Update app.py

app.py

@@ -155,16 +155,21 @@ def infer(prompt, negative_prompt, image, model_type="Standard"):
     prompts = num_samples * [prompt]
     if model_type=="Standard":
         prompt_ids = std_pipeline.prepare_text_inputs(prompts)
-    if model_type=="Hand Encoding":
+    elif model_type=="Hand Encoding":
         prompt_ids = enc_pipeline.prepare_text_inputs(prompts)
+    else:
+        pass
     prompt_ids = shard(prompt_ids)
 
     if model_type=="Standard":
         annotated_image = generate_annotation(image, overlap=False, hand_encoding=False)
         overlap_image = generate_annotation(image, overlap=True, hand_encoding=False)
-    if model_type=="Hand Encoding":
+    elif model_type=="Hand Encoding":
         annotated_image = generate_annotation(image, overlap=False, hand_encoding=True)
         overlap_image = generate_annotation(image, overlap=True, hand_encoding=True)
+
+    else:
+        pass
     validation_image = Image.fromarray(annotated_image).convert("RGB")
 
     if model_type=="Standard":
@@ -183,7 +188,7 @@ def infer(prompt, negative_prompt, image, model_type="Standard"):
             neg_prompt_ids=negative_prompt_ids,
             jit=True,
         ).images
-    if model_type=="Hand Encoding":
+    elif model_type=="Hand Encoding":
         processed_image = enc_pipeline.prepare_image_inputs(num_samples * [validation_image])
         processed_image = shard(processed_image)
 
@@ -200,7 +205,8 @@ def infer(prompt, negative_prompt, image, model_type="Standard"):
             jit=True,
         ).images
 
-
+    else:
+        pass
     images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
 
     results = [i for i in images]