Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from utils import *
+from utils import func_generate
 import random
 
 is_clicked = False
@@ -89,43 +89,6 @@ with gr.Blocks() as app:
     clear_btn2.click(clear_data2, None, [out11, out12, out13, out14, out15])
 
 
-    def func_generate(query, concept_idx, seed_start, contrast_loss=False, contrast_perc=None):
-        prompt = query + ' in the style of bulb'
-        text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True,
-                               return_tensors="pt")
-        input_ids = text_input.input_ids.to(torch_device)
-
-        # Get token embeddings
-        position_ids = text_encoder.text_model.embeddings.position_ids[:, :77]
-        position_embeddings = pos_emb_layer(position_ids)
-
-        s = seed_start
-
-        token_embeddings = token_emb_layer(input_ids)
-        # The new embedding - our special birb word
-        replacement_token_embedding = concept_embeds[concept_idx].to(torch_device)
-
-        # Insert this into the token embeddings
-        token_embeddings[0, torch.where(input_ids[0] == 22373)] = replacement_token_embedding.to(torch_device)
-
-        # Combine with pos embs
-        input_embeddings = token_embeddings + position_embeddings
-
-        #  Feed through to get final output embs
-        modified_output_embeddings = get_output_embeds(input_embeddings)
-
-        # And generate an image with this:
-
-        if contrast_loss and seed_values[concept_idx] > 0:
-            s = seed_values[concept_idx]
-        else:
-            s = random.randint(s + 1, s + 30)
-            seed_values[concept_idx] = s
-        
-        g = torch.manual_seed(s)
-        return generate_with_embs(text_input, modified_output_embeddings, generator=g, contrast_loss=contrast_loss, contrast_perc=contrast_perc)
-
-
     def generate_image(query, con_idx, o1, o2, o3, o4, o5, contrast):
         if not query:
             raise gr.Error("No prompt provided")