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Runtime error
| 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() | |