| """ |
| Example usage script for the Skin Type Classification model on Hugging Face. |
| """ |
|
|
| from transformers import AutoModelForImageClassification, AutoImageProcessor |
| from PIL import Image |
| import torch |
| import requests |
| from io import BytesIO |
|
|
| def load_model(model_name="your-username/skin-type-classifier"): |
| """Load the model and processor from Hugging Face.""" |
| model = AutoModelForImageClassification.from_pretrained(model_name) |
| processor = AutoImageProcessor.from_pretrained(model_name) |
| return model, processor |
|
|
| def predict_skin_type(image_path_or_url, model, processor): |
| """ |
| Predict skin type from an image. |
| |
| Args: |
| image_path_or_url: Path to local image or URL |
| model: The loaded model |
| processor: The loaded processor |
| |
| Returns: |
| dict: Prediction results with class and confidence |
| """ |
| |
| if image_path_or_url.startswith(('http://', 'https://')): |
| response = requests.get(image_path_or_url) |
| image = Image.open(BytesIO(response.content)) |
| else: |
| image = Image.open(image_path_or_url) |
| |
| |
| if image.mode != 'RGB': |
| image = image.convert('RGB') |
| |
| |
| inputs = processor(images=image, return_tensors="pt") |
| |
| |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) |
| predicted_class_idx = predictions.argmax().item() |
| confidence = predictions[0][predicted_class_idx].item() |
| |
| |
| class_names = {0: "dry", 1: "oily"} |
| predicted_class = class_names[predicted_class_idx] |
| |
| return { |
| "predicted_class": predicted_class, |
| "confidence": confidence, |
| "all_scores": { |
| "dry": predictions[0][0].item(), |
| "oily": predictions[0][1].item() |
| } |
| } |
|
|
| def main(): |
| """Example usage of the skin type classification model.""" |
| print("🔬 Loading Skin Type Classification Model...") |
| |
| |
| model, processor = load_model() |
| |
| print("✅ Model loaded successfully!") |
| |
| |
| try: |
| image_path = "example_skin_image.jpg" |
| result = predict_skin_type(image_path, model, processor) |
| |
| print(f"\n📊 Prediction Results:") |
| print(f"Predicted Skin Type: {result['predicted_class']}") |
| print(f"Confidence: {result['confidence']:.2%}") |
| print(f"All Scores: {result['all_scores']}") |
| |
| except FileNotFoundError: |
| print("ℹ️ Please provide a valid image path to test the model") |
| |
| |
| print("\n💡 Usage Examples:") |
| print("1. Local image: predict_skin_type('path/to/image.jpg', model, processor)") |
| print("2. URL image: predict_skin_type('https://example.com/image.jpg', model, processor)") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
