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import gradio as gr | |
import os | |
import torch | |
from timeit import default_timer as timer | |
from typing import Tuple, Dict | |
from torchvision import transforms | |
from models.model import TinyVGG | |
# Setup class names | |
with open("models/labels.txt", "r") as f: # reading them in from class_names.txt | |
class_names = [urdu_character.strip() for urdu_character in f.readlines()] | |
### 2. Model and transforms preparation ### | |
model_transform = transforms.Compose([ | |
transforms.Resize((64, 64)), | |
transforms.Grayscale(num_output_channels=1), | |
transforms.ToTensor(), | |
]) | |
# Recreate an instance of TinyVGG | |
model_0 = TinyVGG(input_shape=1, # number of color channels (3 for RGB) | |
hidden_units=10, | |
output_shape=len(class_names)) | |
# Load saved weights | |
model_0.load_state_dict( | |
torch.load( | |
f='models/saved/01_pytorch_workflow_model_0.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() | |
# Transform the target image and add a batch dimension | |
img_transform = model_transform(img).unsqueeze(dim=0) | |
# Put model into evaluation mode and turn on inference mode | |
model_0.eval() | |
with torch.inference_mode(): | |
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities | |
pred_probs = torch.softmax(model_0(img_transform), 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. Gradio app ### | |
# Create title, description and article strings | |
title = "Urdu Characters Vision βͺοΈπ" | |
description = "An TinyVGG feature extractor computer vision model to classify images of urdu characters [23 different classes]" | |
# Create examples list from "static/" directory | |
example_list = [["examples/" + example] for example in os.listdir("examples")] | |
# Create Gradio interface | |
demo = gr.Interface( | |
fn=predict, | |
inputs=gr.Image(type="pil"), | |
outputs=[ | |
gr.Label(num_top_classes=5, label="Predictions"), | |
gr.Number(label="Prediction time (s)"), | |
], | |
examples=example_list, | |
title=title, | |
description=description, | |
) | |
# Launch the app! | |
demo.launch() |