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| ### 1. Imports and class names setup | |
| import gradio as gr | |
| import os | |
| import torch | |
| import gradio as gr | |
| import torchvision | |
| from model import create_effnetb2_model | |
| from timeit import default_timer as timer | |
| from typing import Dict, Tuple | |
| class_names = ['butterfly', 'cat', 'chicken', 'cow', 'dog', | |
| 'elephant', 'horse', 'sheep', 'spider', 'squirrel'] | |
| ### 2. Model and transforms prepartaion ### | |
| effnetb2, effnetb2_transforms = create_effnetb2_model() | |
| # Loade the save weights. | |
| effnetb2.load_state_dict(torch.load(f = "effnetb2_model.pth", | |
| map_location = torch.device("cpu"))) | |
| ### 3. Predict Function ### | |
| effnetb2 = effnetb2.to('cpu') | |
| 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() | |
| # Transform the target image and add a batch dimension | |
| img = effnetb2_transforms(img).unsqueeze(0) | |
| # Put model into evaluation mode and turn on inference mode | |
| effnetb2.eval() | |
| with torch.inference_mode(): | |
| # Pass the transformed image through the model and turn the prediction logits into prediction probabilities | |
| pred_probs = torch.softmax(effnetb2(img), dim=1) | |
| # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter) | |
| 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. ### | |
| # Create title, description and article strings | |
| title = "AnimalsClassification " | |
| description = """An EfficientNetB2 feature extractor computer vision model to classify images of ten different animals. | |
| Curently the app can identify 10 diffferent animal species which is the following. | |
| 1. Dog | |
| 2. Cat | |
| 3. Horse | |
| 4. Butterfly | |
| 5. Cow | |
| 6. Chicken | |
| 7. Sheep | |
| 8. Squirrel | |
| 9. Elephant | |
| 10. Spider""" | |
| article = "ModelDeployment" | |
| # Create example list. | |
| example_list = [["examples/" + example] for example in os.listdir('examples')] | |
| # 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=3, label="Predictions"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article) | |
| # Launch the demo! | |
| demo.launch(debug=False, # print errors locally? | |
| share=True) # generate a publically shareable URL? | |