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| import gradio as gr | |
| import torch | |
| import torch.nn.functional as F | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| from constants import MAKES_MODELS, PRICE_BIN_LABELS | |
| model = torch.jit.load("mobilenetv2_432000_calib.pt") | |
| model.eval() | |
| transform = transforms.Compose( | |
| [ | |
| transforms.ToTensor(), | |
| transforms.Normalize( | |
| (0.48145466, 0.4578275, 0.40821073), | |
| (0.26862954, 0.26130258, 0.27577711), | |
| ), | |
| ] | |
| ) | |
| def classify(img: Image.Image): | |
| in_tensor = transform(img)[None] | |
| outputs = model(in_tensor) | |
| price_bins = dict( | |
| zip(PRICE_BIN_LABELS, F.softmax(outputs["price_bin"], dim=-1)[0].tolist()) | |
| ) | |
| make_models = dict( | |
| zip( | |
| ([f"{make} {model}" for make, model in MAKES_MODELS] + ["Unknown"]), | |
| F.softmax(outputs["make_model"], dim=-1)[0].tolist(), | |
| ) | |
| ) | |
| return f"${int(round(outputs['price_median'].item()))}", price_bins, make_models | |
| iface = gr.Interface( | |
| fn=classify, | |
| inputs=gr.Image(shape=(224, 224), type="pil"), | |
| outputs=[ | |
| gr.Text(label="Price Prediction"), | |
| gr.Label(label="Price Bin"), | |
| gr.Label(label="Make/Model"), | |
| ], | |
| ) | |
| iface.launch() | |