Update app.py

app.py

@@ -9,28 +9,28 @@ import gradio as gr
 model = from_pretrained_keras("Dvjc1899/super-resolution")
 
 def infer(img):
-    img = img.resize((300, 300))
+	img = img.resize((300, 300))
 	ycbcr = img.convert("YCbCr")
-    y, cb, cr = ycbcr.split()
-    y = img_to_array(y)
-    y = y.astype("float32") / 255.0
+	y, cb, cr = ycbcr.split()
+	y = img_to_array(y)
+	y = y.astype("float32") / 255.0
 
-    input = np.expand_dims(y, axis=0)
-    out = model.predict(input)
+	input = np.expand_dims(y, axis=0)
+	out = model.predict(input)
 
-    out_img_y = out[0]
-    out_img_y *= 255.0
+	out_img_y = out[0]
+	out_img_y *= 255.0
 
-    # Restore the image in RGB color space.
-    out_img_y = out_img_y.clip(0, 255)
-    out_img_y = out_img_y.reshape((np.shape(out_img_y)[0], np.shape(out_img_y)[1]))
-    out_img_y = PIL.Image.fromarray(np.uint8(out_img_y), mode="L")
-    out_img_cb = cb.resize(out_img_y.size, PIL.Image.BICUBIC)
-    out_img_cr = cr.resize(out_img_y.size, PIL.Image.BICUBIC)
-    out_img = PIL.Image.merge("YCbCr", (out_img_y, out_img_cb, out_img_cr)).convert(
-        "RGB"
-    )
-    return out_img
+	# Restore the image in RGB color space.
+	out_img_y = out_img_y.clip(0, 255)
+	out_img_y = out_img_y.reshape((np.shape(out_img_y)[0], np.shape(out_img_y)[1]))
+	out_img_y = PIL.Image.fromarray(np.uint8(out_img_y), mode="L")
+	out_img_cb = cb.resize(out_img_y.size, PIL.Image.BICUBIC)
+	out_img_cr = cr.resize(out_img_y.size, PIL.Image.BICUBIC)
+	out_img = PIL.Image.merge("YCbCr", (out_img_y, out_img_cb, out_img_cr)).convert(
+		"RGB"
+	)
+	return out_img
 	
 iface = gr.Interface(
 	fn=infer,