Update app.py

app.py

@@ -1,7 +1,11 @@
+import numpy as np
 import gradio as gr
+import glob
 import cv2
+import matplotlib.pyplot as plt
 import insightface
 from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
 
 def predict(image_in_video, image_in_img):
     if image_in_video == None and image_in_img == None:
@@ -11,10 +15,22 @@ def predict(image_in_video, image_in_img):
 
 app = FaceAnalysis(name='buffalo_l')
 app.prepare(ctx_id=0, det_size=(640, 640))
-swapper = insightface.model_zoo.get_model('inswapper_128.onnx', download='FALSE', download_zip= 'FALSE')
+swapper = insightface.model_zoo.get_model('inswapper_128.onnx')
+
 
 def swapi(imagen):
-    img = cv2.cvtColor(np.array(imagen), cv2.COLOR_RGB2BGR)  # Convert image from RGB to BGR format
+    # Check if the input is a file path (str) or an image array (numpy.ndarray)
+    if isinstance(imagen, str):
+        img = cv2.imread(imagen)
+    else:
+        img = np.array(imagen)
+
+    if img is None:
+        raise ValueError("Failed to read the image.")
+
+    if img.shape[-1] == 3:  # Check if the image is RGB or grayscale
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
     faces = app.get(img)
 
     if not faces:
@@ -28,7 +44,8 @@ def swapi(imagen):
     for face in faces:
         res = swapper.get(res, face, source_face, paste_back=True)
 
-    return res[:, :, [2, 1, 0]]  # Convert BGR to RGB for Gradio display
+    return res  
+
 
 with gr.Blocks() as blocks:
     gr.Markdown("### Capture Image Using WebCam or Upload") 
@@ -57,4 +74,4 @@ with gr.Blocks() as blocks:
     gr.Examples(fn=predict, examples=[], inputs=[image_in_img, image_in_video], outputs=[image_out])
 
 blocks.queue()
-blocks.launch()
+blocks.launch(debug=True)