Adding application file, requirements file as well as examples

app.py

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+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):
+    # Resize image
+    resized_image = keras.layers.Resizing(
+        IMAGE_SIZE[0],
+        IMAGE_SIZE[1],
+        interpolation="bilinear",
+        crop_to_aspect_ratio=True
+    )(image)
+    resized_image = tf.expand_dims(resized_image, axis=0)
+
+    # Predict
+    pred = model.predict(resized_image)
+    prob = 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=IMAGE_SIZE,
+                    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, plt_output],
+        )
+        prd_btn.click(
+            fn=predict,
+            inputs=[image_input],
+            outputs=[lbl_output, plt_output],
+        )
+
+        gr.Examples(
+            examples=[
+                os.path.join(os.path.curdir, "examples", "sample1.jpg"),
+                os.path.join(os.path.curdir, "examples", "sample2.jpg"),
+            ],
+            inputs=image_input,
+            outputs=lbl_output,
+            fn=predict,
+            cache_examples=True,
+        )
+
+    demo.launch(debug=True, share=True)

requirements.txt

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+numpy
+tensorflow