app.py

import streamlit as st
import torch
import imageio
import tempfile
from diffusers import DiffusionPipeline

st.set_page_config(page_title="Sora-lite 🎬", layout="centered")
st.title("🎥 Sora-lite: Text-to-Video Generator")
st.markdown("Generate mini videos from text prompts using open-source models!")

prompt = st.text_input("Enter a prompt", "a robot dancing on Mars")

if st.button("🎬 Generate Video"):
    with st.spinner("Generating video, please wait..."):

        pipe = DiffusionPipeline.from_pretrained(
            "damo-vilab/text-to-video-ms-1.7b",
            torch_dtype=torch.float16,
            variant="fp16"
        )
        pipe.to("cuda" if torch.cuda.is_available() else "cpu")

        result = pipe(prompt)
        frames = result.frames

        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
            imageio.mimsave(tmpfile.name, frames, fps=8)
            st.video(tmpfile.name)

        st.success("✅ Done!")