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11e60cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import gradio as gr
import numpy as np
import tensorflow as tf
from PIL import Image
# LOAD MODEL (.h5) ✅
model = tf.keras.models.load_model("lite_model.h5", compile=False)
# LABELS
class_names = [
"Didgeridoo", "Tambourine", "Xylophone", "acordian", "alphorn",
"bagpipes", "banjo", "bongo drum", "casaba", "castanets",
"clarinet", "clavichord", "concertina", "drums", "dulcimer",
"flute", "guiro", "guitar", "harmonica", "harp",
"marakas", "ocarina", "piano", "saxaphone", "sitar",
"steel drum", "trombone", "trumpet", "tuba", "violin"
]
IMG_SIZE = (224, 224)
def preprocess_image(image):
image = image.convert("RGB")
image = image.resize(IMG_SIZE)
image = np.array(image) / 255.0
image = np.expand_dims(image, axis=0)
return image
def predict(image):
img = preprocess_image(image)
preds = model.predict(img)[0]
return {class_names[i]: float(preds[i]) for i in range(len(class_names))}
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
title="🎵 Musical Instrument Classifier",
description="Upload an image to predict the instrument",
)
interface.launch() |