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import gradio as gr |
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import os |
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import torch |
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from model import create_effnetb2_model |
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from timeit import default_timer as timer |
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from typing import Tuple, Dict |
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with open("class_names.txt", "r") as f: |
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class_names = [food_name.strip() for food_name in f.readlines()] |
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effnetb2, effnetb2_transforms = create_effnetb2_model( |
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num_classes=101, |
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) |
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effnetb2.load_state_dict( |
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torch.load( |
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f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth", |
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map_location=torch.device("cpu"), |
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) |
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) |
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def predict(img) -> Tuple[Dict, float]: |
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start_time = timer() |
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img = effnetb2_transforms(img).unsqueeze(0) |
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effnetb2.eval() |
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with torch.inference_mode(): |
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pred_probs = torch.softmax(effnetb2(img), dim=1) |
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pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} |
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pred_time = round(timer() - start_time, 5) |
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return pred_labels_and_probs, pred_time |
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title = "FoodVision ππ" |
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description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes]" |
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example_list = [["examples/" + example] for example in os.listdir("examples")] |
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demo = gr.Interface( |
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fn=predict, |
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inputs=gr.Image(type="pil"), |
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outputs=[ |
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gr.Label(num_top_classes=5, label="Predictions"), |
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gr.Number(label="Prediction time (s)"), |
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], |
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examples=example_list, |
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title=title, |
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description=description, |
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) |
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demo.launch() |
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