add check to see if correct class order used for outpu

dataPrep/data_preparation.py

@@ -74,15 +74,15 @@ task.connect({
 })
 
 # ----- Load a subset from a given dataset & track with ClearML -----
-data_plants, subset_dataset, features = make_subset(
-    DATASET_LINK, DATASET_SUBSET_RATIO, task
+data_plants, prototyping_dataset, features, clearml_dataset = make_subset(
+    DATASET_LINK, DATASET_SUBSET_RATIO, clearml_logger
 )
 
 
 # ---- Exploratory data analysis (EDA) ----
 
 # Reformatting the label feature to understand bias
-labels_list = subset_dataset['label']
+labels_list = prototyping_dataset['label']
 df_labels = pd.Series(labels_list)
 label_count = df_labels.value_counts(sort=False)
 
@@ -111,7 +111,6 @@ clearml_logger.report_scalar(
     value=(max_count / min_count), 
     iteration=1
 )
-
 print("--- Class imbalance analysis --- ")
 print(f"Max labels in a class: {max_count}")
 print(f"Min labels in a class: {min_count}")
@@ -123,17 +122,16 @@ class_names = features['label'].names
 formatted_class_names = [" ".join(name.replace('_', ' ').split()) for name in class_names]
 label_count.index = formatted_class_names
 
-# Plotting class distribution
 plt.figure(figsize=(10,6))
 label_count.plot(kind='bar', color='skyblue')
-plt.title("Class Distribution in Subset Dataset")
+plt.title("Class Distribution in Prototype Dataset")
 plt.xlabel("Class")
 plt.ylabel("Count")
 plt.tight_layout()
 
 clearml_logger.report_matplotlib_figure(
     title="EDA Class Distribution",
-    series="Subset Dataset",
+    series="Prototype Subset",
     figure=plt.gcf(),
     iteration=1
 )
@@ -151,7 +149,7 @@ if __name__ == "__main__":
     }
 
     prototype_loaders = make_dataset_loaders(
-        subset_dataset, SEED, BATCH_SIZE, TEST_SIZE, aug_config
+        prototyping_dataset, SEED, BATCH_SIZE, TEST_SIZE, aug_config
     )
 
     print("\n--- Handoff Test Successful ---")
@@ -175,9 +173,14 @@ if __name__ == "__main__":
     print(f"Validation loader batches: {len(final_loaders['val'])}")
     print(f"Test loader batches: {len(final_loaders['test'])}")
 
+    # Record dataset info in ClearML
+    task.connect_configuration(
+        {"dataset_id": clearml_dataset.id},
+        name="Dataset Metadata"
+    )
+    task.mark_completed()
 
+    
     # Close the ClearML task 
-    task.mark_completed()
     task.close()
-
     print("\n--- Script Finished ---")

ui/app.py

@@ -48,7 +48,7 @@ class PlantDiseaseApp:
         try:
             # Load model if needed
             if modelName != self.current_modelName:
-                self.model = self.model_loader.loadModel(modelName)
+                self.model, self = self.model_loader.loadModel(modelName)
                 self.current_modelName = modelName
 
             # Preprocess image
@@ -61,6 +61,10 @@ class PlantDiseaseApp:
             # Convert logits to probabilities
             probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy()[0]
 
+
+            predID = probs.argmanx().item()
+            print("predicted index: " + predID)
+
             # Map to class names
             predictions = {name: float(prob) for name, prob in zip(self.class_names, probs)}