Update app.py

app.py

@@ -68,6 +68,7 @@ def load_captioning(uploaded_images, concept_sentence):
 
     # Update for the sample caption area
     updates.append(gr.update(visible=True))
+    # Update prompt samples
     updates.append(gr.update(placeholder=f'A portrait of person in a bustling cafe {concept_sentence}', value=f'A person in a bustling cafe {concept_sentence}'))
     updates.append(gr.update(placeholder=f"A mountainous landscape in the style of {concept_sentence}"))
     updates.append(gr.update(placeholder=f"A {concept_sentence} in a mall"))
@@ -238,6 +239,15 @@ def swap_visibilty(profile: Union[gr.OAuthProfile, None]):
     else:
         return gr.update(elem_classes=["main_ui_logged_in"])
 
+def update_pricing(steps):
+    updates.append()
+    seconds_per_iteration = 7.54
+    total_seconds = (steps * seconds_per_iteration) + 240
+    cost_per_second = 0.80/60/60
+    cost = round(cost_per_second * total_seconds, 2)
+    cost_preview = f'''To train this LoRA, a paid L4 GPU will be hooked under the hood during training and then removed once finished.
+    ## Estimated to cost <b>< US$ {str(cost)}</b> for {round(int(total_seconds)/60, 2)} minutes with your current train settings <small>({int(iterations)} iterations at {seconds_per_iteration}s/it)</small>'''
+    return gr.update(visible=True), gr.update(cost_preview)
 
 with gr.Blocks(theme=theme, css=css) as demo:
     gr.Markdown(
@@ -318,7 +328,8 @@ with gr.Blocks(theme=theme, css=css) as demo:
                 sample_1 = gr.Textbox(label="Test prompt 1")
                 sample_2 = gr.Textbox(label="Test prompt 2")
                 sample_3 = gr.Textbox(label="Test prompt 3")
-
+            with gr.Group(visible=False) as cost_preview:
+                cost_preview_info = gr.Markdown()
             output_components.append(sample)
             output_components.append(sample_1)
             output_components.append(sample_2)
@@ -367,7 +378,15 @@ with gr.Blocks(theme=theme, css=css) as demo:
 
     dataset_folder = gr.State()
 
-    images.upload(load_captioning, inputs=[images, concept_sentence], outputs=output_components)
+    images.upload(
+        load_captioning,
+        inputs=[images, concept_sentence],
+        outputs=output_components
+    ).then(
+        update_pricing,
+        inputs=[steps],
+        outputs=[cost_preview, cost_preview_info]
+    )
 
     start.click(fn=create_dataset, inputs=[images] + caption_list, outputs=dataset_folder).then(
         fn=start_training,