dream-machine / app.py
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import gradio as gr
import os
from lumaai import AsyncLumaAI
import asyncio
import aiohttp
import tempfile
async def generate_video(api_key, prompt, loop=False, aspect_ratio="16:9", progress=gr.Progress()):
client = AsyncLumaAI(auth_token=api_key)
progress(0, desc="Initiating video generation...")
generation = await client.generations.create(
prompt=prompt,
loop=loop,
aspect_ratio=aspect_ratio
)
progress(0.1, desc="Video generation started. Waiting for completion...")
# Poll for completion
start_time = asyncio.get_event_loop().time()
while True:
status = await client.generations.get(id=generation.id)
if status.state == "completed":
break
elif status.state == "failed":
raise Exception("Video generation failed")
# Update progress based on time elapsed (assuming 60 seconds total)
elapsed_time = asyncio.get_event_loop().time() - start_time
progress_value = min(0.1 + (elapsed_time / 60) * 0.8, 0.9)
progress(progress_value, desc="Generating video...")
await asyncio.sleep(5)
progress(0.9, desc="Downloading generated video...")
# Download the video
video_url = status.assets.video
async with aiohttp.ClientSession() as session:
async with session.get(video_url) as resp:
if resp.status == 200:
file_name = f"luma_ai_generated_{generation.id}.mp4"
with open(file_name, 'wb') as fd:
while True:
chunk = await resp.content.read(1024)
if not chunk:
break
fd.write(chunk)
progress(1.0, desc="Video generation complete!")
return file_name
async def text_to_video(api_key, prompt, loop, aspect_ratio, progress=gr.Progress()):
if not api_key:
raise gr.Error("Please enter your Luma AI API key.")
try:
video_path = await generate_video(api_key, prompt, loop, aspect_ratio, progress)
return video_path, ""
except Exception as e:
return None, f"An error occurred: {str(e)}"
async def image_to_video(api_key, prompt, image, loop, aspect_ratio, progress=gr.Progress()):
if not api_key:
raise gr.Error("Please enter your Luma AI API key.")
if image is None:
raise gr.Error("Please upload an image.")
try:
client = AsyncLumaAI(auth_token=api_key)
progress(0, desc="Uploading image...")
# Create a temporary file to store the uploaded image
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
temp_file.write(image)
temp_file_path = temp_file.name
# Upload the image to Luma AI (you might need to implement this function)
image_url = await upload_image_to_luma(client, temp_file_path)
progress(0.1, desc="Initiating video generation from image...")
generation = await client.generations.create(
prompt=prompt,
loop=loop,
aspect_ratio=aspect_ratio,
keyframes={
"frame0": {
"type": "image",
"url": image_url
}
}
)
progress(0.2, desc="Video generation started. Waiting for completion...")
# Poll for completion
start_time = asyncio.get_event_loop().time()
while True:
status = await client.generations.get(id=generation.id)
if status.state == "completed":
break
elif status.state == "failed":
raise Exception("Video generation failed")
# Update progress based on time elapsed (assuming 60 seconds total)
elapsed_time = asyncio.get_event_loop().time() - start_time
progress_value = min(0.2 + (elapsed_time / 60) * 0.7, 0.9)
progress(progress_value, desc="Generating video...")
await asyncio.sleep(5)
progress(0.9, desc="Downloading generated video...")
# Download the video
video_url = status.assets.video
async with aiohttp.ClientSession() as session:
async with session.get(video_url) as resp:
if resp.status == 200:
file_name = f"luma_ai_generated_{generation.id}.mp4"
with open(file_name, 'wb') as fd:
while True:
chunk = await resp.content.read(1024)
if not chunk:
break
fd.write(chunk)
# Clean up the temporary file
os.unlink(temp_file_path)
progress(1.0, desc="Video generation complete!")
return file_name, ""
except Exception as e:
return None, f"An error occurred: {str(e)}"
# You need to implement this function based on Luma AI's API for image uploading
async def upload_image_to_luma(client, image_path):
# This is a placeholder. You need to implement the actual image upload logic
# using the Luma AI API. The function should return the URL of the uploaded image.
raise NotImplementedError("Image upload to Luma AI is not implemented yet.")
with gr.Blocks() as demo:
gr.Markdown("# Luma AI Text-to-Video Demo")
api_key = gr.Textbox(label="Luma AI API Key", type="password")
with gr.Tab("Text to Video"):
prompt = gr.Textbox(label="Prompt")
generate_btn = gr.Button("Generate Video")
video_output = gr.Video(label="Generated Video")
error_output = gr.Textbox(label="Error Messages", visible=True)
with gr.Accordion("Advanced Options", open=False):
loop = gr.Checkbox(label="Loop", value=False)
aspect_ratio = gr.Dropdown(label="Aspect Ratio", choices=["16:9", "1:1", "9:16", "4:3", "3:4"], value="16:9")
generate_btn.click(
text_to_video,
inputs=[api_key, prompt, loop, aspect_ratio],
outputs=[video_output, error_output]
)
with gr.Tab("Image to Video"):
img_prompt = gr.Textbox(label="Prompt")
img_input = gr.Image(label="Upload Image", type="numpy")
img_generate_btn = gr.Button("Generate Video from Image")
img_video_output = gr.Video(label="Generated Video")
img_error_output = gr.Textbox(label="Error Messages", visible=True)
with gr.Accordion("Advanced Options", open=False):
img_loop = gr.Checkbox(label="Loop", value=False)
img_aspect_ratio = gr.Dropdown(label="Aspect Ratio", choices=["16:9", "1:1", "9:16", "4:3", "3:4"], value="16:9")
img_generate_btn.click(
image_to_video,
inputs=[api_key, img_prompt, img_input, img_loop, img_aspect_ratio],
outputs=[img_video_output, img_error_output]
)
demo.queue().launch(share=True)