Update app.py

app.py

@@ -13,15 +13,12 @@ model_4x = "stabilityai/stable-diffusion-x4-upscaler"
 sd_2_0_2x = StableDiffusionLatentUpscalePipelin.from_pretrained(model_2x, torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionLatentUpscalePipeline.from_pretrained(model_2x)
 sd_2_1_4x = StableDiffusionUpscalePipeline.from_pretrained(model_4x, torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionUpscalePipeline.from_pretrained(model_4x)
 
-# Define the input and output components for the Gradio interface
-input_image = gr.inputs.Image(type="filepath")
-output_image = gr.outputs.Image(type="filepath")
-
 # Define the function that will be called when the interface is used
+
 def upscale_image(model, input_image):
     # Convert the image to a PyTorch tensor
     generator = torch.manual_seed(999999)
-    image = Image.open(input_image).convert("RGB")
+    input_image = Image.open(input_image).convert("RGB")
     
     
     # Upscale the image using the selected model
@@ -39,8 +36,8 @@ def upscale_image(model, input_image):
 # Define the Gradio interface
 iface = gr.Interface(
     fn=upscale_image,
-    inputs=[gr.Radio(["SD 2.0 2x Latent Upscaler", "SD 2.1 4x Upscaler"]), input_image],
-    outputs=output_image,
+    inputs=[gr.Radio(["SD 2.0 2x Latent Upscaler", "SD 2.1 4x Upscaler"]), gr.Image(type="filepath")],
+    outputs=gr.Image(type="filepath"),
     title="Image Upscaler",
     description="Upscale an image using either the SD 2.0 2x Latent Upscaler or the SD 2.1 4x Upscaler."
 )