103 lines
9.1 KiB
Markdown
103 lines
9.1 KiB
Markdown
# Tasks: Analysis Dashboard Redesign
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## Phase 1: Backend Data Model + API Changes (Foundation)
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- [x] 1. Extend SessionData model
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- [x] 1.1 Add `rounds: List[Dict]` attribute to `SessionData.__init__()` in `web/main.py`, initialized to empty list
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- [x] 1.2 Add `data_files: List[Dict]` attribute to `SessionData.__init__()` in `web/main.py`, initialized to empty list
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- [x] 1.3 Update `_reconstruct_session()` to load `rounds` and `data_files` from `results.json` when reconstructing historical sessions
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- [x] 1.4 Update `run_analysis_task()` to persist `session.rounds` and `session.data_files` to `results.json` on analysis completion
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- [x] 2. Update Status API response
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- [x] 2.1 Add `rounds` field to `GET /api/status` response dict, returning `session.rounds`
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- [x] 2.2 Verify backward compatibility: ensure `log`, `is_running`, `has_report`, `progress_percentage`, `current_round`, `max_rounds`, `status_message` fields remain unchanged
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- [x] 3. Add Data Files API endpoints
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- [x] 3.1 Implement `GET /api/data-files` endpoint: return `session.data_files` merged with fallback directory scan for CSV/XLSX files, each entry containing filename, description, rows, cols, size_bytes
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- [x] 3.2 Implement `GET /api/data-files/preview` endpoint: read CSV/XLSX via pandas, return `{columns: [...], rows: [...first 5 rows as dicts...]}`; return 404 if file not found
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- [x] 3.3 Implement `GET /api/data-files/download` endpoint: return `FileResponse` with correct MIME type (`text/csv` or `application/vnd.openxmlformats-officedocument.spreadsheetml.sheet`); return 404 if file not found
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- [x] 4. Enhance Report API for evidence linking
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- [x] 4.1 Implement `_extract_evidence_annotations(paragraphs, session)` function: parse `<!-- evidence:round_N -->` comments from paragraph content, look up `session.rounds[N-1].evidence_rows`, build `supporting_data` mapping keyed by paragraph ID
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- [x] 4.2 Update `GET /api/report` to include `supporting_data` mapping in response JSON
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## Phase 2: CodeExecutor Enhancements
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- [x] 5. Add evidence capture to CodeExecutor
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- [x] 5.1 In `execute_code()`, after successful execution, check if `result.result` is a DataFrame; if so, capture `result.result.head(10).to_dict(orient='records')` as `evidence_rows`; wrap in try/except returning empty list on failure
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- [x] 5.2 Also check the last-assigned DataFrame variable in the namespace as a fallback evidence source when `result.result` is not a DataFrame
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- [x] 5.3 Include `evidence_rows` key in the returned result dict
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- [x] 6. Add DataFrame auto-detection and export
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- [x] 6.1 Before `shell.run_cell(code)`, snapshot DataFrame variables: `{name: id(obj) for name, obj in shell.user_ns.items() if isinstance(obj, pd.DataFrame)}`
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- [x] 6.2 After execution, compare snapshots to detect new or changed DataFrame variables
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- [x] 6.3 For each new DataFrame, export to `{output_dir}/{var_name}.csv` with numeric suffix deduplication if file exists
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- [x] 6.4 Record metadata for each export: `{variable_name, filename, rows, cols, columns}` in `auto_exported_files` list
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- [x] 6.5 Include `auto_exported_files` key in the returned result dict
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- [x] 7. Add DATA_FILE_SAVED marker parsing
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- [x] 7.1 After execution, scan `captured.stdout` for lines matching `[DATA_FILE_SAVED] filename: {name}, rows: {count}, description: {desc}`
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- [x] 7.2 Parse each marker line and record `{filename, rows, description}` in `prompt_saved_files` list
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- [x] 7.3 Include `prompt_saved_files` key in the returned result dict
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## Phase 3: Agent Changes
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- [x] 8. Structured Round_Data construction in DataAnalysisAgent
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- [x] 8.1 Add `_summarize_result(result)` method: produce one-line summary from execution result (e.g., "执行成功，输出 DataFrame (150行×8列)" or "执行失败: {error}")
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- [x] 8.2 In `_handle_generate_code()`, construct `round_data` dict with fields: round, reasoning (from `yaml_data.get("reasoning", "")`), code, result_summary, evidence_rows, raw_log, auto_exported_files, prompt_saved_files
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- [x] 8.3 After constructing round_data, append it to `SessionData.rounds` (via progress callback or direct reference)
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- [x] 8.4 Merge file metadata from `auto_exported_files` and `prompt_saved_files` into `SessionData.data_files`
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- [x] 9. Update system prompts
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- [x] 9.1 Add intermediate data saving instructions to `data_analysis_system_prompt` in `prompts.py`: instruct LLM to save intermediate results and print `[DATA_FILE_SAVED]` marker
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- [x] 9.2 Add evidence annotation instructions to `final_report_system_prompt` in `prompts.py`: instruct LLM to add `<!-- evidence:round_N -->` comments to report paragraphs
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- [x] 9.3 Update `_build_final_report_prompt()` in `data_analysis_agent.py` to include collected evidence data from all rounds in the prompt context
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## Phase 4: Frontend Tab Restructuring
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- [x] 10. HTML restructuring
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- [x] 10.1 In `index.html`, replace tab labels: "Live Log" → "执行过程", add "数据文件" tab, keep "Report"; remove "Gallery" tab
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- [x] 10.2 Replace the `logsTab` div content with an Execution Process container (`executionTab`) containing a scrollable round-cards wrapper
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- [x] 10.3 Add a `datafilesTab` div with a file-cards grid container and a preview panel area
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- [x] 10.4 Remove the Gallery tab HTML: carousel container, navigation buttons, image info panel
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- [x] 11. JavaScript: Execution Process Tab
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- [x] 11.1 Add `lastRenderedRound` state variable and `renderRoundCards(rounds)` function: compare `rounds.length` with `lastRenderedRound`, create and append new Round_Card DOM elements for new entries only
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- [x] 11.2 Implement Round_Card HTML generation: collapsed state shows round number + result_summary; expanded state shows reasoning, code (collapsible), result_summary, evidence table ("本轮数据案例"), raw log (collapsible)
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- [x] 11.3 Add click handler for Round_Card toggle (collapse/expand)
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- [x] 11.4 Add auto-scroll logic: when analysis is running, scroll Execution Process container to bottom after appending new cards
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- [x] 12. JavaScript: Data Files Tab
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- [x] 12.1 Implement `loadDataFiles()`: fetch `GET /api/data-files`, render file cards showing filename, description, row count
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- [x] 12.2 Implement `previewDataFile(filename)`: fetch `GET /api/data-files/preview`, render a table with column headers and up to 5 rows
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- [x] 12.3 Implement `downloadDataFile(filename)`: trigger download via `GET /api/data-files/download`
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- [x] 12.4 In `startPolling()`, call `loadDataFiles()` on each polling cycle when Data Files tab is active or when analysis is running
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- [x] 13. JavaScript: Gallery removal and tab updates
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- [x] 13.1 Remove gallery functions: `loadGallery`, `renderGalleryImage`, `prevImage`, `nextImage` and state variables `galleryImages`, `currentImageIndex`
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- [x] 13.2 Update `switchTab()` to handle `execution`, `datafiles`, `report` identifiers instead of `logs`, `report`, `gallery`
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- [x] 13.3 Update `startPolling()` to call `renderRoundCards()` with `data.rounds` on each polling cycle
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- [x] 14. JavaScript: Supporting data in Report
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- [x] 14.1 Update `loadReport()` to store `supporting_data` mapping from API response
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- [x] 14.2 Update `renderParagraphReport()` to add "查看支撑数据" button below paragraphs that have entries in `supporting_data`
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- [x] 14.3 Implement `showSupportingData(paraId)`: display a popover/modal with evidence rows rendered as a table
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- [x] 15. CSS updates
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- [x] 15.1 Add `.round-card`, `.round-card-header`, `.round-card-body`, `.round-card-collapsed`, `.round-card-expanded` styles
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- [x] 15.2 Add `.data-file-card`, `.data-preview-table` styles
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- [x] 15.3 Add `.supporting-data-btn`, `.supporting-data-popover` styles
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- [x] 15.4 Remove `.carousel-*` styles (carousel-container, carousel-slide, carousel-btn, image-info, image-title, image-desc)
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## Phase 5: Property-Based Tests
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- [x] 16. Write property-based tests
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- [x] 16.1 ~PBT~ Property 1: Round_Data structural completeness — generate random execution results, verify all required fields present with correct types and insertion order preserved
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- [x] 16.2 ~PBT~ Property 2: Evidence capture bounded — generate random DataFrames (0-10000 rows, 1-50 cols), verify evidence_rows length <= 10 and each row dict has correct keys
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- [x] 16.3 ~PBT~ Property 3: Filename deduplication — generate sequences of same-name exports (1-20), verify all filenames unique
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- [x] 16.4 ~PBT~ Property 4: Auto-export metadata completeness — generate random DataFrames, verify metadata contains variable_name, filename, rows, cols, columns with correct values
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- [x] 16.5 ~PBT~ Property 5: DATA_FILE_SAVED marker parsing round-trip — generate random filenames/rows/descriptions, verify parse(format(x)) == x
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- [x] 16.6 ~PBT~ Property 6: Data file preview bounded rows — generate random CSVs (0-10000 rows), verify preview returns at most 5 rows with correct column names
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- [x] 16.7 ~PBT~ Property 7: Evidence annotation parsing — generate random annotated Markdown, verify correct round extraction and non-annotated paragraph exclusion
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- [x] 16.8 ~PBT~ Property 8: SessionData JSON round-trip — generate random rounds/data_files, verify serialize then deserialize produces equal data
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