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()