app.py

import os
import pathlib
import random
import shlex
import subprocess
import gradio as gr
from gradio import inputs, outputs
import torch
from huggingface_hub import snapshot_download
from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys
import boto3
from botocore.client import Config
import gradio as gr

# Downloading and setting up the model
model_dir = pathlib.Path('weights')
if not model_dir.exists():
    model_dir.mkdir()
    snapshot_download('damo-vilab/modelscope-damo-text-to-video-synthesis',
                      repo_type='model',
                      local_dir=model_dir)

s3_access_key = "juj22qxqxql7u2pl6nomgxu3ip7a"
s3_secret_key = "j3uwidtozhboy5vczhymhzkkjsaumznnqlzck5zjs5qxgsung4ukk"
s3_endpoint = "https://gateway.storjshare.io"

s3 = boto3.client("s3", aws_access_key_id=s3_access_key, aws_secret_access_key=s3_secret_key, endpoint_url=s3_endpoint, config=Config(signature_version="s3v4"))

# Function to generate video and upload it to Storj
def generate_video(prompt: str, seed: int) -> str:
    if seed == -1:
        seed = random.randint(0, 1000000)
    torch.manual_seed(seed)
    result = pipe({'text': prompt})[OutputKeys.OUTPUT_VIDEO]
    
    # Upload video to Storj
    bucket_name = "huggingface-demo"
    # Add the code to upload the video to the Storj bucket
    # and return the URL of the uploaded video

    return result

# Gradio Interface
examples = [
    ['An astronaut riding a horse.', 0],
    ['A panda eating bamboo on a rock.', 0],
    ['Spiderman is surfing.', 0],
]

# Import Storj Theme from the hub
storj_theme = gr.Theme.from_hub("bethecloud/storj_theme")

inputs = [
    gr.inputs.Textbox(lines=1, placeholder="Enter your text here..."),
    gr.inputs.Slider(minimum=-1, maximum=1000000, step=1, default=-1, label="Seed")
]

iface = gr.Interface(
    fn=generate_video,
    inputs=inputs,
    outputs=gr.outputs.Video(),
    theme=storj_theme,
    allow_flagging=False,
    examples=examples
)

## Run app
iface.launch(share=True, debug=True)