Update app.py

app.py

@@ -104,7 +104,6 @@ def encode_image_to_base64(image):
 def generate_image_from_text(encoded_image,pos_prompt=None):
     neg_prompt = '''Detailed, complex textures, intricate patterns, realistic lighting, high contrast, reflections, fuzzy surface, realistic proportions, photographic quality, vibrant colors, detailed background, shadows, disfigured, deformed, ugly, multiple, duplicate.'''
     encoded_str = encode_image_to_base64(encoded_image)
-    new_prompt = gen_pos_prompt(pos_prompt)
     if pos_prompt:
         parameters = {
               'taskType': 'IMAGE_VARIATION',
@@ -199,8 +198,7 @@ with gr.Blocks() as demo:
                     image_mode="RGBA",
                     sources="upload",
                     type="pil",
-                    elem_id="content_image",
-                    visible=True  
+                    elem_id="content_image" 
                 )
                 text_prompt = gr.Textbox(
                     label="Text Prompt",
@@ -241,24 +239,13 @@ with gr.Blocks() as demo:
                     interactive=False,
                 )
                 gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
-    # with gr.Row(variant="panel"):
-    #     gr.Examples(
-    #         examples=[
-    #             os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
-    #         ],
-    #         inputs=[input_image],
-    #         outputs=[processed_image, output_model_obj, output_model_glb],
-    #         cache_examples=True,
-    #         fn=partial(run_example, do_remove_background=True, foreground_ratio=0.85, mc_resolution=256),
-    #         label="Examples",
-    #         examples_per_page=20
-    #     )
+                
     submit.click(fn=check_input_image, inputs=[input_image]).success(
         fn=run_example,
         inputs=[input_image, do_remove_background, foreground_ratio, mc_resolution, text_prompt],
-        outputs=[output_model_obj, output_model_glb],
+        outputs=[processed_image, output_model_obj, output_model_glb],
         # outputs=[output_model_obj, output_model_glb],
     )
 
 demo.queue(max_size=10)
-demo.launch(auth=(os.getenv("USERNAME"), os.getenv("PASSWORD")))
+demo.launch()