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b0a9fa4 caf8336 b0a9fa4 7daaf98 b0a9fa4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | ### 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()
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