Update app.py

app.py

@@ -1,53 +1,6 @@
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
 from PIL import Image, ImageOps, ImageEnhance
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else:
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(prompt_part1, color, dress_type, front_design, back_design, prompt_part5, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-    front_prompt = f"front view of {prompt_part1} {color} colored plain {dress_type} with {front_design} design, {prompt_part5}"
-    back_prompt = f"back view of {prompt_part1} {color} colored plain {dress_type} with {back_design} design, {prompt_part5}"
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    front_image = pipe(
-        prompt=front_prompt, 
-        guidance_scale=guidance_scale, 
-        num_inference_steps=num_inference_steps, 
-        width=width, 
-        height=height,
-        generator=generator
-    ).images[0]
-    
-    back_image = pipe(
-        prompt=back_prompt, 
-        guidance_scale=guidance_scale, 
-        num_inference_steps=num_inference_steps, 
-        width=width, 
-        height=height,
-        generator=generator
-    ).images[0]
-    
-    return front_image, back_image
-
 def edit_image(img_data, operation, *args):
     image = Image.open(img_data)
     
@@ -72,63 +25,27 @@ def edit_image(img_data, operation, *args):
     return image
 
 examples = [
-    ["red", "t-shirt", "yellow stripes", "polka dots"],
-    ["blue", "hoodie", "minimalist", "abstract art"],
-    ["red", "sweat shirt", "geometric design", "plain"],
+    ["rotate", "90", "crop", "100,100,400,400"],
+    ["resize", "400,400", "flip", "horizontal"],
+    ["color", "1.5"]
 ]
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
 with gr.Blocks() as demo:
     with gr.Row():
-        gr.Markdown(f"""
-        # GenZ Couture
-        Currently running on {power_device}.
-        """)
+        gr.Markdown("# Image Editor")
 
     with gr.Row():
         with gr.Column():
-            prompt_part1 = gr.Textbox(value="a single", label="Prompt Part 1")
-            prompt_part2 = gr.Textbox(label="Color", placeholder="Color (e.g., red, blue)")
-            prompt_part3 = gr.Textbox(label="Dress Type", placeholder="Dress Type (e.g., t-shirt, hoodie)")
-            prompt_part4_front = gr.Textbox(label="Front Design", placeholder="Front Design")
-            prompt_part4_back = gr.Textbox(label="Back Design", placeholder="Back Design")
-            prompt_part5 = gr.Textbox(value="hanging on the plain wall", label="Prompt Part 5")
-            seed = gr.Slider(0, MAX_SEED, step=1, label="Seed", value=42)
-            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-            width = gr.Slider(256, MAX_IMAGE_SIZE, step=32, label="Width", value=512)
-            height = gr.Slider(256, MAX_IMAGE_SIZE, step=32, label="Height", value=512)
-            guidance_scale = gr.Slider(1, 20, step=0.5, label="Guidance Scale", value=7.5)
-            num_inference_steps = gr.Slider(10, 100, step=1, label="Number of Inference Steps", value=50)
-            
-            run_button = gr.Button("Generate Designs")
-        
-        with gr.Column():
-            front_result = gr.Image(label="Front View Result", type="pil", interactive=True)
-            back_result = gr.Image(label="Back View Result", type="pil", interactive=True)
-
-    run_button.click(
-        fn=infer,
-        inputs=[prompt_part1, prompt_part2, prompt_part3, prompt_part4_front, prompt_part4_back, prompt_part5, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[front_result, back_result]
-    )
-
-    gr.Markdown("## Creative Touch")
-
-    with gr.Row():
-        edit_operation = gr.Dropdown(choices=["rotate", "crop", "resize", "flip", "color"], label="Edit Operation")
-        edit_args = gr.Textbox(label="Edit Arguments (comma-separated)", placeholder="For rotate: angle, For crop: left,top,right,bottom, For resize: width,height, For flip: horizontal/vertical, For color: factor")
+            edit_operation = gr.Dropdown(choices=["rotate", "crop", "resize", "flip", "color"], label="Edit Operation")
+            edit_args = gr.Textbox(label="Edit Arguments (comma-separated)", placeholder="For rotate: angle, For crop: left,top,right,bottom, For resize: width,height, For flip: horizontal/vertical, For color: factor")
 
-        edit_button = gr.Button("Edit Front Design")
-        edited_image = gr.Image(label="Edited Front Design", type="pil", interactive=True)
+            edit_button = gr.Button("Edit Image")
+            edited_image = gr.Image(label="Edited Image", type="pil", interactive=True)
 
         edit_button.click(
             fn=lambda img_data, operation, args: edit_image(img_data, operation, *args.split(',')),
-            inputs=[front_result, edit_operation, edit_args],
+            inputs=[edited_image, edit_operation, edit_args],
             outputs=[edited_image]
         )
 
-demo.queue().launch()
+demo.queue().launch()