Spaces:
Runtime error
Runtime error
File size: 2,572 Bytes
4ce6817 |
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 torchvision
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 transdorms preparation ###
effnetb2, effnetb2_transforms = create_effnetb2_model()
# Load save weights
effnetb2.load_state_dict(
torch.load(data = "09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
map_location = torch.device("cpu"))) # Load the model to the CPU))
### 3. Predict function ###
def predict(img) -> Tuple[Dict, float]:
#Start timer
start_time = timer()
# Transform the input image for use with EffNetB2
img = effnetb2_transforms(img).unsqueeze(0)
# Put the model in eval mode, make prediction
effnetb2.eval()
with torch.inference_mode():
# Pass transformed image trough the model abd turn the prediction logits into prediction probs
pred_probs = torch.softmax(effnetb2(img), dim = 1)
# Create a prediction label and prediction probability dictionary
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names)) }
# Calculate pre time
end_time = timer()
pred_time = round(end_time -start_time, 4)
# return pred dict and pred time
return pred_labels_and_probs, pred_time
# Create example list
example_list = [["examples/"+example] for example in os.listdir("examples")]
example_list
### 4. Gradio App
# Create title, description and article strings
title = "FoodVision Mini 🍕🥩🍣"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
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=3, label="Predictions"), # what are the outputs?
gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch(debug=False, # print errors locally?
share=True) # generate a publically shareable URL?
|