# ===========================
# Ultimate AI App - Final CPU Only (Zero Interaction)
# ===========================

# Required Libraries:
# pip install diffusers transformers torch TTS moviepy pillow

import torch
from diffusers import StableDiffusionPipeline
from TTS.api import TTS
from transformers import pipeline
from moviepy.editor import ImageSequenceClip
import numpy as np
from PIL import Image

# ===========================
# DEFAULT PROMPTS
# ===========================
DEFAULT_IMAGE_PROMPT = "3D, 4k, cinematic landscape, highly detailed"
DEFAULT_VOICE_TEXT = "Hello! This is a fully automatic AI generation test."
DEFAULT_TEXT_PROMPT = "Once upon a time in a futuristic world, AI created wonders."
DEFAULT_VIDEO_PROMPT = "Fantasy landscape, cinematic, 512x512"

# ===========================
# IMAGE GENERATION
# ===========================
def generate_image(prompt, save_path="generated_image.png"):
    print("Generating image...")
    pipe = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16
    )
    # Force CPU
    pipe = pipe.to("cpu")
    image = pipe(prompt).images[0]
    image.save(save_path)
    print(f"✅ Image saved as {save_path}")

# ===========================
# VOICE GENERATION
# ===========================
def generate_voice(text, save_path="generated_voice.wav"):
    print("Generating voice...")
    tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=True, gpu=False)
    tts.tts_to_file(text=text, file_path=save_path)
    print(f"✅ Voice saved as {save_path}")

# ===========================
# TEXT GENERATION
# ===========================
def generate_text(prompt, max_length=100):
    print("Generating text...")
    generator = pipeline('text-generation', model='gpt2', device=-1)  # device=-1 ensures CPU
    result = generator(prompt, max_length=max_length, num_return_sequences=1)
    with open("generated_text.txt", "w", encoding="utf-8") as f:
        f.write(result[0]['generated_text'])
    print(f"✅ Text saved as generated_text.txt")

# ===========================
# VIDEO GENERATION (CPU-Friendly)
# ===========================
def generate_video(prompt, num_frames=5, save_path="generated_video.mp4"):
    print("Generating video frames on CPU...")
    pipe = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16
    )
    pipe = pipe.to("cpu")  # Force CPU

    frames = []
    for i in range(num_frames):
        image = pipe(prompt).images[0]
        frames.append(image.convert("RGB"))

    clip = ImageSequenceClip([np.array(frame) for frame in frames], fps=2)
    clip.write_videofile(save_path)
    print(f"✅ Video saved as {save_path}")

# ===========================
# MAIN - ZERO INTERACTION
# ===========================
if __name__ == "__main__":
    print("=== Ultimate AI App - Final CPU Only ===\n")

    generate_image(DEFAULT_IMAGE_PROMPT)
    generate_voice(DEFAULT_VOICE_TEXT)
    generate_text(DEFAULT_TEXT_PROMPT)
    generate_video(DEFAULT_VIDEO_PROMPT)

    print("\n🎉 All tasks completed! All files are saved in this folder.")