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| 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() |