import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
import streamlit as st
import numpy as np 

# Title and User Input
st.title("Text-to-Video with Streamlit")
prompt = st.text_input("Enter your text prompt:", "Spiderman is surfing")

# Button to trigger generation
if st.button("Generate Video"):  
    # Ensure you have 'accelerate' version 0.17.0 or higher 
    import accelerate 
    if accelerate.__version__ < "0.17.0":
        st.warning("Please upgrade 'accelerate' to version 0.17.0 or higher for CPU offloading.")
    else:
        with st.spinner("Generating video..."):
            # ... (Your model loading code)

            video_frames = pipe(prompt, num_inference_steps=25).frames  

            # ... (Your potential reshaping code)

            # Create dummy frames for testing 
            dummy_frames = [np.ones((256, 256, 3), dtype=np.uint8) for _ in range(20)]  

            # Replace with your actual export logic
            video_path = export_to_video(dummy_frames)

            # Display the video in the Streamlit app  
            st.video(video_path)