ranggaaldosas commited on
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feat: add app.py

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  1. app.py +82 -0
app.py ADDED
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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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+
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+ from model import create_effnetv2_model
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+ from timeit import default_timer as timer
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+ from typing import Tuple, Dict
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+
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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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+
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+ ### 2. Model and transforms preparation ###
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+
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+ # Create model
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+ effnetv2, effnetv2_transforms = create_effnetv2_model(
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+ num_classes=101, # could also use len(class_names)
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+ )
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+
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+ # Load saved weights
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+ effnetv2.load_state_dict(
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+ torch.load(
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+ f="effnetv2_m_food101_100_percent.pth",
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+ map_location=torch.device("cpu"), # Huggingface offer cpu only
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+ )
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+ )
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+
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+ ### 3. Predict function ###
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+
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+
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+ # Create predict function
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+ def predict(img) -> Tuple[Dict, float]:
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+ """Transforms and performs a prediction on img and returns prediction and time taken."""
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+ # Start the timer
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+ start_time = timer()
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+
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+ # Transform the target image and add a batch dimension
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+ img = effnetv2_transforms(img).unsqueeze(0)
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+
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+ # Put model into evaluation mode and turn on inference mode
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+ effnetv2.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(effnetv2(img), dim=1)
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+
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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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+
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+ # Calculate the prediction time
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+ pred_time = round(timer() - start_time, 5)
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+
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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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+
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+
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+ ### 4. Gradio app ###
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+
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+ # Create title, description and article strings
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+ title = "🍰TasteNet-Big🍰"
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+ description = "A computer vision model based on EfficientNetV2_M designed for the classification of food images into 101 specific classes."
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+
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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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+
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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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+ outputs=[
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+ gr.Label(num_top_classes=5, label="Predictions"),
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+ gr.Number(label="Prediction time (s)"),
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+ ],
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+ examples=example_list,
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+ title=title,
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+ description=description,
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+ )
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+
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+ # Launch the app!
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+ demo.launch()