Update app.py

app.py

@@ -27,8 +27,7 @@ pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.conf
 
 pipeline = pipeline.to(device)
 torch.cuda.empty_cache()
-seed = randrange(500)
-generator = torch.Generator(device=device).manual_seed(seed)
+
 
 
 
@@ -95,23 +94,31 @@ def floorplan_generation(outline, num_of_rooms):
     validation_prompt = "floor plan, " + num_of_rooms + " rooms"
     validation_image = n_outline
 
-    image = pipeline(validation_prompt, 
-                     validation_image, 
-                     num_inference_steps=20, 
-                     generator=generator).images[0]
-
-    image = np.array(image)
-    mask = np.array(n_outline)
-    mask = cv2.cvtColor(mask, cv2.COLOR_RGB2BGR)
-    image = clean_img(image, mask)
-    return image
+    image_lst = []
+    for i in range(2):
+        seed = randrange(500)
+        generator = torch.Generator(device=device).manual_seed(seed)
+    
+    
+        image = pipeline(validation_prompt, 
+                         validation_image, 
+                         num_inference_steps=20, 
+                         generator=generator).images[0]
+    
+        image = np.array(image)
+        mask = np.array(n_outline)
+        mask = cv2.cvtColor(mask, cv2.COLOR_RGB2BGR)
+        image = clean_img(image, mask)
+        image_lst.append(image)
+        
+    return image_lst[0], image_lst[1]
 
 
 gradio_interface = gradio.Interface(
   fn=floorplan_generation,
   inputs=[gradio.Image(label="Floor Plan Outline, Entrance"),
           gradio.Textbox(type="text", label="number of rooms", placeholder="number of rooms")],
-  outputs=gradio.Image(label="Generated Floor Plan"),
+  outputs=[gradio.Image(label="Generated Floor Plan 1"), gradio.Image(label="Generated Floor Plan 2")],
   title="floorplan generation")