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 upload an image.")
    image = image_in_video or image_in_img
    return image

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)

with gr.Blocks() as blocks:
    gr.Markdown("### 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")

            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()