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### 1. Import and class names setup ###
import gradio as gr
import os
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
class_names = ['pizza', 'steak', 'sushi']
### 2. Model and Transforms perparation ###
effnetb2, effnetb2_transforms = create_effnetb2_model(
num_classes=3)
# Load save weights
effnetb2.load_state_dict(
torch.load(
f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
map_location=torch.device("cpu"),
weights_only=True
)
)
### 3. Predict fucntin ###
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Transform the input image for use with EffNetB2
transform_img = effnetb2_transforms(img).unsqueeze(0)
# Put model into eval mode, main prediction
effnetb2.eval()
with torch.inference_mode():
pred_prob=torch.softmax(effnetb2(transform_img),dim=1)
# Create a prediction label and prediction probability dictionary
pred_labels_and_probs = {class_names[i]:float(pred_prob[0][i]) for i in range(len(class_names))}
# Calculate pred time
time = round(timer()-start_time,4)
# Return pred dict and pred time
return pred_labels_and_probs,time
### 4. Gradio app ###
# Create title , description and article
title = "FoodVision Mini 🍕🥩🍣"
description = " An [EfficinetNetB2](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html) feature extractor computer vision model to classify images as pizza, steak, sushi"
# Create example list
example_list = [["examples/"+example] for example in os.listdir("examples")]
# Create the Graio demo
demo = gr.Interface(fn=predict, # maps inputs to ouputs
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=3,label='Predictions'),
gr.Number(label="Predicition time (s)")],
examples=example_list,
title=title,
description=description,
cache_examples=True)
# Launch the demo!
demo.launch(debug=False )
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