Add inference.py

inference.py

@@ -0,0 +1,189 @@
+"""
+Inference script for Autism Spectrum Disorder (ASD) Detector
+
+Example usage of the trained model for making predictions.
+"""
+
+import json
+import torch
+import joblib
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from model import AutismDetectorNet, load_model
+
+
+class ASDPredictor:
+    """
+    Wrapper class for easy ASD prediction.
+
+    Example:
+        >>> predictor = ASDPredictor('.')
+        >>> result = predictor.predict(patient_data)
+        >>> print(result)
+    """
+
+    def __init__(self, model_dir='.', device='cpu'):
+        """
+        Initialize the predictor.
+
+        Args:
+            model_dir (str): Directory containing model files
+            device (str): Device for inference ('cpu' or 'cuda')
+        """
+        model_dir = Path(model_dir)
+        self.device = device
+
+        # Load model
+        self.model = load_model(model_dir / 'autism_detector.pth', device)
+
+        # Load preprocessor
+        self.preprocessor = joblib.load(model_dir / 'preprocessor.joblib')
+
+        # Load config
+        with open(model_dir / 'config.json', 'r') as f:
+            self.config = json.load(f)
+
+        # Load feature info
+        with open(model_dir / 'feature_info.json', 'r') as f:
+            self.feature_info = json.load(f)
+
+        self.feature_columns = self.config['feature_columns']
+
+    def predict(self, data, return_proba=False):
+        """
+        Make predictions on patient data.
+
+        Args:
+            data (pd.DataFrame or dict): Patient data with required features
+            return_proba (bool): If True, return probabilities instead of labels
+
+        Returns:
+            dict: Prediction results including label and probability
+        """
+        # Convert dict to DataFrame if necessary
+        if isinstance(data, dict):
+            data = pd.DataFrame([data])
+
+        # Ensure all required columns are present
+        missing_cols = set(self.feature_columns) - set(data.columns)
+        if missing_cols:
+            raise ValueError(f"Missing required columns: {missing_cols}")
+
+        # Select only required columns in correct order
+        data = data[self.feature_columns]
+
+        # Preprocess
+        X = self.preprocessor.transform(data)
+        X_tensor = torch.FloatTensor(X).to(self.device)
+
+        # Predict
+        self.model.eval()
+        with torch.no_grad():
+            probabilities = self.model(X_tensor).cpu().numpy().flatten()
+
+        # Format results
+        results = []
+        for prob in probabilities:
+            result = {
+                'prediction': 'ASD' if prob > 0.5 else 'Healthy',
+                'label': 1 if prob > 0.5 else 0,
+                'probability_asd': float(prob),
+                'probability_healthy': float(1 - prob),
+                'confidence': float(max(prob, 1 - prob))
+            }
+            results.append(result)
+
+        return results if len(results) > 1 else results[0]
+
+    def get_required_features(self):
+        """Return list of required feature columns."""
+        return self.feature_columns.copy()
+
+    def get_feature_info(self):
+        """Return detailed feature information."""
+        return self.feature_info.copy()
+
+
+def create_sample_patient():
+    """Create a sample patient record for testing."""
+    return {
+        'Gender': 'M',
+        'Age': 48,
+        'Urban/Rural': 'U',
+        'Pregnancy natural /IVF': 'N',
+        'Single/twins': 'S',
+        'Pregnancy evolution, N=normal, AN=abnormal': 'N',
+        'Birth weeks': 39,
+        'Type of birth': 'N',
+        'Birth Weight g': 3200,
+        'Length at birth  ': 50,
+        'Head circumference at birth  ': 35,
+        'APGAR score': 9,
+        'Postnatal adaptation N=normal, AN=abnormal': 'N',
+        'Developmental milestones- global delay (G), motor delay (M), cognitive delay (C)': 'N',
+        'Other Chronic diseases': 'N',
+        'Infections': 0,
+        'allergies': 0,
+        'Family history psychiatric (P) or Neuro disease (Ne), No=absent': 'NO',
+        'Mother age (years)': 30,
+        'Father age (years)': 32,
+        'IQ/DQ': 100,
+        'ICD': 'N',
+        'Neurological Examination; N=normal, text = abnormal; free cell = examination not performed ???': 'N',
+        'Weight kg': 15,
+        'Height  ': 100,
+        'head circumf  ': 48,
+        'Dysmorphysm y=present, no=absent': 'NO',
+        'malformations Y= present, N=absent': 'N',
+        'Behaviour disorder- agressivity, agitation, irascibility': 'N',
+        'Language development: delay, normal=N, absent=A': 'N',
+        'Language disorder Y= present, N=absent': 'N',
+        'EEG, N=normal, F=focal discharges, G=bilateral discharges': 'N',
+        'MRI structural anomalies of the brain, N=absent, AN=present': 'N'
+    }
+
+
+def main():
+    """Example usage of the ASD predictor."""
+    print("=" * 60)
+    print("ASD Detector - Inference Example")
+    print("=" * 60)
+
+    # Initialize predictor
+    predictor = ASDPredictor(model_dir='.')
+
+    # Create sample patient
+    print("\nSample Patient (Healthy profile):")
+    patient = create_sample_patient()
+    for key, value in list(patient.items())[:10]:
+        print(f"  {key}: {value}")
+    print("  ...")
+
+    # Make prediction
+    print("\nPrediction:")
+    result = predictor.predict(patient)
+    print(f"  Label: {result['prediction']}")
+    print(f"  Probability (ASD): {result['probability_asd']:.4f}")
+    print(f"  Probability (Healthy): {result['probability_healthy']:.4f}")
+    print(f"  Confidence: {result['confidence']:.4f}")
+
+    # Test with ASD-like profile
+    print("\n" + "-" * 60)
+    print("\nSample Patient (ASD-like profile):")
+    patient_asd = create_sample_patient()
+    patient_asd['Developmental milestones- global delay (G), motor delay (M), cognitive delay (C)'] = 'G'
+    patient_asd['Language development: delay, normal=N, absent=A'] = 'delay'
+    patient_asd['IQ/DQ'] = 45
+    patient_asd['Behaviour disorder- agressivity, agitation, irascibility'] = 'Y'
+
+    result_asd = predictor.predict(patient_asd)
+    print(f"  Changed features: Developmental=G, Language=delay, IQ=45, Behaviour=Y")
+    print(f"\nPrediction:")
+    print(f"  Label: {result_asd['prediction']}")
+    print(f"  Probability (ASD): {result_asd['probability_asd']:.4f}")
+    print(f"  Confidence: {result_asd['confidence']:.4f}")
+
+
+if __name__ == '__main__':
+    main()