import torch
import gradio as gr
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
import os

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def generate_video(prompt):
    # load pipeline
    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16,
                                             variant="fp16")
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

    # optimize for GPU memory
    pipe.enable_model_cpu_offload()
    pipe.enable_vae_slicing()

    # generate
    video_frames = pipe(prompt, num_inference_steps=25, num_frames=200).frames

    # get absolute path to current working directory
    current_directory = os.getcwd()

    # create directory to store video
    video_directory = os.path.join(current_directory, "generated_videos")
    os.makedirs(video_directory, exist_ok=True)

    # convert to video
    video_path = export_to_video(video_frames, os.path.join(video_directory, "generated_video.mp4"))
    return video_path


demo = gr.Interface(fn=generate_video, inputs="text", outputs="video")
demo.launch(share=True)