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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
import numpy as np
from model import create_gadgets_model
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
class_names = ["Headphones","Laptop","Mobile"]
### 2. Model and transforms preparation ###
# Create Gadget model
gadget, gadget_transforms = create_gadgets_model(
num_classes=3, # len(class_names) would also work
)
# Load saved weights
gadget.load_state_dict(
torch.load(
f="Gadgets_model_save.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()
threshold=0.87
# Transform the target image and add a batch dimension
img = gadget_transforms(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
gadget.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = torch.softmax(gadget(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)
y=(pred_probs>threshold).float()
pred_prob=y.long()
pred_prob=pred_prob.numpy()
x=np.count_nonzero(pred_prob)
if x==0:
pred_labels_and_probs={"Unknown Images found...Please Provide Images of Headphone,Mobile or laptop":0}
else:
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 = "Gadgets Classifier 📱🖥🎧"
description = "An computer vision model to classify images of Gadgets as Headphone, Laptop and Mobile."
article = "Created by Vishal Jadhav (www.linkedin.com/in/vishaljadhav1855)"
# 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=["Headphones17.jpg","Image_16.jpg","Image_20.jpg"],
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch()
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