| from keras.models import model_from_json | |
| import cv2 | |
| import numpy as np | |
| class_names = ['Selfie','Non-Selfie'] | |
| f = open("model_cnn1.json",'r+') | |
| json_string = f.read() | |
| f.close() | |
| model = model_from_json(json_string) | |
| model.load_weights('model_cnn1.h5') | |
| model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) | |
| def predict_image(img): | |
| prediction = model.predict(img.reshape(-1,100,100,3))[0] | |
| return {class_names[i]: float(prediction[i]) for i in range(2)} | |
| import gradio as gr | |
| image = gr.inputs.Image(shape=(100,100)) | |
| label = gr.outputs.Label(num_top_classes=5) | |
| gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(debug='True',share='True') | |