Spaces:
Sleeping
Sleeping
File size: 3,144 Bytes
d1ffd11 9d84828 d1ffd11 5990ce9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import gradio as gr
import numpy as np
import tensorflow as tf
import cv2
# 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(video_in, image_in_video, image_in_img):
if video_in == None and image_in_video == None and image_in_img == None:
raise gr.Error("Please upload a video or image.")
if image_in_video or image_in_img:
print("image", image_in_video, image_in_img)
image = image_in_video or image_in_img
return image
return video_in
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("### Video or Image? WebCam or Upload?""")
with gr.Tab("Video") as tab:
with gr.Row():
with gr.Column():
video_or_file_opt = gr.Radio(["webcam", "upload"], value="webcam",
label="How would you like to upload your video?")
video_in = gr.Video(source="webcam", include_audio=False)
video_or_file_opt.change(fn=lambda s: gr.update(source=s, value=None), inputs=video_or_file_opt,
outputs=video_in, queue=False, show_progress=False)
with gr.Column():
video_out = gr.Video()
run_btn = gr.Button("Run")
run_btn.click(fn=predict, inputs=[video_in], outputs=[video_out])
gr.Examples(fn=predict, examples=[], inputs=[
video_in], outputs=[video_out])
with gr.Tab("Image"):
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() |