Update app.py

app.py

@@ -1,8 +1,10 @@
-import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
 import torch
+import gradio as gr
+from diffusers import DiffusionPipeline
+from PIL import Image
+import io
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -37,11 +39,10 @@ def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt
             generator=generator
         ).images[0]
         print("Image generated successfully.")  # Debug: Confirm image generation
+        return image
     except Exception as e:
         print(f"Error generating image: {e}")
         return None
-    
-    return image
 
 examples = [
     ["red", "t-shirt", "yellow stripes"],
@@ -88,10 +89,23 @@ with gr.Blocks(css=css) as demo:
         
         gr.Examples(examples=examples, inputs=[prompt_part2, prompt_part3, prompt_part4])
 
-    run_button.click(
-        fn=infer,
-        inputs=[prompt_part1, prompt_part2, prompt_part3, prompt_part4, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result]
-    )
+    def run_infer():
+        output_image = infer(
+            prompt_part1.value,
+            prompt_part2.value,
+            prompt_part3.value,
+            prompt_part4.value,
+            prompt_part5.value,
+            negative_prompt.value,
+            seed.value,
+            randomize_seed.value,
+            width.value,
+            height.value,
+            guidance_scale.value,
+            num_inference_steps.value
+        )
+        return output_image
+
+    run_button.click(fn=run_infer, outputs=result)
 
-demo.queue().launch()
+demo.queue(api_name="/infer").launch()