Added Gradio file and requirements

app.py

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+import os
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+# 1) download your SavedModel from the Hub
+repo_id = "NathanSegers/masterclass-2025"
+model_dir = hf_hub_download(repo_id, filename="model_saved", repo_type="model", local_dir=".")
+
+# 2) load it
+model = tf.keras.models.load_model(model_dir)
+
+# 3) simple preprocess + predict
+CLASS_NAMES = ["cat","dog","panda"]
+def predict(image):
+    img = tf.image.resize(image, (64,64)) / 255.0
+    probs = model.predict(img[None,...])[0]
+    return {c: float(p) for c,p in zip(CLASS_NAMES, probs)}
+
+# 4) launch Gradio
+gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(shape=(224,224)), 
+    outputs=gr.Label(num_top_classes=3)
+).launch()

requirements.txt

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+tensorflow>=2.19.0
+gradio
+huggingface_hub