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import gradio as gr
import tempfile
import os
from SDXLImageGenerator import SDXLImageGenerator # Import your existing class
class ControlNetProcessor:
def controlnet_image(self, image):
# Placeholder for ControlNet processing (e.g., returning a processed image or placeholder text)
return "Placeholder for ControlNet Output Image"
class VideoGenerator:
def generate_3d_video(self, controlled_image):
# Creating a temporary video with a placeholder for demonstration purposes.
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp:
# Generates a sample video with FFmpeg using a solid color and overlay text
os.system(
f"ffmpeg -f lavfi -i color=c=blue:s=320x240:d=5 "
f"-vf drawtext=fontfile=/path/to/font.ttf:text='3D Model':fontsize=24:fontcolor=white:x=(w-text_w)/2:y=(h-text_h)/2 "
f"{tmp.name}"
)
video_path = tmp.name
return video_path
class GradioApp:
def __init__(self):
self.sdxl_generator = SDXLImageGenerator() # Use your existing class
self.controlnet_processor = ControlNetProcessor()
self.video_generator = VideoGenerator()
def full_pipeline(self, prompt):
initial_image = self.sdxl_generator.generate_images([prompt])[0]
controlled_image = self.controlnet_processor.controlnet_image(initial_image)
video_path = self.video_generator.generate_3d_video(controlled_image)
return initial_image, controlled_image, video_path
def launch(self):
interface = gr.Interface(
fn=self.full_pipeline,
inputs=gr.Textbox(label="Input Prompt"),
outputs=[
gr.Image(label="Generated Image"),
gr.Textbox(label="ControlNet Output Image Placeholder"),
gr.Video(label="3D Model Video")
],
title="SDXL to ControlNet to 3D Pipeline",
description="Generate an image using SDXL, refine it with ControlNet, and generate a 3D video output."
)
interface.launch(share=True) # Added `share=True` for public link
if __name__ == "__main__":
app = GradioApp()
app.launch()
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