initial commit for gradio space

README.md

@@ -1,12 +1,13 @@
 ---
-title: CTAIAnimalClassifier
-emoji: 🐢
-colorFrom: green
-colorTo: purple
+title: Animal Classifier Space
+emoji: ⚡
+colorFrom: gray
+colorTo: blue
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 5.29.0
 app_file: app.py
 pinned: false
+short_description: Masterclass 2025
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,28 @@
+import tensorflow as tf
+import gradio as gr
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# 1) download your SavedModel from the Hub
+repo_id = "CTAI2025"
+hf_hub_download(repo_id, filename="config.json", repo_type="model", local_dir="./model")
+hf_hub_download(repo_id, filename="metadata.json", repo_type="model", local_dir="./model")
+hf_hub_download(repo_id, filename="model.weights.h5", repo_type="model", local_dir="./model")
+
+# 2) load it
+model = tf.keras.models.load_model("./model")
+
+# 3) simple preprocess + predict
+CLASS_NAMES = ["cat","dog","panda"]
+def predict(image):
+    resized_image = tf.image.resize(image, (64,64))
+    images_to_predict = np.expand_dims(np.array(resized_image), axis=0)
+    probs = model.predict(images_to_predict)[0]
+    return {c: float(p) for c,p in zip(CLASS_NAMES, probs)}
+
+# 4) launch Gradio
+gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(), 
+    outputs=gr.Label(num_top_classes=3)
+).launch(share=True)

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow>=2.19.0
+gradio
+huggingface_hub