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import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from huggingface_hub import hf_hub_download
import numpy as np
import os
from PIL import Image
MODEL_REPO = "zotthytt12/vegetable-classifier"
MODEL_FILENAME = "model/veg_model.h5"
# pobierz model z Hugging Face Hub
print("Pobieranie modelu...")
model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
print("Model pobrany, ładowanie...")
model = tf.keras.models.load_model(model_path)
print("Model załadowany.")
CLASS_NAMES = ['Bean', 'Bitter_Gourd', 'Bottle_Gourd', 'Brinjal', 'Broccoli',
'Cabbage', 'Capsicum', 'Carrot', 'Cauliflower', 'Cucumber',
'Papaya', 'Potato', 'Pumpkin', 'Radish', 'Tomato']
IMG_SIZE = (128, 128)
def predict(img_path):
img = Image.open(img_path)
img = img.resize(IMG_SIZE)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0) / 255.0
preds = model.predict(x)
probs = preds[0]
return {CLASS_NAMES[i]: float(probs[i]) for i in range(len(CLASS_NAMES))}
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="filepath"),
outputs=gr.Label(num_top_classes=3),
title="Vegetable Classifier",
description="Wgraj zdjęcie warzywa, a model powie co to jest.")
if __name__ == "__main__":
iface.launch(show_error=True)
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