Update README.md

README.md

@@ -9,4 +9,74 @@ tags:
 - medical
 ---
 
-Work in progress.
+## Installation
+
+First, clone the repository:
+
+```bash
+git clone https://github.com/ethicalabs-ai/SkinCancerViT.git
+cd SkinCancerViT
+```
+
+Then, install the package in editable mode using uv (or pip):
+
+```bash
+uv sync   # Recommended if you use uv
+# Or, if using pip:
+# pip install -e .
+```
+
+## Quick Start / Usage
+
+This package allows you to load and use a pre-trained SkinCancerViT model for prediction.
+
+```python
+import torch
+from skincancer_vit.model import SkinCancerViTModel
+from PIL import Image
+from datasets import load_dataset   # To get a random sample
+
+# Load the model from Hugging Face Hub
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = SkinCancerViTModel.from_pretrained("ethicalabs/SkinCancerViT")
+model.to(device)   # Move model to the desired device
+model.eval()   # Set model to evaluation mode
+
+# Example Prediction from a Specific Image File
+image_file_path = "images/patient-001.jpg"   # Specify your image file path here
+specific_image = Image.open(image_file_path).convert("RGB")
+
+# Example tabular data for this prediction
+specific_age = 42
+specific_localization = "face"   # Ensure this matches one of your trained localization categories
+
+predicted_dx, confidence = model.full_predict(
+    raw_image=specific_image,
+    raw_age=specific_age,
+    raw_localization=specific_localization,
+    device=device
+)
+
+print(f"Predicted Diagnosis: {predicted_dx}")
+print(f"Confidence: {confidence:.4f}")
+
+# Example Prediction from a Random Validation Sample from the Dataset
+dataset = load_dataset("marmal88/skin_cancer", split="test")
+random_sample = dataset.shuffle(seed=42).select(range(1))[0] # Get the first shuffled sample
+
+sample_image = random_sample["image"]
+sample_age = random_sample["age"]
+sample_localization = random_sample["localization"]
+sample_true_dx = random_sample["dx"]
+
+predicted_dx_sample, confidence_sample = model.full_predict(
+    raw_image=sample_image,
+    raw_age=sample_age,
+    raw_localization=sample_localization,
+    device=device
+)
+
+print(f"Predicted Diagnosis: {predicted_dx_sample}")
+print(f"Confidence: {confidence_sample:.4f}")
+print(f"Correct Prediction: {predicted_dx_sample == sample_true_dx}")
+```