Update app.py

app.py

@@ -23,6 +23,7 @@ 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(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)
@@ -44,7 +45,7 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
     if image is None:
-        return [None, None]
+        return [None, None, "Error: Failed to generate image"]
     
     image_path = "temp_image.jpg"
     image.save(image_path, format="JPEG")
@@ -54,12 +55,12 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
         if upscale_image_path is not None:
             upscale_image = Image.open(upscale_image_path)
             upscale_image.save("upscale_image.jpg", format="JPEG")
-            return [image_path, "upscale_image.jpg"]
+            return [image_path, "upscale_image.jpg", seed]
         else:
             print("Error: The scaled image path is None")
-            return [image_path, image_path]
+            return [image_path, image_path, seed]
     else:
-        return [image_path, image_path]
+        return [image_path, image_path, seed]
 
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
@@ -84,8 +85,8 @@ with gr.Blocks(css=css) as demo:
                     scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8)
                     steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=8)
                     seed = gr.Number(label="Seed", value=-1)
-                    print(seed)
     
                 btn = gr.Button("Generate")
-                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res,)
+                seed_output = gr.Textbox(label="Selected Seed")  # Add output for seed
+                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=[output_res, seed_output])
     demo.launch()