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| import os | |
| import numpy as np | |
| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from huggingface_hub import from_pretrained_keras | |
| IMAGE_SIZE = (256, 256) | |
| # Load model from HF | |
| model = from_pretrained_keras( | |
| pretrained_model_name_or_path="fbadine/image-spam-detection" | |
| ) | |
| # This is the predict function that takes as input an array-like-image and produces | |
| # the probabilities that this image is either spam or ham | |
| def predict(image): | |
| # Add the batch axis | |
| image = tf.expand_dims(image, axis=0) | |
| # Predict | |
| pred = model.predict(image) | |
| prob = float(pred[0][0]) | |
| scoring_output = { | |
| "Spam": prob, | |
| "Ham": 1 - prob | |
| } | |
| return scoring_output | |
| # Clear Input and outpout | |
| def clear_inputs_and_outputs(): | |
| return [None, None, None] | |
| # Main function | |
| if __name__ == "__main__": | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown( | |
| """ | |
| <center><h1>Image Spam Detection</h1></center> \ | |
| This space is a demo of a proof of concept POC Image Spam Detection<br> \ | |
| In this space, you can upload an image to check if it's spam or not or you can use of the provided samples <br><br> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Input | |
| image_input = gr.Image( | |
| shape=(256,256), | |
| source="upload", | |
| label="Upload an Image" | |
| ) | |
| with gr.Row(): | |
| clr_btn = gr.Button(value="Clear", variant="secondary") | |
| prd_btn = gr.Button(value="Predict") | |
| with gr.Column(): | |
| # Output | |
| lbl_output = gr.Label(label="Prediction") | |
| clr_btn.click( | |
| fn=clear_inputs_and_outputs, | |
| inputs=[], | |
| outputs=[image_input, lbl_output], | |
| ) | |
| prd_btn.click( | |
| fn=predict, | |
| inputs=[image_input], | |
| outputs=[lbl_output], | |
| ) | |
| gr.Examples( | |
| #examples=[os.path.join(os.path.dirname(__file__), "ShowLetter.jpg")], | |
| examples=[ | |
| os.path.join(os.path.curdir, "examples", "sample1.jpg"), | |
| os.path.join(os.path.curdir, "examples", "sample2.jpg"), | |
| os.path.join(os.path.curdir, "examples", "sample3.jpg"), | |
| os.path.join(os.path.curdir, "examples", "sample4.png") | |
| ], | |
| inputs=image_input, | |
| outputs=lbl_output, | |
| fn=predict, | |
| cache_examples=True, | |
| ) | |
| demo.launch(debug=True, share=False) |