Update README.md

README.md

@@ -43,32 +43,40 @@ pip install transformers torch pillow
 ```
 
 ```python
+import argparse
 from transformers import AutoModelForImageClassification, AutoFeatureExtractor
 from PIL import Image
 import torch
 
-image_path = 'path/to/your/image.jpg' 
-model_id = "SADRACODING/SDXL-Deepfake-Detector"
+def main():
+    parser = argparse.ArgumentParser(description="Predict image class using fine-tuned model from Hugging Face Hub")
+    parser.add_argument("--image", type=str, required=True, help="Path to the input image")
+    args = parser.parse_args()
 
-# Load Model and Feature Extractor
-model = AutoModelForImageClassification.from_pretrained(model_id)
-feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+    # Load model and feature extractor directly from Hugging Face Hub
+    model_name = "SADRACODING/SDXL-Deepfake-Detector"
+    model = AutoModelForImageClassification.from_pretrained(model_name)
+    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
 
-# Preprocessing
-image = Image.open(image_path).convert("RGB")
-inputs = feature_extractor(images=image, return_tensors="pt")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    model.eval()
 
-# Inference
-with torch.no_grad():
-    logits = model(**inputs).logits
+    image = Image.open(args.image).convert("RGB")
+    inputs = feature_extractor(images=image, return_tensors="pt").to(device)
 
-# Post-processing and Prediction
-predicted_class_id = logits.argmax().item()
-labels = ["REAL", "FAKE"] 
-prediction = labels[predicted_class_id]
-confidence = torch.nn.functional.softmax(logits, dim=-1)[0][predicted_class_id].item()
+    with torch.no_grad():
+        outputs = model(**inputs)
 
-print(f"Prediction: {prediction} (Confidence: {confidence:.4f})")
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    predicted_label = model.config.id2label[predicted_class_idx]
+
+    print(f"Predicted class index: {predicted_class_idx}")
+    print(f"Predicted label: {predicted_label}")
+
+if __name__ == "__main__":
+    main()
 ```
 ---
 license: mit