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inference_example.py

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+"""
+AgriBot Maize Disease Diagnosis - Inference Example
+Ekip Crusaders - AYITI IA 2025 Hackathon
+"""
+
+import joblib
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import models, transforms
+import sys
+from pathlib import Path
+
+
+class AgribotInference:
+    """Wrapper class for AgriBot model inference"""
+    
+    def __init__(self, model_path='agribot_models.pkl'):
+        print("Loading AgriBot model...")
+        self.model = joblib.load(model_path)
+        
+        print("Loading MobileNetV2 feature extractor...")
+        self.mobilenet = models.mobilenet_v2(pretrained=True)
+        self.mobilenet.classifier = torch.nn.Identity()
+        self.mobilenet.eval()
+        
+        self.class_labels = [
+            "Cercospora Leaf Spot (Gray Leaf Spot)",
+            "Common Rust",
+            "Northern Leaf Blight",
+            "Healthy",
+            "Other"
+        ]
+        
+        self.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]
+            )
+        ])
+        
+        print("Model loaded successfully!")
+    
+    def preprocess_image(self, image_path):
+        image = Image.open(image_path).convert('RGB')
+        return self.transform(image).unsqueeze(0)
+    
+    def extract_features(self, img_tensor):
+        with torch.no_grad():
+            features = self.mobilenet(img_tensor)
+        return features.numpy()
+    
+    def predict(self, image_path):
+        img_tensor = self.preprocess_image(image_path)
+        features = self.extract_features(img_tensor)
+        
+        prediction_idx = self.model.predict(features)[0]
+        probabilities = self.model.predict_proba(features)[0]
+        confidence = float(np.max(probabilities) * 100)
+        diagnosis = self.class_labels[prediction_idx]
+        
+        prob_dist = {
+            label: float(prob * 100)
+            for label, prob in zip(self.class_labels, probabilities)
+        }
+        
+        return {
+            'diagnosis': diagnosis,
+            'confidence': round(confidence, 2),
+            'prediction_index': int(prediction_idx),
+            'probabilities': prob_dist,
+            'is_healthy': diagnosis == "Healthy",
+            'is_maize': diagnosis != "Other"
+        }
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python inference_example.py <image_path>")
+        print("Example: python inference_example.py maize_leaf.jpg")
+        sys.exit(1)
+    
+    image_path = sys.argv[1]
+    
+    if not Path(image_path).exists():
+        print(f"Error: Image file '{image_path}' not found!")
+        sys.exit(1)
+    
+    predictor = AgribotInference()
+    
+    print(f"\nAnalyzing image: {image_path}")
+    print("-" * 50)
+    
+    result = predictor.predict(image_path)
+    
+    print(f"\n🌽 DIAGNOSIS: {result['diagnosis']}")
+    print(f"📊 Confidence: {result['confidence']:.2f}%")
+    print(f"🔍 Is Maize: {'Yes' if result['is_maize'] else 'No'}")
+    print(f"✅ Is Healthy: {'Yes' if result['is_healthy'] else 'No'}")
+    
+    print("\n📈 Probability Distribution:")
+    for disease, prob in result['probabilities'].items():
+        bar_length = int(prob / 2)
+        bar = "█" * bar_length
+        print(f"  {disease:40s} {prob:6.2f}% {bar}")
+    
+    print("\n" + "-" * 50)
+    print("Analysis complete!")
+
+
+if __name__ == "__main__":
+    main()