Update README.md

README.md

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----
-license: gpl-3.0
----
+---
+license: gpl-3.0
+language:
+- en
+metrics:
+- accuracy
+- sensitivity
+- specificity
+base_model: google/mobilenet-v2
+new_version: "true"
+pipeline_tag: image-classification
+library_name: pytorch
+tags:
+- medical
+- oral-cancer
+- healthcare
+- mobileNet
+- image-classification
+- pytorch
+eval_results:
+  accuracy: 0.95
+  sensitivity: 0.93
+  specificity: 0.91
+---
+
+# Umlomo – Oral Cancer Detection Model
+
+This model is a fine‑tuned **MobileNetV2** for binary classification of oral cavity images into **Normal** or **Oral Cancer**. It is part of the MySmile project, an AI‑powered oral health screening tool designed to empower individuals with early risk assessment.
+
+## Model Details
+
+- **Base Architecture:** MobileNetV2 (pretrained on ImageNet)
+- **Fine‑tuned Dataset:** Curated oral images (normal and cancerous)
+- **Input Size:** 224×224 RGB
+- **Output:** Two classes – `Normal` and `Oral Cancer`
+- **Framework:** PyTorch
+
+## Intended Use
+
+This model is intended for research and educational purposes within the MySmile screening application. It provides a preliminary risk assessment and is **not a substitute for professional medical diagnosis**.
+
+## How to Use
+
+### Installation
+```bash
+pip install torch torchvision pillow
+
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Load model
+model = torch.hub.load('mysmile/umlomo', 'model', trust_repo=True)
+model.eval()
+
+# Preprocess image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+image = Image.open('oral_photo.jpg').convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+# Inference
+with torch.no_grad():
+    outputs = model(input_tensor)
+    probs = torch.softmax(outputs, dim=1)
+    pred_idx = torch.argmax(probs, dim=1).item()
+
+class_names = ['Normal', 'Oral Cancer']
+print(f"Prediction: {class_names[pred_idx]}, Confidence: {probs[0][pred_idx]:.2f}")
+```
+
+---