Delete app.py

app.py

@@ -1,78 +0,0 @@
-import numpy as np
-from PIL import Image
-import torch
-from torchvision import transforms
-import gradio as gr
-from src.image_prep import canny_from_pil
-from src.pix2pix_turbo import Pix2Pix_Turbo
-
-model = Pix2Pix_Turbo("edge_to_image")
-
-
-def process(input_image, prompt, low_threshold, high_threshold):
-    # resize to be a multiple of 8
-    new_width = input_image.width - input_image.width % 8
-    new_height = input_image.height - input_image.height % 8
-    input_image = input_image.resize((new_width, new_height))
-    canny = canny_from_pil(input_image, low_threshold, high_threshold)
-    with torch.no_grad():
-        c_t = transforms.ToTensor()(canny).unsqueeze(0).cuda()
-        output_image = model(c_t, prompt)
-        output_pil = transforms.ToPILImage()(output_image[0].cpu() * 0.5 + 0.5)
-    # flippy canny values, map all 0s to 1s and 1s to 0s
-    canny_viz = 1 - (np.array(canny) / 255)
-    canny_viz = Image.fromarray((canny_viz * 255).astype(np.uint8))
-    return canny_viz, output_pil
-
-
-if __name__ == "__main__":
-    # load the model
-    with gr.Blocks() as demo:
-        gr.Markdown("# Pix2pix-Turbo: **Canny Edge -> Image**")
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(sources="upload", type="pil")
-                prompt = gr.Textbox(label="Prompt")
-                low_threshold = gr.Slider(
-                    label="Canny low threshold",
-                    minimum=1,
-                    maximum=255,
-                    value=100,
-                    step=10,
-                )
-                high_threshold = gr.Slider(
-                    label="Canny high threshold",
-                    minimum=1,
-                    maximum=255,
-                    value=200,
-                    step=10,
-                )
-                run_button = gr.Button(value="Run")
-            with gr.Column():
-                result_canny = gr.Image(type="pil")
-            with gr.Column():
-                result_output = gr.Image(type="pil")
-
-        prompt.submit(
-            fn=process,
-            inputs=[input_image, prompt, low_threshold, high_threshold],
-            outputs=[result_canny, result_output],
-        )
-        low_threshold.change(
-            fn=process,
-            inputs=[input_image, prompt, low_threshold, high_threshold],
-            outputs=[result_canny, result_output],
-        )
-        high_threshold.change(
-            fn=process,
-            inputs=[input_image, prompt, low_threshold, high_threshold],
-            outputs=[result_canny, result_output],
-        )
-        run_button.click(
-            fn=process,
-            inputs=[input_image, prompt, low_threshold, high_threshold],
-            outputs=[result_canny, result_output],
-        )
-
-    demo.queue()
-    demo.launch(debug=True, share=False)