model.py

# 🔹 model.py (Complete Animation Engine with AI Integration)

import cv2
import numpy as np
import torch
import moviepy.editor as mp
from transformers import pipeline
from PIL import Image, ImageDraw
import os

class AnimationGenerator:
    def __init__(self):
        self.pose_analyzer = pipeline("text2text-generation", 
                                    model="google/pegasus-x-base")
        self.character_db = {}
        self.resolution = (720, 480)
        self.fps = 24
        
    def create_animation(self, script, character, bg, camera_effect, sound, tmp_dir):
        # Process script into animation sequences
        keyframes = self.parse_script(script, character)
        
        # Generate frames with persistent character
        frames = self.render_frames(keyframes, character, bg)
        
        # Apply cinematic effects
        frames = self.apply_camera_effects(frames, camera_effect)
        
        # Save and add audio
        video_path = os.path.join(tmp_dir, "animation.mp4")
        self.save_video(frames, video_path)
        return self.add_audio_track(video_path, sound, tmp_dir)

    def parse_script(self, script, character):
        # AI-based script analysis
        prompt = f"Convert this story into animation keyframes: {script}"
        analysis = self.pose_analyzer(prompt, max_length=400)
        return self.extract_motion_data(analysis[0]['generated_text'], character)

    def extract_motion_data(self, text, character):
        # Implement actual NLP parsing here
        return [{
            'position': (100 + i*20, 200),
            'pose': 'walking' if i%2 ==0 else 'standing',
            'expression': 'neutral'
        } for i in range(24)]  # 1 second base

    def render_frames(self, keyframes, character, bg):
        # Character memory system
        if character not in self.character_db:
            self.character_db[character] = {
                'color': (0, 0, 0),
                'scale': 1.0,
                'last_position': (100, 200)
            }
            
        frames = []
        for frame_data in keyframes:
            canvas = self.create_background(bg)
            self.draw_character(canvas, frame_data, character)
            frames.append(cv2.cvtColor(np.array(canvas), cv2.COLOR_RGB2BGR))
        return frames

    def create_background(self, bg_name):
        # Add actual background images
        if bg_name == "Dark Forest":
            return Image.new('RGB', self.resolution, (34, 139, 34))
        elif bg_name == "Haunted House":
            return Image.new('RGB', self.resolution, (28, 28, 28))
        return Image.new('RGB', self.resolution, (255, 255, 255))

    def draw_character(self, canvas, data, character):
        draw = ImageDraw.Draw(canvas)
        # Main character drawing logic
        x, y = data['position']
        # Head
        draw.ellipse((x-15, y-40, x+15, y-10), outline=(0,0,0), width=2)
        # Body
        draw.line((x, y, x, y+60), fill=(0,0,0), width=3)
        # Arms
        draw.line((x-30, y+30, x+30, y+30), fill=(0,0,0), width=3)
        # Legs
        draw.line((x-20, y+90, x, y+60), fill=(0,0,0), width=3)
        draw.line((x+20, y+90, x, y+60), fill=(0,0,0), width=3)
        
    def apply_camera_effects(self, frames, effect):
        # Professional camera effects
        if effect == "Dynamic Shake":
            return [self.apply_shake(frame) for frame in frames]
        elif effect == "Cinematic Zoom":
            return self.create_zoom_effect(frames)
        return frames

    def apply_shake(self, frame):
        dx, dy = np.random.randint(-7,7), np.random.randint(-5,5)
        M = np.float32([[1,0,dx], [0,1,dy]])
        return cv2.warpAffine(frame, M, self.resolution)

    def create_zoom_effect(self, frames):
        zoomed = []
        for i, frame in enumerate(frames):
            scale = 1.0 + (i/len(frames))*0.3
            new_frame = cv2.resize(frame, None, fx=scale, fy=scale)
            y_start = int((new_frame.shape[0] - self.resolution[1])/2)
            x_start = int((new_frame.shape[1] - self.resolution[0])/2)
            zoomed.append(new_frame[y_start:y_start+self.resolution[1], 
                                  x_start:x_start+self.resolution[0]])
        return zoomed

    def save_video(self, frames, path):
        clip = mp.ImageSequenceClip(frames, fps=self.fps)
        clip.write_videofile(path, codec='libx264', audio=False)

    def add_audio_track(self, video_path, sound, tmp_dir):
        # Implement actual audio mixing
        final_path = os.path.join(tmp_dir, "final.mp4")
        video = mp.VideoFileClip(video_path)
        video.write_videofile(final_path)
        return final_path