Update app.py

app.py

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+import streamlit as st
+import numpy as np
+import cv2
+import insightface
+from insightface.app import FaceAnalysis
+import tempfile
+import os
+
+# Initialize face analysis and load model
+app = FaceAnalysis(name='buffalo_l')
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+# Load the face swapper model
+swapper = insightface.model_zoo.get_model('inswapper_128.onnx', download=False, download_zip=False)
+
+def swap_faces_in_video(image, video):
+    """
+    Swaps faces from a source image with faces detected in a video and returns the path to the output video file.
+    
+    image: Source image (as an array)
+    video: Path to the input video file
+    """
+    source_faces = app.get(image)
+
+    if len(source_faces) == 0:
+        st.error("No face detected in the source image.")
+        return None
+
+    source_face = source_faces[0]
+
+    # Create a temporary file to save the output video
+    output_path = tempfile.mktemp(suffix='.avi')
+
+    # Open the video file
+    cap = cv2.VideoCapture(video)
+
+    # Get video properties for output
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    # Define the codec and create a VideoWriter object
+    fourcc = cv2.VideoWriter_fourcc(*'XVID')
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break  # Exit if the video is finished
+
+        # Detect faces in the current frame
+        target_faces = app.get(frame)
+
+        # Create a copy of the frame for the result
+        result_frame = frame.copy()
+
+        # Swap faces for each detected face in the video frame
+        for target_face in target_faces:
+            result_frame = swapper.get(result_frame, target_face, source_face, paste_back=True)
+
+        # Write the result frame to the output video
+        out.write(result_frame)
+
+    # Release resources
+    cap.release()
+    out.release()
+    
+    return output_path
+
+# Streamlit UI
+st.title("Face Swapper in Video")
+st.write("Upload an image and a video to swap faces.")
+
+# File uploader for the source image
+image_file = st.file_uploader("Upload Source Image", type=["jpg", "jpeg", "png"])
+
+# File uploader for the video
+video_file = st.file_uploader("Upload Video", type=["mp4", "avi"])
+
+if st.button("Swap Faces"):
+    if image_file is not None and video_file is not None:
+        # Read the source image
+        source_image = cv2.imdecode(np.frombuffer(image_file.read(), np.uint8), cv2.IMREAD_COLOR)
+        
+        # Save the uploaded video temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_video:
+            tmp_video.write(video_file.read())
+            tmp_video_path = tmp_video.name
+
+        # Show a spinner while processing
+        with st.spinner("Processing video..."):
+            output_video_path = swap_faces_in_video(source_image, tmp_video_path)
+
+        if output_video_path:
+            st.success("Face swapping completed!")
+            st.video(output_video_path)
+            os.remove(tmp_video_path)  # Clean up temporary video file
+            os.remove(output_video_path)  # Clean up output video file if needed
+    else:
+        st.error("Please upload both an image and a video.")