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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()
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