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update app.py
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app.py
CHANGED
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### 1. Imports and class names setup ###
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import gradio as gr
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import os
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import torch
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@@ -8,18 +8,18 @@ from timeit import default_timer as timer
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from typing import Tuple, Dict
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# Setup class names
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with open("class_names.txt", "r") as f:
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class_names = [food_name.strip() for food_name in
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### 2. Model and transforms preparation ###
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# Create model
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num_classes=101,
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)
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# Load saved weights
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torch.load(
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f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
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map_location=torch.device("cpu"), # load to CPU
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@@ -34,25 +34,28 @@ def predict(img) -> Tuple[Dict, float]:
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"""
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# Start the timer
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start_time = timer()
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# Transform the target image and add a batch dimension
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img = effnetb2_transforms(img).unsqueeze(0)
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# Put model into evaluation mode and turn on inference mode
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effnetb2.eval()
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with torch.inference_mode():
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# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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pred_probs = torch.softmax(effnetb2(img), dim=1)
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# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
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pred_labels_and_probs = {
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# Calculate the prediction time
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pred_time = round(timer() - start_time, 5)
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# Return the prediction dictionary and prediction time
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return pred_labels_and_probs, pred_time
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### 4. Gradio app ###
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# Create title, description and article strings
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@@ -63,7 +66,7 @@ article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io
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# Create examples list from "examples/" directory
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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# Create Gradio interface
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demo = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil"),
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### 1. Imports and class names setup ###
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import gradio as gr
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import os
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import torch
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from typing import Tuple, Dict
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# Setup class names
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with open("class_names.txt", "r") as f: # reading them in from class_names.txt
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class_names = [food_name.strip() for food_name in f.readlines()]
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### 2. Model and transforms preparation ###
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# Create model
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effnetb2, effnetb2_transforms = create_effnetb2_model(
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num_classes=101, # could also use len(class_names)
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)
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# Load saved weights
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effnetb2.load_state_dict(
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torch.load(
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f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
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map_location=torch.device("cpu"), # load to CPU
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"""
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# Start the timer
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start_time = timer()
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# Transform the target image and add a batch dimension
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img = effnetb2_transforms(img).unsqueeze(0)
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# Put model into evaluation mode and turn on inference mode
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effnetb2.eval()
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with torch.inference_mode():
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# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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pred_probs = torch.softmax(effnetb2(img), dim=1)
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# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
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pred_labels_and_probs = {
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class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
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}
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# Calculate the prediction time
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pred_time = round(timer() - start_time, 5)
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# Return the prediction dictionary and prediction time
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return pred_labels_and_probs, pred_time
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### 4. Gradio app ###
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# Create title, description and article strings
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# Create examples list from "examples/" directory
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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# Create Gradio interface
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demo = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil"),
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