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.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'
        }

    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')
            for img in soup.find_all('img', src=True):
                if 'photos' in img['src'] and 'pexels.com' 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')
            for img in soup.find_all('img', src=True):
                if 'images.unsplash.com' in img['src']:
                    urls.append(img['src'])
        except Exception as e:
            print(f"Unsplash scraping error: {e}")
        return urls

    def get_images(self, query: str, num_images: int = 15) -> List[str]:
        all_urls = []
        all_urls.extend(self.scrape_pexels(query))
        all_urls.extend(self.scrape_unsplash(query))
        
        # Remove duplicates and limit to num_images
        return list(set(all_urls))[:num_images]

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")

    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:
        """Generate voice-over 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:
            self.logger.error(f"Voice-over generation failed: {str(e)}")
            return AudioFileClip(duration=len(script.split()) * 0.3)

     def create_video(self, script: str, style: str, duration: int, output_path: str, selected_images: List[str]) -> str:
            """Create video with selected images"""
            try:
                # Progress bar
                progress_bar = st.progress(0)
                status_text = st.empty()
                
                # Generate voice-over (20%)
                status_text.text("Creating voice-over...")
                audio = self.generate_voice_over(script)
                progress_bar.progress(20)
            
                # Process selected images (40%)
                status_text.text("Processing images...")
                processed_images = []
                for img_url in selected_images:
                    response = requests.get(img_url)
                    img = Image.open(BytesIO(response.content))
                    img = img.resize((1920, 1080), Image.Resampling.LANCZOS)
                    processed_images.append(np.array(img))
                progress_bar.progress(40)
                
                # Create frames with transitions
                fps = 30
                total_frames = int(duration * fps)
                frames = []
                
                status_text.text("Generating frames...")
                frames_per_image = total_frames // len(processed_images)
                
                for idx, img in enumerate(processed_images):
                    for _ in range(frames_per_image):
                        frames.append(img)
                        
                    # Add transition frames
                    if idx < len(processed_images) - 1:
                        next_img = processed_images[idx + 1]
                        for alpha in np.linspace(0, 1, 15):
                            transition_frame = (1 - alpha) * img + alpha * next_img
                            frames.append(transition_frame.astype(np.uint8))
                
                progress_bar.progress(70)
                
                # Create video clip
                status_text.text("Compiling video...")
                video = ImageSequenceClip(frames, fps=fps)
                video = video.set_audio(audio)
                
                progress_bar.progress(90)
                
                # Write final video
                status_text.text("Saving video...")
                video.write_videofile(
                    output_path,
                    fps=fps,
                    codec='libx264',
                    audio_codec='aac',
                    threads=4,
                    preset='ultrafast'
                )
                
                progress_bar.progress(100)
                status_text.text("Video generation complete!")
                
                return output_path
                
            except Exception as e:
                self.logger.error(f"Video creation failed: {str(e)}")
                raise
    
        
    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 extract_key_topics(self, script: str) -> List[str]:
        """Extract main topics from the script for visual asset generation"""
        try:
            # Simple keyword extraction based on noun phrases
            # In a production environment, you might want to use a proper NLP library
            sentences = script.split('.')
            topics = []
            
            for sentence in sentences:
                words = sentence.strip().split()
                if len(words) >= 2:
                    # Extract potential noun phrases (pairs of words)
                    topics.append(' '.join(words[:2]))
            
            # Remove duplicates and limit to top 5 topics
            return list(dict.fromkeys(topics))[:5]
            
        except Exception as e:
            self.logger.error(f"Topic extraction failed: {str(e)}")
            return ["default topic"]

    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.title("Enhanced Video Generator")
        
        # Step 1: Input prompt
        prompt = st.text_input("Enter your video topic/prompt")
        
        if prompt:
            # Step 2: Image Selection
            st.subheader("Select Images for Your Video")
            images = self.generator.image_scraper.get_images(prompt)
            
            if not images:
                st.warning("No images found. Try a different search term.")
                return
                
            selected_images = []
            cols = st.columns(3)
            for idx, img_url in enumerate(images):
                with cols[idx % 3]:
                    try:
                        response = requests.get(img_url)
                        img = Image.open(BytesIO(response.content))
                        st.image(img, use_column_width=True)
                        if st.checkbox(f"Select Image {idx + 1}", key=f"img_{idx}"):
                            selected_images.append(img_url)
                    except:
                        continue
            
            # Step 3: Video Generation (only show if images are selected)
            if selected_images:
                st.subheader("Video Generation Settings")
                col1, col2 = st.columns(2)
                with col1:
                    style = st.selectbox("Choose style", options=list(self.generator.themes.keys()))
                with col2:
                    duration = st.slider("Video duration (seconds)", 10, 300, 60, 10)

                if st.button("Generate Video"):
                    try:
                        output_path = os.path.join(os.getcwd(), f"generated_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!")
                            st.video(video_path)
                            
                            with open(video_path, 'rb') as video_file:
                                st.download_button(
                                    "Download Video",
                                    video_file.read(),
                                    file_name=os.path.basename(video_path),
                                    mime="video/mp4"
                                )
                    except Exception as e:
                        st.error(f"Failed to generate video: {str(e)}")

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