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Runtime error
Adding application file, requirements file as well as examples
Browse files- app.py +92 -0
- examples/sample1.jpg +0 -0
- examples/sample2.jpg +0 -0
- requirements.txt +2 -0
app.py
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import numpy as np
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import gradio as gr
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import tensorflow as tf
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from tensorflow import keras
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from huggingface_hub import from_pretrained_keras
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IMAGE_SIZE = (256, 256)
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# Load model from HF
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model = from_pretrained_keras(
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pretrained_model_name_or_path="fbadine/image-spam-detection"
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)
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# This is the predict function that takes as input an array-like-image and produces
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# the probabilities that this image is either spam or ham
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def predict(image):
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# Resize image
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resized_image = keras.layers.Resizing(
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IMAGE_SIZE[0],
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IMAGE_SIZE[1],
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interpolation="bilinear",
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crop_to_aspect_ratio=True
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)(image)
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resized_image = tf.expand_dims(resized_image, axis=0)
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# Predict
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pred = model.predict(resized_image)
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prob = pred[0][0]
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scoring_output = {
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"Spam": prob,
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"Ham": 1 - prob
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}
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return scoring_output
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# Clear Input and outpout
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def clear_inputs_and_outputs():
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return [None, None, None]
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# Main function
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if __name__ == "__main__":
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demo = gr.Blocks()
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with demo:
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gr.Markdown(
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"""
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<center><h1>Image Spam Detection</h1></center> \
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This space is a demo of a proof of concept POC Image Spam Detection<br> \
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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>
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"""
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)
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with gr.Row():
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with gr.Column():
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# Input
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image_input = gr.Image(
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shape=IMAGE_SIZE,
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source="upload",
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label="Upload an Image"
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)
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with gr.Row():
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clr_btn = gr.Button(value="Clear", variant="secondary")
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prd_btn = gr.Button(value="Predict")
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with gr.Column():
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# Output
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lbl_output = gr.Label(label="Prediction")
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clr_btn.click(
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fn=clear_inputs_and_outputs,
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inputs=[],
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outputs=[image_input, lbl_output, plt_output],
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)
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prd_btn.click(
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fn=predict,
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inputs=[image_input],
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outputs=[lbl_output, plt_output],
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)
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gr.Examples(
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examples=[
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os.path.join(os.path.curdir, "examples", "sample1.jpg"),
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os.path.join(os.path.curdir, "examples", "sample2.jpg"),
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],
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inputs=image_input,
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outputs=lbl_output,
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fn=predict,
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cache_examples=True,
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)
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demo.launch(debug=True, share=True)
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examples/sample1.jpg
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examples/sample2.jpg
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requirements.txt
ADDED
@@ -0,0 +1,2 @@
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numpy
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tensorflow
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