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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()
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