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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from model import create_resnet50_model | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| # Setup class names | |
| class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] | |
| ### 2. Model and transforms preparation ### | |
| # Create model | |
| resnet50, resnet50_transforms = create_resnet50_model(num_classes=36, | |
| seed=42) | |
| # Load saved weights | |
| resnet50.load_state_dict( | |
| torch.load( | |
| f="AMS.pth", | |
| map_location=torch.device("cpu"), # load to CPU | |
| ) | |
| ) | |
| ### 3. Predict function ### | |
| # Create predict function | |
| def predict(img) -> Tuple[Dict, float]: | |
| """Transforms and performs a prediction on img and returns prediction and time taken. | |
| """ | |
| # Start the timer | |
| start_time = timer() | |
| img = img.convert('RGB') | |
| # Transform the target image using the ResNet50 transforms | |
| img = resnet50_transforms(img).unsqueeze(0) | |
| # Put the ResNet50 model into evaluation mode | |
| resnet50.eval() | |
| with torch.inference_mode(): | |
| # Pass the transformed image through the model and obtain the prediction logits | |
| pred_logits = resnet50(img) | |
| # Convert the prediction logits to probabilities using softmax | |
| pred_probs = torch.softmax(pred_logits, dim=1) | |
| # Create a prediction label and prediction probability dictionary for each prediction class | |
| pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} | |
| # Calculate the prediction time | |
| pred_time = round(timer() - start_time, 5) | |
| # Return the prediction dictionary and prediction time | |
| return pred_labels_and_probs, pred_time | |
| ### 4. Gradio app ### | |
| import gradio as gr | |
| # Create title, description and article strings | |
| title = "AMERICA SIGN LAGNGUAGE" | |
| description = "An resnet50 feature extractor computer vision model to classify american sign language ." | |
| #article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." | |
| # Create the Gradio demo | |
| demo = gr.Interface(fn=predict, # mapping function from input to output | |
| inputs=gr.Image(type="pil"), # what are the inputs? | |
| outputs=[gr.Label(num_top_classes=5, label="Predictions"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs, | |
| title=title, | |
| description=description, | |
| ) | |
| # Launch the demo! | |
| demo.launch() | |