Update app.py

app.py

@@ -23,8 +23,6 @@ async def generate_image(prompt, model, lora_word, width, height, scales, steps,
         if seed == -1:
             seed = random.randint(0, MAX_SEED)
         seed = int(seed)
-        print("Console logging is functional.")
-        print(f"Selected Seed: {seed}")  # Print the seed
         text = str(Translator().translate(prompt, 'English')) + "," + lora_word
         client = AsyncInferenceClient()
         image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
@@ -44,13 +42,10 @@ def get_upscale_finegrain(prompt, img_path, upscale_factor):
 
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
-    image, selected_seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
+    image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
     if image is None:
-        print(f"Error: Image generation failed with seed {selected_seed}")
         return [None, None]
-    print(f"Selected Seed: {selected_seed}")  # Log after completion
-    sys.stdout.flush()  # Ensure immediate console output
-
+    
     image_path = "temp_image.jpg"
     image.save(image_path, format="JPEG")
     
@@ -66,7 +61,6 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
     else:
         return [image_path, image_path]
 
-
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """