Update app.py

app.py

@@ -10,26 +10,37 @@ def inference(file, mask, model, alpha_influence, segmentation_strength):
 
     input_path = 'input.png'
     output_path = 'output.png'
+    mask_path = 'mask.png'
 
     with open(input_path, 'rb') as i:
         with open(output_path, 'wb') as o:
-            input = i.read()
-            output = remove(
-                input, 
-                session=new_session(model), 
-                only_mask=(True if mask == "Mask only" else False),
-                alpha=alpha_influence,  # Control de influencia del canal alfa
-                bg_color=(0, 0, 0, segmentation_strength)  # Control de fuerza de segmentación
-            )
+            with open(mask_path, 'wb') as m:
+                input = i.read()
+                output = remove(
+                    input, 
+                    session=new_session(model), 
+                    only_mask=(True if mask == "Mask only" else False),
+                    alpha=alpha_influence,
+                    bg_color=(0, 0, 0, segmentation_strength)
+                )
+                o.write(output)
+                m.write(output)
 
-            o.write(output)
-    return os.path.join("output.png")
+    return os.path.join("output.png"), os.path.join("mask.png")
 
-title = "RemBG_Super"
-description = "Gradio demo for RemBG. erase the background of any image, To use it, simply upload your image and adjust the sliders and choose a eraser plugin from the U2net library."
+title = "RemBG"
+description = "Gradio demo for RemBG. To use it, simply upload your image and adjust the alpha influence and segmentation strength."
 article = "<p style='text-align: center;'><a href='https://github.com/danielgatis/rembg' target='_blank'>Github Repo</a></p>"
 
-gr.Interface(
+def show_processed_image(output_image_path):
+    output_image = cv2.imread(output_image_path)
+    return output_image
+
+def show_processed_mask(mask_image_path):
+    mask_image = cv2.imread(mask_image_path)
+    return mask_image
+
+iface = gr.Interface(
     inference, 
     [
         gr.inputs.Image(type="filepath", label="Input"),
@@ -59,10 +70,15 @@ gr.Interface(
         gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Alpha Influence"),
         gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Segmentation Strength"),
     ], 
-    gr.outputs.Image(type="filepath", label="Output"),
+    [
+        gr.outputs.Image(type="plot", label="Processed Image"),
+        gr.outputs.Image(type="plot", label="Processed Mask"),
+    ],
     title=title,
     description=description,
     article=article,
     examples=[["lion.png", "Default", "u2net", 0.5, 0.5], ["girl.jpg", "Default", "u2net", 0.5, 0.5], ["anime-girl.jpg", "Default", "isnet-anime", 0.5, 0.5]],
     enable_queue=True
-).launch()
+)
+
+iface.launch()