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Upload app.py

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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_leaderss_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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+ class_names = ['Barack Obama', 'Narendra Modi', 'Vladimir Vladimirovich Putin']
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+
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+ ### 2. Model and transforms preparation ###
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+
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+
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+ Leaders, Leaders_transforms = create_Leaders_model(
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+ num_classes=3, # len(class_names) would also work
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+ )
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+
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+ # Load saved weights
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+ Leaders.load_state_dict(
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+ torch.load(
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+ f="Leaders_model_save.pth",
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+ map_location=torch.device("cpu"), # load to CPU
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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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+ # 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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+ """
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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 = Leaders_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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+ Leaders.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(gadget(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 = {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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+
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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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+ ### 4. Gradio app ###
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+
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+ # Create title, description and article strings
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+ title = "Leaders Classifier "
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+ description = "An computer vision model to classify images of Leaders as Barack Obama,Narendra Modi,Vladimir Vladimirovich Putin"
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+ article = "Created by Shreya Valte"
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+
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+
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+
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+ # Create the Gradio demo
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+ demo = gr.Interface(fn=predict, # mapping function from input to output
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+ inputs=gr.Image(type="pil"), # what are the inputs?
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+ outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
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+ gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
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+ # Create examples list from "examples/" directory
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+ examples=["Image_1.jpg","Image_108.jpg"],
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+ title=title,
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+ description=description,
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+ article=article)
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+
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+ # Launch the demo!
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+ demo.launch()