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import gradio as gr | |
import numpy as np | |
from tensorflow.keras.models import load_model | |
from keras.applications.imagenet_utils import preprocess_input | |
from tensorflow.keras.preprocessing import image | |
import requests | |
from PIL import Image | |
import io | |
model = load_model('vgg_model.h5') | |
def infer(image_url): | |
# Load and preprocess the image | |
response = requests.get(image_url) | |
image = Image.open(io.BytesIO(response.content)) | |
image = image.resize((224, 224)) # Resize image to match input size of VGG16 | |
x = image | |
x = np.expand_dims(x, axis=0) | |
x = preprocess_input(x) | |
# Perform inference | |
preds = model.predict(x) | |
result = preds[0][0] | |
# Determine the label | |
if result < preds[0][1]: | |
label = "messy" | |
else: | |
label = "clean" | |
return label | |
# Create a Gradio interface | |
iface = gr.Interface(fn=infer, inputs="text", outputs="text", title="Image Classifier") | |
iface.launch() | |