Move to real LaTeX rendering
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@@ -65,13 +65,15 @@ def apply_actions_and_regenerate_grouped(root_dir, data, student_id, actions, la
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# --- 2. Process Images (Regenerate & Concatenate) ---
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concat_list = []
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concat_list_F = []
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d_notes = dict.fromkeys(all_labels, "")
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# Iterate over all labels naturally to assemble a complete student profile
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sorted_labels = sorted(labels_data.items(), key=lambda x: natural_key(x[0]))
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for label, content in sorted_labels:
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d_notes[label] = str(content['result'].get('score', 0))
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result = content['result']
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d_notes[label] = str(result.get('score', 0))
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pdf_path = os.path.join(root_dir, f"Copie{student_id}", f"{label}.pdf")
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if not os.path.exists(pdf_path): continue
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@@ -102,6 +104,16 @@ def apply_actions_and_regenerate_grouped(root_dir, data, student_id, actions, la
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concat_list.append(final_img)
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perfect_no_comment = True
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if float(d_notes[label]) != 4.0:
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perfect_no_comment = False
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else:
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if len(result.get('feedback', [])) != 0:
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perfect_no_comment = False
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if not perfect_no_comment:
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concat_list_F.append(final_img)
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# --- 3. Save Final Outputs ---
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with open(score_path, "w") as f:
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json.dump(d_notes, f, indent=4)
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@@ -119,6 +131,18 @@ def apply_actions_and_regenerate_grouped(root_dir, data, student_id, actions, la
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full_img.save(os.path.join(output_dir, "Concat.jpg"))
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print(f" Saved regenerated Concat.jpg")
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if concat_list_F:
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max_w = max(i.width for i in concat_list_F)
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total_h = sum(i.height for i in concat_list_F)
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full_img = Image.new("RGB", (max_w, total_h), "white")
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y = 0
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for img in concat_list_F:
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full_img.paste(img, (0, y))
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y += img.height
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full_img.save(os.path.join(output_dir, "Concat_F.jpg"))
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print(f" Saved regenerated Concat_F.jpg")
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if __name__ == "__main__":
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