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import gradio as gr |
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import os |
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import torch |
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import torchvision |
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import torch.nn as nn |
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from torchvision import transforms |
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from timeit import default_timer as timer |
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from typing import Tuple, Dict |
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class_names = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"] |
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model = torchvision.models.efficientnet_b2() |
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model.classifier = nn.Sequential( |
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nn.Dropout(p=0.3, inplace=True), |
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nn.Linear(in_features=1408, out_features=7), |
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) |
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for param in model.parameters(): |
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param.requires_grad = False |
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model.load_state_dict( |
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torch.load( |
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f="trained_model.pt", |
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map_location=torch.device("cpu"), |
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) |
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) |
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def preprocessImg(img): |
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transform = transforms.Compose([ |
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transforms.Resize((256,256)), |
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transforms.ToTensor(), |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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]) |
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img = transform(img) |
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return img |
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def predict(img) -> Tuple[Dict, float]: |
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"""Transforms and performs a prediction on img and returns prediction and time taken. |
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""" |
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start_time = timer() |
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img = preprocessImg(img).unsqueeze(0) |
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model.eval() |
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with torch.inference_mode(): |
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pred_probs = torch.softmax(model(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 = "Facial Expression Classifier" |
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description = "An EfficientNetB2 feature extractor computer vision model to classify images of facial expressions" |
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article = "for source code you can visit [my github](https://github.com/Bijan-K/Pytorch-Facial-Expression-Recognition)." |
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example_list = [["examples/" + example] for example in os.listdir("examples")] |
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demo = gr.Interface(fn=predict, |
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inputs=gr.Image(type="pil"), |
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outputs=[gr.Label(num_top_classes=3, label="Predictions"), |
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gr.Number(label="Prediction time (s)")], |
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examples=example_list, |
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title=title, |
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description=description, |
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article=article) |
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demo.launch() |
