Update app.py

app.py

@@ -10,7 +10,7 @@ from diffusers import FluxPipeline
 from translatepy import Translator
 
 # -----------------------------------------------------------------------------
-# CONFIGURATION: Adjust these parameters to customize your app
+# CONFIGURATION
 # -----------------------------------------------------------------------------
 config = {
     "model_id": "black-forest-labs/FLUX.1-dev",
@@ -60,15 +60,18 @@ def enable_lora(lora_add: str):
 # Function to generate an image from a prompt
 # -----------------------------------------------------------------------------
 @spaces.GPU()
-def generate_image(prompt: str, lora_word: str, lora_scale: float = config["default_loRa_scale"],
-                   width: int = config["default_width"], height: int = config["default_height"],
-                   guidance_scale: float = config["default_guidance_scale"], steps: int = config["default_steps"],
-                   seed: int = -1, nums: int = 1):
+def generate_image(
+    prompt: str, lora_word: str, lora_scale: float = config["default_loRa_scale"],
+    width: int = config["default_width"], height: int = config["default_height"],
+    guidance_scale: float = config["default_guidance_scale"], steps: int = config["default_steps"],
+    seed: int = -1, nums: int = 1
+):
     pipe.to(device)
     seed = random.randint(0, config["max_seed"]) if seed == -1 else int(seed)
     prompt_english = str(translator.translate(prompt, "English"))
     full_prompt = f"{prompt_english} {lora_word}"
     generator = torch.Generator().manual_seed(seed)
+
     result = pipe(
         prompt=full_prompt, height=height, width=width, guidance_scale=guidance_scale,
         output_type="pil", num_inference_steps=steps, num_images_per_prompt=nums,
@@ -88,6 +91,9 @@ example_prompts = [
 
 with gr.Blocks(css=config["css"]) as demo:
     gr.HTML("<h1><center>BR METAVERSO - Avatar Generator</center></h1>")
+    
+    processing_status = gr.Markdown("**🟢 Ready**", visible=True)  # Status indicator
+    
     with gr.Row():
         with gr.Column(scale=4):
             gallery = gr.Gallery(label="Flux Generated Image", columns=1, preview=True, height=600)
@@ -106,7 +112,21 @@ with gr.Blocks(css=config["css"]) as demo:
             load_lora_btn = gr.Button(value="Load LoRA", variant="secondary")
     
     gr.Examples(examples=example_prompts, inputs=[prompt_input, lora_word_text, lora_scale_slider], cache_examples=False, examples_per_page=4)
+    
+    # Ensuring processing status updates correctly
+    def update_status():
+        return "**⏳ Processing...**"
+    
+    generate_btn.click(fn=update_status, inputs=[], outputs=[processing_status]).then(
+        fn=generate_image,
+        inputs=[prompt_input, lora_word_text, lora_scale_slider, width_slider, height_slider, guidance_slider, steps_slider, seed_slider, nums_slider],
+        outputs=[gallery, seed_slider]
+    ).then(
+        fn=lambda: "**✅ Done!**",
+        inputs=[],
+        outputs=[processing_status]
+    )
+    
     load_lora_btn.click(fn=enable_lora, inputs=[lora_add_text], outputs=lora_add_text)
-    generate_btn.click(fn=generate_image, inputs=[prompt_input, lora_word_text, lora_scale_slider, width_slider, height_slider, guidance_slider, steps_slider, seed_slider, nums_slider], outputs=[gallery, seed_slider], api_name="run")
     
-demo.queue().launch()
+demo.queue().launch()