add train code

train.py

@@ -0,0 +1,47 @@
+# Make sure to put your kaggle token in the environment variable KAGGLE_API_TOKEN
+
+import kagglehub
+from pathlib import Path
+from fastai.vision.all import *
+
+# Download latest version
+path = kagglehub.dataset_download("utkarshsaxenadn/fruits-classification")
+path = Path(path) / 'Fruits Classification'
+
+print("Path to dataset files:", path)
+
+block = DataBlock(
+    blocks = (ImageBlock(), CategoryBlock()),
+    get_items=get_image_files,
+    splitter = GrandparentSplitter(train_name='train', valid_name='valid'),
+    get_y = parent_label,
+    item_tfms=RandomResizedCrop(224, min_scale=0.3),
+    batch_tfms = aug_transforms()
+)
+
+dls = block.dataloaders(path, bs = 64, seed=2026)
+
+learner = vision_learner(dls, resnet34, metrics=error_rate)
+learner.fine_tune(5)
+
+# Show classification accuracy
+
+interpretation = ClassificationInterpretation.from_learner(learner)
+interpretation.plot_confusion_matrix()
+interpretation.plot_top_losses(5, nrows=1, figsize=(20,5))
+
+# Test the model
+# https://forums.fast.ai/t/how-to-evaluate-model-on-test-set/97972/3
+
+test_files = get_image_files(path / "test")
+print(len(test_files))
+test_dl = dls.test_dl(test_files, with_labels=True)
+test_dl.show_batch(max_n=5)
+preds, y = learner.get_preds(dl=test_dl)
+acc = accuracy(preds, y)
+print("Accuracy: ", acc)
+acc2 = learner.validate(dl=test_dl)
+print(acc2)
+
+# Export the model
+learner.export("fruits-model-v1.pkl")