prompt

app.py

@@ -162,9 +162,18 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue", secondar
             with gr.Group(elem_classes="input-group"):
                 input_image = gr.Image(label="Input Image (Florence-2 Captioner)", height=512)
             
-            with gr.Accordion("Advanced Settings", open=False):
-                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
+            with gr.Accordion("Image Settings", open=False):
+                width = gr.Slider(label="Width", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                height = gr.Slider(label="Height", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=7.5, step=0.1, value=4.5)
+                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=40)
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                 negative_prompt = gr.Textbox(label="Negative Prompt")
+
+        with gr.Column(scale=1):
+            with gr.Group(elem_classes="input-group"):
+                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
                 use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False)
                 use_llm_generator = gr.Checkbox(label="Use LLM Prompt Generator", value=False)
                 llm_provider = gr.Dropdown(
@@ -185,38 +194,45 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue", secondar
                     value="Random",
                     visible=False
                 )
-                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                width = gr.Slider(label="Width", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
-                height = gr.Slider(label="Height", minimum=512, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
-                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=7.5, step=0.1, value=4.5)
-                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=40)
+                generate_prompt_btn = gr.Button("Generate Prompt", elem_classes="submit-btn")
+                final_prompt = gr.Textbox(label="Final Prompt", interactive=False)
             
             generate_btn = gr.Button("Generate Image", elem_classes="submit-btn")
         
         with gr.Column(scale=1):
             with gr.Group(elem_classes="output-group"):
                 output_image = gr.Image(label="Result", elem_id="gallery", show_label=False)
-                final_prompt = gr.Textbox(label="Final Prompt Used")
                 used_seed = gr.Number(label="Seed Used")
-    
-    def update_llm_visibility(use_llm):
-        return {
-            llm_provider: gr.update(visible=use_llm),
-            llm_model: gr.update(visible=use_llm),
-            prompt_type: gr.update(visible=use_llm)
-        }
-
-    use_llm_generator.change(
-        update_llm_visibility,
-        inputs=[use_llm_generator],
-        outputs=[llm_provider, llm_model, prompt_type]
+
+    # ... existing code for update_llm_visibility function ...
+
+    def generate_prompt(image, text_prompt, use_enhancer, use_llm_generator, llm_provider, llm_model, prompt_type):
+        if image is not None:
+            caption = florence_caption(image)
+            if use_llm_generator:
+                prompt = generate_llm_prompt(caption, llm_provider, llm_model, prompt_type)
+            else:
+                prompt = caption
+        else:
+            prompt = text_prompt
+        
+        if use_enhancer:
+            prompt = enhance_prompt(prompt)
+        
+        return prompt
+
+    generate_prompt_btn.click(
+        fn=generate_prompt,
+        inputs=[
+            input_image, text_prompt, use_enhancer, use_llm_generator, llm_provider, llm_model, prompt_type
+        ],
+        outputs=[final_prompt]
     )
 
     generate_btn.click(
         fn=process_workflow,
         inputs=[
-            input_image, text_prompt, use_enhancer, use_llm_generator, llm_provider, llm_model, prompt_type,
+            input_image, final_prompt, use_enhancer, use_llm_generator, llm_provider, llm_model, prompt_type,
             seed, randomize_seed, width, height, guidance_scale, num_inference_steps, negative_prompt
         ],
         outputs=[output_image, final_prompt, used_seed]