Update README.md

README.md

@@ -27,4 +27,41 @@ Deepfake-Detection-Exp-02-21 is a minimalist, high-quality dataset trained on a
     weighted avg     0.9886    0.9884    0.9884      3200
 
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/6720824b15b6282a2464fc58/0ISoyjxLs-zpqt9Gv4YRo.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/6720824b15b6282a2464fc58/0ISoyjxLs-zpqt9Gv4YRo.png)
+
+# **Inference with Hugging Face Pipeline**
+```python
+from transformers import pipeline
+
+# Load the model
+pipe = pipeline('image-classification', model="prithivMLmods/Deepfake-Detection-Exp-02-21", device=0)
+
+# Predict on an image
+result = pipe("path_to_image.jpg")
+print(result)
+```
+
+# **Inference with PyTorch**
+```python
+from transformers import ViTForImageClassification, ViTImageProcessor
+from PIL import Image
+import torch
+
+# Load the model and processor
+model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
+processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-Detection-Exp-02-21")
+
+# Load and preprocess the image
+image = Image.open("path_to_image.jpg").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+
+# Perform inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+
+# Map class index to label
+label = model.config.id2label[predicted_class]
+print(f"Predicted Label: {label}")
+```