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()