Leaf Disease detection

Hi, I need help finalizing my plant disease detection project. Current setup: - Model: CNN + MobileNetV2 (already trained) - Model file: .keras format available - API is already working and giving predictions correctly - Frontend is also working What I need you to do: 1. Create an [login to view URL] script that: - Loads the trained model (.keras file) - Loads dataset (PlantVillage / similar structured dataset) - Generates Confusion Matrix - Generates Classification Report (precision, recall, f1-score) 2. Ensure: - Correct class label mapping (no mismatch between dataset and model) - No changes in existing API behavior/output - Dataset should be properly loaded using ImageDataGenerator or [login to view URL] 3. Important: - Current prediction output from API should remain unchanged - Evaluation should be done separately (do not break API) 4. Optional (if possible): - Plot confusion matrix using matplotlib/seaborn - Print class names clearly Issue currently faced: - Some confusion between similar diseases (e.g., Blight vs Septoria) - Need evaluation metrics to demonstrate model performance Goal: - Make the project ready for final submission with proper evaluation (confusion matrix + report) - Keep system stable and working Let me know if you need dataset structure or model details.

Project ID: 40396015