| import os | |
| import cv2 | |
| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| from huggingface_hub import login, from_pretrained_keras | |
| MODEL_NAME = os.getenv("MODEL_NAME") | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| login(token=HF_TOKEN) | |
| model = from_pretrained_keras(MODEL_NAME) | |
| def classify_image(inp): | |
| inp = cv2.resize(inp, (299, 299)) | |
| inp = np.expand_dims(inp, axis=0) | |
| prediction = model.predict(inp) | |
| pred = tf.nn.sigmoid(prediction).numpy().squeeze() | |
| confidences = {"Application": 1 - pred, "Product": pred.item()} | |
| return confidences | |
| gr.Interface( | |
| fn=classify_image, | |
| inputs=gr.Image(), | |
| outputs=gr.Label(num_top_classes=2), | |
| allow_flagging="never", | |
| ).launch(debug=True, enable_queue=True) | |