| """ | |
| 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() | |