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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from model import create_DenseNet121_model | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| # Setup class names | |
| class_names = ['infected', 'notinfected'] | |
| ### 2. Model and transforms preparation ### | |
| # Create an instance of trained DenseNet121 model | |
| Dense121, transform = create_DenseNet121_model() | |
| ### 3. Predict function ### | |
| # Create predict function | |
| def predict(img) -> Tuple[Dict, float]: | |
| """ | |
| Transforms and performs a prediction on img then returns prediction and time taken | |
| """ | |
| # Start the timer | |
| start_time = timer() | |
| # Transform the target image and add a batch dimension | |
| img = transform(img).unsqueeze(0) | |
| # Put model into evaluation mode and turn on the inference mode | |
| Dense121.eval() | |
| with torch.inference_mode(): | |
| # Pass the transformed image through the model and turn the prediction logit intp prediction probability | |
| pred_logit = Dense121(img).squeeze() | |
| pred_prob = torch.sigmoid(pred_logit) | |
| pred_label = torch.round(pred_prob) | |
| pred_label = pred_label.type(torch.int64) | |
| pred_class = class_names[pred_label.cpu()] | |
| # pred_prob = float(pred_prob) # This line and next one are for formatting the pred_prob to print only 4 decimal places | |
| # pred_prob = round(pred_prob, 4) | |
| # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter) | |
| pred_label = pred_class | |
| # Calculate the prediction time | |
| pred_time = round(timer() - start_time, 5) | |
| # Return the prediction dictionary and prediction time | |
| return pred_label, pred_time | |
| ### 4. Gradio app ### | |
| # Create title and description strings | |
| title = "PCOS Detector in Ultrasound Images" | |
| description = "A DenseNet121 feature extractor computer vision model trained from scratch to classify ultrasound images of ovaries into PCOS infected or not infected." | |
| #article= "Code implementation available at [GitHub](https://github.com/haidary99?tab=repositories)" | |
| # Create examples list from "examples/" directory | |
| 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"), | |
| outputs=[gr.Label(label="Model Prediction"), | |
| gr.Number(label="Prediction time (s)")], | |
| # Create examples list from "examples/" directory | |
| examples=example_list, | |
| title=title, | |
| description=description) | |
| #article=article) | |
| # Launch the demo | |
| demo.launch() | |