Upload 3 files

app.py

@@ -1,10 +1,20 @@
 import gradio as gr
 from gen_tab import create_gen_tab
 from train_tab import create_train_tab
+from dotenv import load_dotenv, find_dotenv
+import os 
 
-with gr.Blocks() as demo:
+_ = load_dotenv(find_dotenv())
+with gr.Blocks(theme=gr.themes.Soft(
+    radius_size=gr.themes.sizes.radius_none,
+    primary_hue=gr.themes.colors.emerald, secondary_hue=gr.themes.colors.green
+ 
+                                    )) as demo:
     with gr.Tabs() as tabs:
         create_gen_tab()
         create_train_tab()
+      #  with gr.TabItem("Theme builder"):
+     #       gr.themes.builder()
+      
 
-demo.launch(debug=True)
+demo.launch(share=True,debug=True) #,auth=[("username", "password"),(os.getenv("APP_USER"),os.getenv("APP_PW"))])

train_tab.py

@@ -8,7 +8,7 @@ def create_train_tab():
         gr.Markdown("# Image Importing & Auto captions")
         with gr.Row():
             input_images = gr.File(file_count="multiple", type="filepath", label="Upload Images")
-            label_model = gr.Dropdown(["blip", "llava-16","img2prompt"], label="Caption model", info="Auto caption model")
+            label_model = gr.Dropdown(["None","blip", "llava-16","img2prompt"],value="None", label="Caption model", info="Auto caption model")
             token_string= gr.Textbox(label="Token string",value="TOK",interactive=True,
                                      info="A unique string that will be trained to refer to the concept in the input images. Can be anything, but TOK works well.")
             context_text = gr.Textbox(label="Context Text", info="Context Text for auto caption",value=" I want a description caption for this image")
@@ -39,7 +39,7 @@ def create_train_tab():
             gr.Markdown("# Training on replicate")
         with gr.Row():
             traning_model = gr.Dropdown(["flux"], label="Caption model", info="Auto caption model")
-            traning_destination = gr.Textbox(label="destination",info="add in replicate model destination")
+            traning_destination = gr.Textbox(label="destination",info="add in replicate model destination, format [user]/[model_name]")
             seed = gr.Number(label="Seed", value=42,info="Random seed integer for reproducible training. Leave empty to use a random seed.")
             max_train_steps =gr.Number(label="max_train_steps", value= 1000, info="Number of individual training steps. Takes precedence over num_train_epochs.")
 
@@ -51,8 +51,11 @@ def create_train_tab():
 
         train_button.click(fn=traning_function, inputs=[zip_output,traning_model,traning_destination,seed,token_string,max_train_steps], 
                           outputs=[training_logs,traning_finnal],queue=True)
-        process_button.click(fn=process_images, inputs=[input_images,label_model,context_text,token_string], outputs=[image_output,text_output,zip_output],queue=True)
-        btn_update_zip.click(fn=create_zip, inputs=[image_output,text_output,token_string],outputs=zip_output)
+        
+        process_button.click(fn=process_images, inputs=[input_images,label_model,context_text,token_string],
+                              outputs=[image_output,text_output,zip_output],queue=True)
+        
+        btn_update_zip.click(fn=create_zip, inputs=[input_images,text_output,token_string],outputs=zip_output)
 
       #  traning_finnal.change(
       #      fn=update_dropdown,