Update app.py

app.py

@@ -1,60 +1,31 @@
 import gradio as gr
-import numpy as np
 import cv2
-import numpy as np
-import os
-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
 
-
-
-# Load your trained model
-#model = tf.keras.models.load_model('path_to_your_model.h5')
-
-def predict_gender(image):
-    # Convert image to format expected by your model & preprocess
-    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-    img = cv2.resize(img, (224, 224))  # Example size
-    img = img / 255.0  # Normalizing
-    img = np.expand_dims(img, axis=0)
-
-    prediction = model.predict(img)
-
-    # Assuming binary classification with a single output neuron
-    return "Male" if prediction[0] < 0.5 else "Female"
-
-
 def predict(image_in_video, image_in_img):
     if image_in_video == None and image_in_img == None:
         raise gr.Error("Please capture an image using the webcam or upload an image.")
+    
     image = image_in_video or image_in_img
-    return image
+    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    
+    faces = app.get(img)
+    source_face = faces[0]
+    bbox = source_face['bbox']
+    bbox = [int(b) for b in bbox]
+
+    res = img.copy()
+    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
 
 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')
 
-img = ins_get_image('t1')
-faces = app.get(img)
-
-source_face = faces[0]
-bbox = source_face['bbox']
-bbox = [int(b) for b in bbox]
-
-source_face = faces[0]
-bbox = source_face['bbox']
-bbox = [int(b) for b in bbox]
-
-res = img.copy()
-for face in faces:
-  res = swapper.get(res, face, source_face, paste_back=True)
-
-    
-
 with gr.Blocks() as blocks:
     gr.Markdown("### Capture Image Using WebCam or Upload") 
 
@@ -82,4 +53,5 @@ 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()
+