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Project: Food Image Classification with Hugging Face
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    • Food Image Classification with Hugging Face

    A popular social media platform dedicated to food enthusiasts wants to improve user engagement by adding advanced image recognition features. As a machine learning engineer, you are tasked with developing a food image classification system using Hugging Face's state-of-the-art models. This system will automatically identify and categorize food items in user-uploaded photos, allowing for better content organization and personalized food content recommendations.

    Your responsibility is to develop a robust food category image classification system using pre-trained models from Hugging Face.

    The goal is to enhance user interaction by providing accurate food classification, enabling users to easily find and engage with content related to their favorite foods, and improving the overall experience on the platform.

    In this dynamic project, we leverage the power of PyTorch and transformers, utilizing an open-source model from Hugging Face as the backbone of our solution.

    # Install required libraries
!pip install matplotlib
!pip install pillow
!pip install transformers
    Hidden output 
    # Import required libraries
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

from transformers import pipeline
from transformers.utils import logging
# Only show error messages from the transformers library to reduce the amount of log output
logging.set_verbosity_error()

import warnings
# Ignore all Python warnings to keep the output clean
warnings.filterwarnings("ignore")
    # Helper function to convert image to RGB format
def convert_to_rgb(image):
    """
    Converts an image to RGB format.

    Parameters:
    image (PIL.Image): An image object.

    Returns:
    PIL.Image: Image object in RGB format.
    """
    return image.convert('RGB')

# Read and convert the image
image_path = 'Food Pictures/food_1.png'
image_raw = Image.open(image_path)
image = convert_to_rgb(image_raw)

# Display the image
plt.imshow(image)
plt.axis('off')

    # Define the model ID for the pre-trained food category classification model
modelId = "Kaludi/food-category-classification-v2.0"

# Load the image classification pipeline using the specified model ID
model = pipeline("image-classification", model=modelId)

# Save the pre-trained model to a local directory
model.save_pretrained(save_directory=f"models/{modelId}")

# Get the probabilities across different food categories
class_scores=model(image)
print(class_scores)


    # Get the class with the highest score
highest_probability = max(class_scores, key=lambda x: x['score'])
highest_probability_class = highest_probability['label']

# Print the label of the highest probability class for the uploaded image
print(f"Predicted food category: {highest_probability_class}")