fix(tasks): enhanced model training zipping by adding input image pat…

…h and improving file copying

backend/core/tasks.py

@@ -225,6 +225,9 @@ def ramp_model_training(
         shutil.rmtree(output_path)
     shutil.copytree(final_model_path, os.path.join(output_path, "checkpoint.tf"))
     shutil.copytree(preprocess_output, os.path.join(output_path, "preprocessed"))
+    shutil.copytree(
+        model_input_image_path, os.path.join(output_path, "preprocessed", "input")
+    )
 
     graph_output_path = f"{base_path}/train/graphs"
     shutil.copytree(graph_output_path, os.path.join(output_path, "graphs"))
@@ -374,11 +377,30 @@ def yolo_model_training(
         os.path.join(os.path.dirname(output_model_path), "best.onnx"),
         os.path.join(output_path, "checkpoint.onnx"),
     )
+    shutil.copyfile(
+        os.path.join(os.path.dirname(output_model_path), "best.onnx"),
+        os.path.join(output_path, "checkpoint.onnx"),
+    )
     # shutil.copyfile(os.path.dirname(output_model_path,'checkpoint.tflite'), os.path.join(output_path, "checkpoint.tflite"))
 
     shutil.copytree(preprocess_output, os.path.join(output_path, "preprocessed"))
+    shutil.copytree(
+        model_input_image_path, os.path.join(output_path, "preprocessed", "input")
+    )
     os.makedirs(os.path.join(output_path, model), exist_ok=True)
 
+    shutil.copytree(
+        os.path.join(yolo_data_dir, "images"),
+        os.path.join(output_path, model, "images"),
+    )
+    shutil.copytree(
+        os.path.join(yolo_data_dir, "labels"),
+        os.path.join(output_path, model, "labels"),
+    )
+    shutil.copyfile(
+        os.path.join(yolo_data_dir, "yolo_dataset.yaml"),
+        os.path.join(output_path, model, "yolo_dataset.yaml"),
+    )
     shutil.copytree(
         os.path.join(yolo_data_dir, "images"),
         os.path.join(output_path, model, "images"),
@@ -473,18 +495,17 @@ def train_model(
     if training_instance.task_id is None or training_instance.task_id.strip() == "":
         training_instance.task_id = train_model.request.id
         training_instance.save()
-    log_file = os.path.join(settings.LOG_PATH, f"run_{train_model.request.id}.log")
+    log_file = os.path.join(settings.LOG_PATH, f"run_{train_model.request.id}_log.txt")
 
     if model_instance.base_model == "YOLO_V8_V1" and settings.YOLO_HOME is None:
         raise ValueError("YOLO Home is not configured")
     elif model_instance.base_model != "YOLO_V8_V1" and settings.RAMP_HOME is None:
         raise ValueError("Ramp Home is not configured")
 
     try:
-        with open(log_file, "a") as f:
+        with open(log_file, "w") as f:
             # redirect stdout to the log file
             sys.stdout = f
-            logging.info("Training Started")
             training_input_image_source, aoi_serializer, serialized_field = (
                 prepare_data(
                     training_instance, dataset_id, feedback, zoom_level, source_imagery