gradio.exceptions.Error: 'No GPU is currently available for you after 60s'

app.py

@@ -139,7 +139,10 @@ def stage2_process(*args, **kwargs):
         return restore_in_Xmin(*args, **kwargs)
     except Exception as e:
         print('Exception of type ' + str(type(e)))
-        if type(e).__name__ == "<class 'gradio.exceptions.Error'>":
+        print("gradio.exceptions.Error: 'No GPU is currently available for you after 60s'")
+        print('Exception of message ' + str(e))
+        print(NO_GPU_MESSAGE_INQUEUE)
+        if str(type(e)) == "<class 'gradio.exceptions.Error'>":
             print('Exception of name ' + type(e).__name__)
         raise e
 
@@ -535,7 +538,7 @@ with gr.Blocks() as interface:
         prompt_hint = gr.HTML("You can use a <a href='"'https://huggingface.co/spaces/MaziyarPanahi/llava-llama-3-8b'"'>LlaVa space</a> to auto-generate the description of your image.")
         upscale = gr.Radio([["x1", 1], ["x2", 2], ["x3", 3], ["x4", 4], ["x5", 5], ["x6", 6], ["x7", 7], ["x8", 8], ["x9", 9], ["x10", 10]], label="Upscale factor", info="Resolution x1 to x10", value=2, interactive=True)
         output_format = gr.Radio([["As input", "input"], ["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for result", info="File extention", value="input", interactive=True)
-        allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min (discouraged)", 8], ["9 min (discouraged)", 9], ["10 min (discouraged)", 10]], label="GPU allocation time", info="lower=May abort run, higher=Quota penalty for next runs", value=5, interactive=True)
+        allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min (discouraged)", 7], ["8 min (discouraged)", 8], ["9 min (discouraged)", 9], ["10 min (discouraged)", 10]], label="GPU allocation time", info="lower=May abort run, higher=Quota penalty for next runs", value=5, interactive=True)
 
     with gr.Accordion("Pre-denoising (optional)", open=False):
         gamma_correction = gr.Slider(label="Gamma Correction", info = "lower=lighter, higher=darker", minimum=0.1, maximum=2.0, value=1.0, step=0.1)