Update app.py

app.py

@@ -2,7 +2,6 @@ import torch
 from PIL import Image
 from RealESRGAN import RealESRGAN
 import gradio as gr
-from gradio_imageslider import ImageSlider
 import spaces
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -13,7 +12,6 @@ model4.load_weights('weights/RealESRGAN_x4.pth', download=True)
 model8 = RealESRGAN(device, scale=8)
 model8.load_weights('weights/RealESRGAN_x8.pth', download=True)
 
-
 @spaces.GPU
 def inference(image, size):
     global model2
@@ -22,8 +20,6 @@ def inference(image, size):
     if image is None:
         raise gr.Error("Image not uploaded")
         
-    # Store original image for comparison
-    original_image = image.copy()
 
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
@@ -47,7 +43,7 @@ def inference(image, size):
     else:
         try:
             width, height = image.size
-            if width >= 5000 or height >= 5000:
+            if width >= 6000 or height >= 6000:
                 raise gr.Error("The image is too large.")
             result = model8.predict(image.convert('RGB'))
         except torch.cuda.OutOfMemoryError as e:
@@ -57,36 +53,21 @@ def inference(image, size):
             result = model2.predict(image.convert('RGB'))
             
     print(f"Image size ({device}): {size} ... OK")
-    # Return tuple of original and processed images for the slider
-    return (original_image, result)
-
+    return result
 
-title = """<h1 align="center">ProFaker</h1>"""
 
-with gr.Blocks() as demo:
-    gr.HTML(title)
-    
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(type="pil", label="Input Image")
-            size_select = gr.Radio(
-                ["2x", "4x", "8x"],
-                type="value",
-                value="2x",
-                label="Resolution model"
-            )
-            process_btn = gr.Button("Upscale Image")
-            
-        with gr.Column():
-            result_slider = ImageSlider(
-                interactive=False,
-                label="Before and After Comparison"
-            )
-    
-    process_btn.click(
-        fn=inference,
-        inputs=[input_image, size_select],
-        outputs=result_slider
-    )
+title = "ProFaker"
 
-demo.queue(api_open=True).launch(show_error=True, show_api=True)
+gr.Interface(inference,
+    [gr.Image(type="pil"), 
+    gr.Radio(["2x", "4x", "8x"], 
+    type="value",
+    value="2x",
+    label="Resolution model")], 
+    gr.Image(type="pil", label="Output"),
+    title=title,
+    flagging_mode="never",
+    cache_mode="lazy",
+    delete_cache=(44000, 44000),
+    ).queue(api_open=True).launch(show_error=True, show_api=True)
+