Fix memory leaks with unclosed PIL Image handles - #132
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Co-authored-by: VisionExpo <84554295+VisionExpo@users.noreply.github.com>
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| y_true = np.concatenate(y_true_accum_list) | ||
| y_pred = np.concatenate(y_pred_accum_list) |
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Suggestion: If dataset yields no batches, both accumulation lists stay empty and np.concatenate raises before evaluation can return metrics. [logic error]
Assessment: 🟠 Major · 🔁 Occurrence: Sometimes
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**Path:** src/solar_fault_detector/training/evaluator.py
**Line:** 40:41
**Comment:**
*Logic Error: If `dataset` yields no batches, both accumulation lists stay empty and `np.concatenate` raises before evaluation can return metrics.
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Once fix is implemented, also check other comments on the same PR, and ask user if the user wants to fix the rest of the comments as well. if said yes, then fetch all the comments validate the correctness and implement a minimal fix![chatgpt-codex-connector[bot]](https://avatars.githubusercontent.com/in/1144995?s=60&v=4){kind=small}
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| st.error("Unsupported file type.") | ||
| else: | ||
| image = Image.open(uploaded_file) | ||
| with Image.open(uploaded_file) as img: |
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For every valid upload that proceeds to inference, leaving this context closes the UploadedFile object supplied to Image.open. The subsequent uploaded_file.getvalue() call used to build the POST body then raises ValueError: I/O operation on closed file, so the Streamlit app can display the image but cannot submit it to /predict. Decode from a copied byte buffer, or preserve the upload stream for the request.
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User description
Fix memory leaks caused by unclosed Image.open file handles by using context managers. Also fixes a related bug with uninitialized variables in
src/solar_fault_detector/training/evaluator.py.PR created automatically by Jules for task 831726038996004958 started by @VisionExpo
CodeAnt-AI Description
Prevent image and evaluation memory leaks during inference and model evaluation
What Changed
Impact
✅ Fewer memory leaks during image processing✅ Reliable evaluation metrics across batches✅ More stable repeated model evaluations💡 Usage Guide
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