Update app.py

app.py

@@ -1,46 +1,16 @@
 import gradio as gr
-import numpy as np
 import random
-from diffusers import DiffusionPipeline
-import torch
 
-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, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image
+# Example T-shirt mockup generation function (replace with actual implementation)
+def generate_tshirt_mockup(style, color, graphics, text=None):
+    # Generate a mockup based on T-shirt style, color, graphics, and optionally text
+    mockup = f"Generated T-shirt mockup:\nStyle: {style}\nColor: {color}\nGraphics: {graphics}\nText: {text}" if text else f"Generated T-shirt mockup:\nStyle: {style}\nColor: {color}\nGraphics: {graphics}"
+    return mockup
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "Casual T-shirt, Blue, with abstract art",
+    "Formal T-shirt, White, with logo",
+    "Sports T-shirt, Red, with team name",
 ]
 
 css="""
@@ -50,97 +20,55 @@ css="""
 }
 """
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
+        # T-shirt Mockup Generator
         """)
         
         with gr.Row():
             
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
+            style = gr.Dropdown(
+                label="T-shirt Style",
+                choices=["Casual", "Formal", "Sports"],
+                default="Casual",
                 container=False,
             )
             
-            run_button = gr.Button("Run", scale=0)
+            run_button = gr.Button("Generate Mockup", scale=0)
         
-        result = gr.Image(label="Result", show_label=False)
+        result = gr.Textbox(label="Mockup", placeholder="Generated Mockup", readonly=True)
 
-        with gr.Accordion("Advanced Settings", open=False):
+        with gr.Accordion("Design Options", open=False):
             
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+            color = gr.Textbox(
+                label="T-shirt Color",
+                placeholder="Enter color",
+                visible=True,
             )
             
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+            graphics = gr.Textbox(
+                label="Graphics",
+                placeholder="Enter graphic details",
+                visible=True,
             )
             
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
+            text = gr.Textbox(
+                label="Text (optional)",
+                placeholder="Enter text for T-shirt",
+                visible=True,
+            )
             
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            examples=examples,
+            inputs=[style]
         )
 
     run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+        fn=generate_tshirt_mockup,
+        inputs=[style, color, graphics, text],
+        outputs=[result]
     )
 
-demo.queue().launch()
+demo.queue().launch()