Update app.py

app.py

@@ -32,8 +32,6 @@ def get_negative_prompt():
     negative_prompt = st.text_input('Enter your negative prompt here:', placeholder="low quality, out of frame, watermark.. etc.")
     return negative_prompt
 
-import os
-
 def get_user_input():
     st.subheader("Upload an image file, Press Clean Background Button.")
     uploaded_file = st.file_uploader("Upload a JPG image file", type=["jpg", "jpeg"])
@@ -108,14 +106,15 @@ def load_images():
 if button_1:
     button_1_clicked = True    
     # Move items from data/custom_dataset/ to data/processed    
-    log( copy= True)        
+    log( copy= True)
+    clean_files("data/custom_dataset/")    
     if uploaded_file is not None:   
         uploaded_file.save(user_file_path)        
         run_subprocess()
-        clean_files("data/custom_dataset/")
         st.success("Background cleaned.")
         log(copy = True)
         
+        
 
 st.subheader("Text your prompt and choose parameters, then press Run Model button")
 
@@ -134,9 +133,8 @@ num_images_per_prompt = st.slider('Image Count to be Produced:', min_value=1, ma
 # use seed with torch generator
 #torch.manual_seed(0)
 # seed
-
-seed = st.slider('Seed:', min_value=0, max_value=100, value=1)
-generator = [torch.Generator(device="cuda").manual_seed(seed) for i in range(num_images_per_prompt)]
+#seed = st.slider('Seed:', min_value=0, max_value=100, value=1)
+#generator = [torch.Generator(device="cuda").manual_seed(seed) for i in range(num_images_per_prompt)]
 
 #generator = torch.Generator(device="cuda").manual_seed(0)
 run_model_button = st.button("Run Model") 
@@ -181,7 +179,6 @@ def show_output(x5):
 
 # Check if the button is clicked and all inputs are provided
 if run_model_button == True and input_prompt is not None :
-    dif_image , inverted_image = load_images()
     st.write("Running the model...")
     dif_image , inverted_image = load_images()
     output_width, output_height = image_resize(dif_image)
@@ -195,7 +192,7 @@ if run_model_button == True and input_prompt is not None :
     #output_height = 128
     #output_width = 128
 
-    x5 = pipe(image=dif_image, mask_image=inverted_image, num_inference_steps=num_inference_steps,generator= generator,
+    x5 = pipe(image=dif_image, mask_image=inverted_image, num_inference_steps=num_inference_steps,  # , generator= generator
                 num_images_per_prompt=num_images_per_prompt, prompt=prompt, negative_prompt=input_negative_prompt,
                 height=output_height, width=output_width).images