Add README.md

README.md

@@ -80,15 +80,47 @@ This simplified model requires only 8 inputs instead of 33, making it practical
 pip install torch scikit-learn joblib pandas
 ```
 
-### Quick Start
+### Quick Start (TorchScript - Recommended)
+
+```python
+import torch
+import joblib
+
+# Load model directly with PyTorch
+model = torch.jit.load('autism_detector_traced.pt')
+model.eval()
+
+# Load preprocessor
+preprocessor = joblib.load('preprocessor.joblib')
+
+# Prepare input (8 features)
+import pandas as pd
+patient = pd.DataFrame([{
+    'Developmental milestones- global delay (G), motor delay (M), cognitive delay (C)': 'N',
+    'IQ/DQ': 100,
+    'ICD': 'N',
+    'Language disorder Y= present, N=absent': 'N',
+    'Language development: delay, normal=N, absent=A': 'N',
+    'Dysmorphysm y=present, no=absent': 'NO',
+    'Behaviour disorder- agressivity, agitation, irascibility': 'N',
+    'Neurological Examination; N=normal, text = abnormal; free cell = examination not performed ???': 'N'
+}])
+
+# Preprocess and predict
+X = preprocessor.transform(patient)
+with torch.no_grad():
+    prob = model(torch.FloatTensor(X)).item()
+
+print(f"Probability of ASD: {prob:.2%}")
+print(f"Prediction: {'ASD' if prob > 0.5 else 'Healthy'}")
+```
+
+### Using the Inference Helper
 
 ```python
 from inference import ASDPredictor
 
-# Initialize predictor
 predictor = ASDPredictor('.')
-
-# Example: Healthy child
 result = predictor.predict({
     'developmental_milestones': 'N',
     'iq_dq': 100,
@@ -140,7 +172,8 @@ Output (probability of ASD)
 
 | File | Description |
 |------|-------------|
-| `autism_detector.pth` | PyTorch model weights |
+| `autism_detector_traced.pt` | **TorchScript model** - load with `torch.jit.load()` |
+| `autism_detector.pth` | PyTorch checkpoint (weights + config) |
 | `preprocessor.joblib` | Feature preprocessor |
 | `config.json` | Model configuration |
 | `model.py` | Model class definition |