Upload 3 files

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model("model.h5")
+
+# Class labels (Italian animal names)
+class_names = [
+    "cane", "cavallo", "elefante", "farfalla", "gallina",
+    "gatto", "mucca", "pecora", "ragno", "scoiattolo"
+]
+
+# Prediction function
+def predict(img):
+    img = img.convert("RGB")
+    img = img.resize((224, 224))
+    img_array = image.img_to_array(img)
+    img_array = preprocess_input(img_array)
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    predictions = model.predict(img_array)[0]
+    return {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Animal Classifier (10 Species)",
+    description="Upload an image of an animal. The model will classify it as one of: cane, cavallo, elefante, farfalla, gallina, gatto, mucca, pecora, ragno, scoiattolo."
+)
+
+if __name__ == "__main__":
+    interface.launch()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7bf4beb3a718712e7dc52f18a6965132ce3f457a608e8598b13b27252d6103ac
+size 30495248

requirements.txt

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+gradio>=4.0.0
+tensorflow>=2.10.0
+numpy
+Pillow