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| from fastai.vision.all import * | |
| import gradio as gr | |
| import os | |
| # Load the learner model | |
| learn_inf = load_learner("model/model.pkl") | |
| # Get the categories from the model | |
| categories = learn_inf.dls.vocab | |
| # Comma separated string of categories with and between the last two | |
| categories_str = ", ".join(categories[:-1]) + " or " + categories[-1] | |
| def classify_image(img): | |
| pred, idx, probs = learn_inf.predict(img) | |
| return {f"{categories[idx]}": float(probs[idx])} | |
| image = gr.Image(sources=["upload"], label="Image") | |
| label = gr.Label() | |
| # Load the images from the examples folder | |
| examples = [] | |
| for file in os.listdir("images/examples"): | |
| if file.endswith(".jpg"): | |
| examples.append([f"images/examples/{file}"]) | |
| iface = gr.Interface( | |
| fn=classify_image, | |
| inputs=image, | |
| outputs=label, | |
| examples=examples, | |
| analytics_enabled=False, | |
| title="<h1>House Room Classifier</h1>", | |
| description='<p style = "font-size: 1.4rem;">This model classifies a photo of a house room as either <strong>' + categories_str + '</strong>.</p><p style = "font-size: 1.2rem;">Upload an image or use the examples below.</p>', | |
| flagging_options=[], | |
| ) | |
| iface.launch() |