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---
tags:
- image-classification
- image
- data-classification
- image-categorisation
- data-categoriasation
pipeline_tag: image-classification
language:
- de
- en
---
# Model Card for Model ID
This model is a Jewelry Classifier. Just upload an image of one of the categories named below and the model will classify it for you.
- Pendant
- Bracelet
- Chain
- Earring
- Ring
- Watch
# How to use?
Before following the steps below, please install these dependencies:
```pyhton
numpy==1.26.4
keras==3.3.3
pillow==10.3.0
```
### Step1: Load the Model (jewelry_classification.h5)
Download the model file from (https://huggingface.co/beyondxlabs/JewelryClassification/resolve/main/jewelry_classification.h5?download=true) and then use the below code snippet to load the model.
```python
model = load_model('jewelry_classification_model.h5')
class_labels = ['Anhänger', 'Armbänder', 'Ketten', 'Ohrringe', 'Ringe', 'Uhren']
```
### Step 2: Preprocess your images
Before giving images to the model, that image needs to be preprocessed to get a numpy array. You can just use the below function.
```python
def preprocess_image(img):
try:
img = Image.open(img)
img = img.resize((224, 224))
img_array = img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array.astype(np.float32) / 255.0
return img_array
except Exception as error:
st.error(f"An error occurred during image preprocessing: {error}")
return None
```
### Step 3: Predict the output
In this step the preprocessed image could be given to the model to get the classification. Below is the sample code snippet.
```python
def choose_category(img, is_url=True):
try:
processed_img = preprocess_image(img, is_url)
if processed_img is not None:
preds = model.predict(processed_img)
category = class_labels[np.argmax(preds)]
confidence = np.max(preds)
return category, confidence*100
return 'Other', 0
except Exception as e:
st.error(f"An error occurred during prediction: {e}")
return 'Other', 0
```
### Step 4(optional): Streamlit UI
Use the below snippet to make an UI Application using the model
```python
# UI interface
import streamlit as st
st.title("Jewelry Classification")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg"])
if st.button("Classify"):
if uploaded_file is not None:
category, confidence = choose_category(uploaded_file, is_url=False)
st.write(f"Predicted Category: **{category}** with confidence **{confidence:.2f}%**")
else:
st.error("Please upload an image file.")
```
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