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:
        raise gr.Error("Please capture an image using the webcam or upload an image.")
    image = image_in_video or image_in_img
    return swapi(image)

app = FaceAnalysis(name='buffalo_l')
app.prepare(ctx_id=0, det_size=(640, 640))
swapper = insightface.model_zoo.get_model('inswapper_128.onnx')


def swapi(imagen):
    # Use the uploaded image to extract features
    img_user = cv2.imread(imagen)
    faces_user = app.get(img_user)

    # Use another image "background1" for modifications
    img_background = cv2.imread('background1.jpg')
    faces_background = app.get(img_background)

    # Assuming the user image has a face and we are using its features
    source_face = faces_user[0]

    # Apply modifications to the "background" image
    res = img_background.copy()
    for face in faces_background:
        res = swapper.get(res, face, source_face, paste_back=True)

    # Convert from BGR to RGB
    res_rgb = cv2.cvtColor(res, cv2.COLOR_BGR2RGB)

    return res_rgb



with gr.Blocks() as blocks:
    gr.Markdown("### Capture Image Using WebCam or Upload") 

    with gr.Row():
        with gr.Column():
            image_or_file_opt = gr.Radio(["webcam", "file"], value="webcam",
                                         label="How would you like to upload your image?")
            image_in_video = gr.Image(source="webcam", type="filepath")
            image_in_img = gr.Image(source="upload", visible=False, type="filepath")

            # Update visibility based on selection
            def toggle(choice):
                if choice == "webcam":
                    return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
                else:
                    return gr.update(visible=False, value=None), gr.update(visible=True, value=None)

            image_or_file_opt.change(fn=toggle, inputs=[image_or_file_opt],
                                     outputs=[image_in_video, image_in_img], queue=False, show_progress=False)
        with gr.Column():
            image_out = gr.Image()

    run_btn = gr.Button("Run")
    run_btn.click(fn=predict, inputs=[image_in_img, image_in_video], outputs=[image_out])
    gr.Examples(fn=predict, examples=[], inputs=[image_in_img, image_in_video], outputs=[image_out])

blocks.queue()
blocks.launch(debug=True)