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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from tensorflow.keras import models | |
| from tensorflow.keras.preprocessing.image import load_img | |
| import tensorflow as tf | |
| from hugsvision.inference.TorchVisionClassifierInference import TorchVisionClassifierInference | |
| models_name = [ | |
| "VGG16", | |
| "DenseNet121", | |
| "DenseNet" | |
| ] | |
| # open categories.txt in read mode | |
| categories = open("categories.txt", "r") | |
| labels = categories.readline().split(";") | |
| # create a radio | |
| radio = gr.inputs.Radio(models_name, default="DenseNet121", type="value") | |
| def predict_image(image, model_name): | |
| # model create by keras | |
| if model_name == "DenseNet": | |
| image = np.array(image) / 255 | |
| image = np.expand_dims(image, axis=0) | |
| model = model = models.load_model("./models/" + model_name + "/model.h5") | |
| pred = model.predict(image) | |
| pred = dict((labels[i], "%.2f" % pred[0][i]) for i in range(len(labels))) | |
| # model create by HugsVision | |
| else: | |
| image = Image.fromarray(np.uint8(image)).convert('RGB') | |
| classifier = TorchVisionClassifierInference( | |
| model_path = "./models/" + model_name | |
| ) | |
| pred = classifier.predict_image(img=image, return_str=False) | |
| for key in pred.keys(): | |
| pred[key] = pred[key]/100 | |
| print(pred) | |
| return pred | |
| image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here") | |
| label = gr.outputs.Label(num_top_classes=len(labels)) | |
| samples = [["samples/" + p + ".jpg"] for p in labels] | |
| interface = gr.Interface( | |
| fn=predict_image, | |
| inputs=[image, radio], | |
| outputs=label, | |
| capture_session=True, | |
| allow_flagging=False, | |
| title="🦈 Shark image classifier", | |
| description="Made with HugsVision & ❤️", | |
| examples=samples, | |
| theme=None | |
| ) | |
| interface.launch() |