Fix marimo variable uniqueness (prefix private vars with _)

train-image-classifier.py

@@ -228,29 +228,29 @@ def _(dataset_name, mo):
 @app.cell
 def _(dataset, labels, mo):
     # Show sample images (notebook mode only)
-    import base64
-    from io import BytesIO
+    import base64 as _base64
+    from io import BytesIO as _BytesIO
 
-    def image_to_base64(img, max_size=150):
+    def _image_to_base64(img, max_size=150):
         """Convert PIL image to base64 for HTML display."""
-        img_copy = img.copy()
-        img_copy.thumbnail((max_size, max_size))
-        buffered = BytesIO()
-        img_copy.save(buffered, format="PNG")
-        return base64.b64encode(buffered.getvalue()).decode()
+        _img_copy = img.copy()
+        _img_copy.thumbnail((max_size, max_size))
+        _buffered = _BytesIO()
+        _img_copy.save(_buffered, format="PNG")
+        return _base64.b64encode(_buffered.getvalue()).decode()
 
     # Get 6 sample images with different labels
-    samples = dataset["train"].shuffle(seed=42).select(range(6))
+    _samples = dataset["train"].shuffle(seed=42).select(range(6))
 
-    images_html = []
-    for sample in samples:
-        img_b64 = image_to_base64(sample["image"])
-        label_name = labels[sample["label"]] if labels else sample["label"]
-        images_html.append(
+    _images_html = []
+    for _sample in _samples:
+        _img_b64 = _image_to_base64(_sample["image"])
+        _label_name = labels[_sample["label"]] if labels else _sample["label"]
+        _images_html.append(
             f"""
             <div style="text-align: center; margin: 5px;">
-                <img src="data:image/png;base64,{img_b64}" style="border-radius: 8px;"/>
-                <br/><small>{label_name}</small>
+                <img src="data:image/png;base64,{_img_b64}" style="border-radius: 8px;"/>
+                <br/><small>{_label_name}</small>
             </div>
             """
         )
@@ -258,7 +258,7 @@ def _(dataset, labels, mo):
     mo.md(f"""
     ### Sample Images
     <div style="display: flex; flex-wrap: wrap; gap: 10px;">
-    {"".join(images_html)}
+    {"".join(_images_html)}
     </div>
     """)
     return
@@ -420,41 +420,40 @@ def _(trainer):
 @app.cell
 def _(dataset, id2label, image_processor, mo, model):
     import torch
+    import base64 as _b64
+    from io import BytesIO as _BIO
 
     # Show some predictions (notebook mode)
     model.eval()
-    test_samples = dataset["test"].shuffle(seed=42).select(range(4))
+    _test_samples = dataset["test"].shuffle(seed=42).select(range(4))
 
-    prediction_html = []
-    for sample in test_samples:
-        img = sample["image"].convert("RGB")
-        inputs = image_processor(img, return_tensors="pt")
+    _prediction_html = []
+    for _sample in _test_samples:
+        _img = _sample["image"].convert("RGB")
+        _inputs = image_processor(_img, return_tensors="pt")
 
         with torch.no_grad():
-            outputs = model(**inputs)
-            pred_idx = outputs.logits.argmax(-1).item()
+            _outputs = model(**_inputs)
+            _pred_idx = _outputs.logits.argmax(-1).item()
 
-        true_label = id2label[sample["label"]] if id2label else sample["label"]
-        pred_label = id2label[pred_idx] if id2label else pred_idx
-        correct = "correct" if pred_idx == sample["label"] else "wrong"
+        _true_label = id2label[_sample["label"]] if id2label else _sample["label"]
+        _pred_label = id2label[_pred_idx] if id2label else _pred_idx
+        _correct = "correct" if _pred_idx == _sample["label"] else "wrong"
 
         # Convert image for display
-        from io import BytesIO
-        import base64
-
-        img_copy = img.copy()
-        img_copy.thumbnail((120, 120))
-        buffered = BytesIO()
-        img_copy.save(buffered, format="PNG")
-        img_b64 = base64.b64encode(buffered.getvalue()).decode()
-
-        border_color = "#4ade80" if correct == "correct" else "#f87171"
-        prediction_html.append(
+        _img_copy = _img.copy()
+        _img_copy.thumbnail((120, 120))
+        _buffered = _BIO()
+        _img_copy.save(_buffered, format="PNG")
+        _img_b64 = _b64.b64encode(_buffered.getvalue()).decode()
+
+        _border_color = "#4ade80" if _correct == "correct" else "#f87171"
+        _prediction_html.append(
             f"""
-            <div style="text-align: center; margin: 5px; padding: 10px; border: 2px solid {border_color}; border-radius: 8px;">
-                <img src="data:image/png;base64,{img_b64}" style="border-radius: 4px;"/>
-                <br/><small>True: <b>{true_label}</b></small>
-                <br/><small>Pred: <b>{pred_label}</b></small>
+            <div style="text-align: center; margin: 5px; padding: 10px; border: 2px solid {_border_color}; border-radius: 8px;">
+                <img src="data:image/png;base64,{_img_b64}" style="border-radius: 4px;"/>
+                <br/><small>True: <b>{_true_label}</b></small>
+                <br/><small>Pred: <b>{_pred_label}</b></small>
             </div>
             """
         )
@@ -462,7 +461,7 @@ def _(dataset, id2label, image_processor, mo, model):
     mo.md(f"""
     ### Sample Predictions
     <div style="display: flex; flex-wrap: wrap; gap: 10px;">
-    {"".join(prediction_html)}
+    {"".join(_prediction_html)}
     </div>
     <small>Green border = correct, Red border = wrong</small>
     """)