3):
        """Display images in a grid with metadata and confidence scores"""
        if not images or not isinstance(images, list):
            return
        
        n_images = len(images)
        n_rows = (n_images + cols - 1) // cols
        
        for row in range(n_rows):
            with st.container():
                columns = st.columns(cols)
                for col in range(cols):
                    idx = row * cols + col
                    if idx < n_images:
                        img = images[idx]
                        with columns[col]:
                            try:
                                st.image(img['url'], use_container_width=True)
                                
                                # Add confidence score to checkbox label
                                confidence = img.get('relevance_score', 0) * 100
                                checkbox_label = f"Select (AI Confidence: {confidence:.1f}%)"
                                
                                st.checkbox(
                                    checkbox_label,
                                    key=f"img_{img['url']}",
                                    help=f"Keywords: {img['keyword']}\nTags: {img['tags']}"
                                )
                                
                                # Show relevance metadata
                                st.markdown(
                                    f"<div class='image-metadata'>"
                                    f"<b>AI Relevance:</b> {img['relevance']}<br>"
                                    f"<b>Keywords:</b> {img['keyword']}<br>"
                                    f"<b>Match Type:</b> {img.get('category', 'General')}"
                                    f"</div>",
                                    unsafe_allow_html=True
                                )
                            except Exception as e:
                                print(f"Error displaying image: {e}")
    
    def show_video_settings(self, prompt: str, selected_images: List[str]):
        """Show video generation settings and controls"""
        st.subheader("Video Settings")
        
        col1, col2 = st.columns(2)
        with col1:
            style = st.selectbox(
                "Choose style",
                options=["Professional", "Creative", "Educational"],
                index=0
            )
        with col2:
            duration = st.slider(
                "Video duration (seconds)",
                min_value=30,
                max_value=180,
                value=60,
                step=30
            )
    
        if st.button("🎬 Generate Video", type="primary"):
            if not selected_images:
                st.error("Please select at least one image before generating the video.")
                return
                
            try:
                output_dir = "temp_videos"
                os.makedirs(output_dir, exist_ok=True)
                output_path = os.path.join(output_dir, f"vaultgenix_video_{int(time.time())}.mp4")
                
                video_path = self.generator.create_video(
                    prompt,
                    style,
                    duration,
                    output_path,
                    selected_images
                )
                
                if os.path.exists(video_path):
                    st.success("✨ Video generated successfully!")
                    
                    # Display video
                    with open(video_path, 'rb') as video_file:
                        video_bytes = video_file.read()
                        st.video(video_bytes)
                        
                        # Download button
                        st.download_button(
                            label="⬇️ Download Video",
                            data=video_bytes,
                            file_name=os.path.basename(video_path),
                            mime="video/mp4"
                        )
                else:
                    st.error("Video generation failed. Please try again.")
                    
            except Exception as e:
                st.error(f"Error generating video: {str(e)}")
                print(f"Video generation error: {str(e)}")  # For debugging
    
    def generate_video(self, prompt: str, style: str, duration: int, selected_images: List[str]):
        """Handle video generation with improved error handling"""
        if not selected_images:
            st.error("Please select at least one image before generating the video.")
            return
            
        with st.spinner("🎥 Generating your video..."):
            try:
                # Create temp directory if it doesn't exist
                temp_dir = Path("temp_videos")
                temp_dir.mkdir(exist_ok=True)
                
                # Generate unique output path
                output_path = str(temp_dir / f"vaultgenix_video_{int(time.time())}.mp4")
                
                # Generate video
                video_path = self.generator.create_video(
                    prompt,
                    style,
                    duration,
                    output_path,
                    selected_images
                )
                
                if video_path and os.path.exists(video_path):
                    st.success("✨ Video generated successfully!")
                    
                    # Display video
                    video_file = open(video_path, 'rb')
                    video_bytes = video_file.read()
                    st.video(video_bytes)
                    
                    # Download button
                    st.download_button(
                        label="⬇️ Download Video",
                        data=video_bytes,
                        file_name=os.path.basename(video_path),
                        mime="video/mp4"
                    )
                    
                    video_file.close()
                else:
                    st.error("Video generation failed. Please try again.")
                    
            except Exception as e:
                st.error(f"Failed to generate video: {str(e)}")
                print(f"Video generation error: {str(e)}")
                
            finally:
                # Cleanup temporary files
                try:
                    if 'video_file' in locals():
                        video_file.close()
                except Exception as e:
                    print(f"Cleanup error: {e}")

if __name__ == "__main__":
    ui = VideoGeneratorUI()