Update app.py

app.py

@@ -1,8 +1,11 @@
 import torch
 import requests
+import rembg
+import random
+import gradio as gr
+
 from PIL import Image
 from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
-import rembg
 
 # Load the pipeline
 pipeline = DiffusionPipeline.from_pretrained(
@@ -23,7 +26,9 @@ pipeline.to('cuda:0')
 def inference(input_img, num_inference_steps, guidance_scale, seed ):
     # Download an example image.
     cond = Image.open(input_img)
-    
+    if seed==0:
+        seed = random.randint(1, 1000000)
+        
     # Run the pipeline!
     #result = pipeline(cond, num_inference_steps=75).images[0]
     result = pipeline(cond, num_inference_steps=num_inference_steps, 
@@ -40,13 +45,17 @@ def inference(input_img, num_inference_steps, guidance_scale, seed ):
     return result
 
 def remove_background(result):
-    result = Image.open(result)
-    result = rembg.remove(result)
+    # Check if the variable is a PIL Image
+    if isinstance(result, Image.Image):
+        result = rembg.remove(result)
+    # Check if the variable is a str filepath
+    elif isinstance(result, str):
+        result = Image.open(result)
+        result = rembg.remove(result)
     return result
+    
 
 
-import gradio as gr
-
 with gr.Blocks() as demo:
     gr.Markdown("<h1><center> Zero123++ Demo</center></h1>")
     with gr.Column():