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"""
Hugging Face Model Hub Integration Example
==========================================
This script demonstrates how to use the cattle breed classification model
from Hugging Face Model Hub.
"""
import onnxruntime as ort
import numpy as np
import json
from PIL import Image
from torchvision import transforms
import requests
from huggingface_hub import hf_hub_download
import os
class CattleBreedClassifier:
def __init__(self, model_name="your-username/cattle-breed-classifier"):
"""
Initialize the classifier by downloading model files from Hugging Face
Args:
model_name: HuggingFace model repository name
"""
self.model_name = model_name
self.session = None
self.prototypes = None
self.metadata = None
# Download and load model files
self._download_model_files()
self._load_model()
self._load_prototypes()
def _download_model_files(self):
"""Download model files from Hugging Face Hub"""
print("📥 Downloading model files from Hugging Face...")
# Download ONNX model
self.model_path = hf_hub_download(
repo_id=self.model_name,
filename="model.onnx"
)
# Download prototypes
self.prototypes_path = hf_hub_download(
repo_id=self.model_name,
filename="prototypes.json"
)
# Download metadata
self.metadata_path = hf_hub_download(
repo_id=self.model_name,
filename="metadata.json"
)
print("✅ Model files downloaded successfully!")
def _load_model(self):
"""Load the ONNX model"""
self.session = ort.InferenceSession(self.model_path)
print("✅ ONNX model loaded")
def _load_prototypes(self):
"""Load breed prototypes"""
with open(self.prototypes_path, 'r') as f:
self.prototypes = json.load(f)
with open(self.metadata_path, 'r') as f:
self.metadata = json.load(f)
print(f"✅ Loaded prototypes for {len(self.prototypes['prototypes'])} breeds")
def preprocess_image(self, image_input):
"""
Preprocess image for model inference
Args:
image_input: PIL Image, numpy array, or file path
Returns:
numpy.ndarray: Preprocessed image tensor
"""
# Handle different input types
if isinstance(image_input, str):
image = Image.open(image_input).convert('RGB')
elif isinstance(image_input, np.ndarray):
image = Image.fromarray(image_input).convert('RGB')
else:
image = image_input.convert('RGB')
# Apply preprocessing
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
tensor = transform(image).unsqueeze(0)
return tensor.numpy()
def predict(self, image_input, return_all_scores=False):
"""
Predict cattle/buffalo breed from image
Args:
image_input: Image input (PIL Image, numpy array, or file path)
return_all_scores: Whether to return scores for all breeds
Returns:
dict: Prediction results
"""
# Preprocess image
input_data = self.preprocess_image(image_input)
# Run inference
features = self.session.run(None, {'input': input_data})[0][0]
# Calculate similarities with all breed prototypes
similarities = {}
for breed, prototype in self.prototypes['prototypes'].items():
similarity = np.dot(features, np.array(prototype))
similarities[breed] = float(similarity)
# Get top prediction
predicted_breed = max(similarities, key=similarities.get)
confidence = similarities[predicted_breed]
# Determine animal type
buffalo_breeds = ['Bhadawari', 'Jaffarbadi', 'Mehsana', 'Murrah', 'Surti']
animal_type = 'Buffalo' if predicted_breed in buffalo_breeds else 'Cattle'
result = {
'predicted_breed': predicted_breed,
'confidence': confidence,
'animal_type': animal_type
}
if return_all_scores:
result['all_scores'] = similarities
return result
# Example usage
def main():
# Initialize classifier (will download model from Hugging Face)
classifier = CattleBreedClassifier("your-username/cattle-breed-classifier")
# Example 1: Predict from local image
image_path = "path/to/your/image.jpg"
if os.path.exists(image_path):
result = classifier.predict(image_path, return_all_scores=True)
print(f"\n🐄 Prediction Results:")
print(f"Animal Type: {result['animal_type']}")
print(f"Predicted Breed: {result['predicted_breed']}")
print(f"Confidence: {result['confidence']:.4f}")
print(f"\n📊 All Breed Scores:")
for breed, score in sorted(result['all_scores'].items(),
key=lambda x: x[1], reverse=True):
print(f" {breed}: {score:.4f}")
# Example 2: Predict from PIL Image
from PIL import Image
image = Image.open(image_path)
result = classifier.predict(image)
print(f"\nDirect PIL prediction: {result['predicted_breed']} ({result['confidence']:.4f})")
if __name__ == "__main__":
main()
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