{"chunk_end": 953, "chunk_start": 1, "error": "RuntimeError('Gemini response was not valid JSON: Expecting \\',\\' delimiter: line 12 column 6 (char 826); raw=\\'{\\\\n  \"chunk_summary\": \"The file implements scene segmentation and detailed analysis using LLM-generated anchors for text mapping and blind string mutation for T2I prompt enforcement.\",\\\\n  \"findings\": [\\\\n    {\\\\n      \"line_start\": 330,\\\\n      \"line_end\": 330,\\\\n      \"category\": \"semantic_string_judgment\",\\\\n      \"severity\": \"P1\",\\\\n      \"evidence\": \"pos = fulltext.find(start_text, search_from)\",\\\\n      \"why_problematic\": \"Uses an LLM-generated natural language snippet (start_text) as a brittle anchor to determine scene boundaries in the original scenario text. If the LLM slightly alters punctuation or characters, the match fails, leading to incorrect scene routing.\",\\\\n      \"recommended_fix\": \"Use token-based offsets or unique line identifiers if the source format allows, or implement fuzzy string matching for anchors.\"\\\\n    },\\\\n    {\\\\n      \"line_start\": 450,\\\\n      \"line_end\": 451,\\\\n      \"category\": \"semantic_string_judgment\",\\\\n      \"severity\": \"P1\",\\\\n      \"evidence\": \"if split_text in scene_text: split_pos = scene_text.index(split_text)\",\\\\n      \"why_problematic\": \"Similar to the segmentation anchor, this uses LLM-generated text to find a split point in a long scene. Failure to match exactly\\'')", "path": "backend/app/modules/pipeline/scene_extractor_v2.py", "scan_kind": "python"}
{"chunk_end": 3657, "chunk_start": 1, "error": "RuntimeError('Gemini response was not valid JSON: Expecting \\',\\' delimiter: line 12 column 6 (char 1186); raw=\\'{\\\\n  \"chunk_summary\": \"The file defines a builder for the RenderPromptCard, which centralizes T2I generation rules; however, it contains several instances of offloading semantic classification to the LLM via brittle phrase lists and scenario-specific demographic bias.\",\\\\n  \"findings\": [\\\\n    {\\\\n      \"line_start\": 1494,\\\\n      \"line_end\": 1663,\\\\n      \"category\": \"blind_string_mutation\",\\\\n      \"severity\": \"P1\",\\\\n      \"evidence\": \"replace the common-noun person reference inside fixed_elements[i].description (e.g. \\\\\\'An Asian man\\\\\\' / \\\\\\'a woman\\\\\\' / \\\\\\'a figure\\\\\\') with the matched C## or C##O##\",\\\\n      \"why_problematic\": \"This establishes a contract for the LLM to perform blind substring replacement on natural-language scenario text (fixed_elements). This is brittle and prone to errors in character identity injection, potentially leading to \\\\\\'phantom\\\\\\' figures if the replacement is incomplete or contextually incorrect.\",\\\\n      \"recommended_fix\": \"Instead of instructing the LLM to perform string replacement, provide the fixed_elements and the ID mapping as separate structured context and ask the LLM to generate the prompt using the IDs directly, or use a structured template system.\"\\\\n    },\\\\n    {\\\\n      \"line_start\": 582,\\\\n      \"line_end\": 1191,\\\\n\\'')", "path": "backend/app/core/steps/render_prompt_card.py", "scan_kind": "python"}