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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) |