utils.py

import torch
from diffusers import StableDiffusionPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
import os, random
from moviepy.editor import ImageSequenceClip, AudioFileClip

# ---------- STORY GENERATION ----------
def generate_story(prompt, duration):
    model = AutoModelForCausalLM.from_pretrained(
        "mistralai/Mistral-7B-Instruct-v0.2", torch_dtype=torch.float16, device_map="auto"
    )
    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")

    text = tokenizer(
        f"Write a detailed story of {duration} minutes about: {prompt}", 
        return_tensors="pt"
    ).to("cpu")

    output = model.generate(**text, max_new_tokens=900)
    story = tokenizer.decode(output[0], skip_special_tokens=True)
    return story


# ---------- VOICE ----------
def generate_voice(text):
    from bark import generate_audio, preload_models
    preload_models()
    audio = generate_audio(text)
    path = "story_audio.wav"
    import soundfile as sf
    sf.write(path, audio, 24000)
    return path


# ---------- IMAGE GENERATION ----------
def generate_images(story):
    sd = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16
    ).to("cpu")

    scenes = story.split(".")[:10]   # create 10 images max
    paths = []

    for i, s in enumerate(scenes):
        img = sd(s).images[0]
        path = f"img_{i}.png"
        img.save(path)
        paths.append(path)

    return paths


# ---------- VIDEO ----------
def make_video(images, audio_file):
    video_clip = ImageSequenceClip(images, fps=1)  # 1 image per second
    audio_clip = AudioFileClip(audio_file)
    final = video_clip.set_audio(audio_clip)

    out = "final_story_video.mp4"
    final.write_videofile(out)
    return out