Spaces:
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| ### -------------------------------- ### | |
| ### libraries ### | |
| ### -------------------------------- ### | |
| import gradio as gr | |
| import numpy as np | |
| import os | |
| import tensorflow as tf | |
| ### -------------------------------- ### | |
| ### model loading ### | |
| ### -------------------------------- ### | |
| model = tf.keras.models.load_model('model.h5') | |
| ## --------------------------------- ### | |
| ### reading: categories.txt ### | |
| ### -------------------------------- ### | |
| labels = ['please upload categories.txt' for i in range(10)] # placeholder | |
| if os.path.isfile("categories.txt"): | |
| # open categories.txt in read mode | |
| categories = open("categories.txt", "r") | |
| labels = categories.readline().split() | |
| ## --------------------------------- ### | |
| ### page description ### | |
| ### -------------------------------- ### | |
| title = "Cast parts: Deffective or Okay?" | |
| description = "A Hugging Space demo created by datasith!" | |
| article = \ | |
| ''' | |
| #### Deffective or Okay? | |
| Demo app including a binary classification model for casted parts | |
| This is a test project to get familiar with Hugging Face! | |
| The space includes the necessary files for everything to run smoothly on HF's Spaces: | |
| - app.py | |
| - categories.txt | |
| - model.h5 (TensorFlow/Keras) | |
| - requirements.txt | |
| - README.md | |
| - defective.jpg (def_front example) | |
| - okay.jpg (ok_front example) | |
| The data used to train the model is available as a | |
| [Kaggle dataset](https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product). | |
| The space was inspired by @Isabel's wonderful | |
| [cat or pug](https://huggingface.co/spaces/isabel/pug-or-cat-image-classifier) one. | |
| If you enjoy my work feel free to follow me here on HF and/or on: | |
| - [GitHub](https://github.com/datasith) | |
| - [Kaggle](https://kaggle.com/datasith) | |
| - [Twitter](https://twitter.com/datasith) | |
| - [LinkedIn](https://linkedin.com/in/datasith) | |
| Either way, enjoy! | |
| ''' | |
| ### -------------------------------- ### | |
| ### interface creation ### | |
| ### -------------------------------- ### | |
| samples = ['defective.jpeg', 'okay.jpeg'] | |
| def preprocess(image): | |
| img_grayscale = image[:,:,1] | |
| img_array = tf.keras.utils.img_to_array(img_grayscale) | |
| img_array = tf.expand_dims(img_array, 0) | |
| # image = np.array(image) / 255 | |
| # image = np.expand_dims(image, axis=0) | |
| return img_array | |
| def predict_image(image): | |
| # pred = model.predict(preprocess(image)) | |
| # results = {} | |
| # for row in pred: | |
| # for idx, item in enumerate(row): | |
| # results[labels[idx]] = float(item) | |
| predictions = model.predict(preprocess(image)) | |
| scores = tf.nn.softmax(predictions[0]) | |
| results = {} | |
| for idx, res in enumerate(scores): | |
| results[labels[idx]] = float(res) | |
| return results | |
| # generate img input and text label output | |
| image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here") | |
| label = gr.outputs.Label(num_top_classes=len(labels)) | |
| # generate and launch interface | |
| interface = gr.Interface(fn=predict_image, inputs=image, | |
| outputs=label, article=article, theme='default', | |
| title=title, allow_flagging='never', description=description, | |
| examples=samples) | |
| interface.launch() | |