Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import os
 import cv2
-import numpy as np
 
 def inference(file, mask, model, alpha_influence, segmentation_strength):
     im = cv2.imread(file, cv2.IMREAD_COLOR)
@@ -11,37 +10,26 @@ 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:
-            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,  # Control de influencia del canal alfa
-                    bg_color=(0, 0, 0, segmentation_strength)  # Control de fuerza de segmentación
-                )
-                o.write(output)
-                m.write(output)
+            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
+            )
 
-    return os.path.join("output.png"), os.path.join("mask.png")
+            o.write(output)
+    return os.path.join("output.png")
 
 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>"
 
-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(
+gr.Interface(
     inference, 
     [
         gr.inputs.Image(type="filepath", label="Input"),
@@ -71,17 +59,11 @@ iface = 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="plot", label="Processed Image"),
-        gr.outputs.Image(type="plot", label="Processed Mask"),
-    ],
+    gr.outputs.Image(type="filepath", label="Output"),
     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
-)
-
-iface.launch()
-
+).launch()