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Runtime error
| import gradio as gr | |
| import os | |
| from PIL import ImageOps | |
| from torch.utils.benchmark import timer | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| def processImage(img, text): | |
| """Transforms and performs a prediction on img and returns prediction and time taken. | |
| """ | |
| # Start the timer | |
| start_time = timer() | |
| inverted_img = ImageOps.invert(img) | |
| text = "Hello" + text | |
| pred_labels_and_probs = "Pred props" | |
| # Calculate the prediction time | |
| pred_time = round(timer() - start_time, 5) | |
| # Return the prediction dictionary and prediction time | |
| return inverted_img, pred_labels_and_probs, pred_time, text | |
| # Create title, description and article strings | |
| title = "FoodRecognition 🍕🥩🍣" | |
| description = "An EfficientNetB2 model to classify images of food as pizza, steak or sushi." | |
| article = "Created at my." | |
| # Create the Gradio demo | |
| demo = gr.Interface(fn=processImage, # mapping function from input to output | |
| inputs=[gr.Image(type="pil"), gr.Textbox()], | |
| outputs=[gr.Image(type="pil"), gr.Label(label="Process image"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)"), gr.Textbox(label="Result")], | |
| # our fn has two outputs, therefore we have two outputs | |
| # examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=example_list) | |
| # Launch the demo! | |
| demo.launch(debug=False, # print errors locally? | |
| share=True) # generate a publically shareable URL? | |