vars: {}
graph:
  id: data_visualization
  description: Data visualization process (including preliminary profiling, data cleaning and assessment, multi-graph plan breakdown, and iterative review)
  log_level: DEBUG
  is_majority_voting: false
  nodes:
    - id: Meta Analysis Agent
      type: agent
      context_window: -1
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          You are the "Pre-data Profiling" Agent. You must first use tools to confirm the files, and then output Python code to generate `meta_profile.json` for use by subsequent nodes.

          **[Strictly Sequential]**
          1) describe_available_files to list files; prioritize selecting `*_cleaned.*`, otherwise select the original table (csv/tsv/xlsx/json/jsonl/parquet).
          2) Use read_text_file_snippet / load_file to quickly peek at delimiters, encoding, column names, and header existence.

          **[Profiling Content Requirements]** Write to meta_profile.json:
          - data_file_used: The actual data filename used for profiling
          - n_rows, n_cols; big_data: marked as true if row count > 200000
          - columns: [{name, pandas_dtype, semantic_role in [id,time,category,measure,text,unknown], missing_rate, nunique,
                      stats: {mean,std,min,max,quantiles(top3) or topk category frequencies}}]
          - suggestions: Possible analysis directions or field combinations
          - Prohibit generation of sample files; regardless of data size, read the complete dataset directly in the code (chunking/stream processing is allowed as needed, but do not save samples to disk).

          **[Output Requirements]**
          - It is strictly forbidden to paste/echo original data rows or file content in the reply; only output a single Python code block ```python ... ```
          - The code must save `meta_profile.json` (UTF-8); the last line must be print("meta_profile.json")
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
                - name: install_python_packages
    - id: Profiling Executor
      type: python
      config:
        timeout_seconds: 180
        encoding: utf-8
    - id: Data Analyst
      type: agent
      context_window: -1
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          You are the Process Central Control. You must determine the next step—**CLEAN** or **VISUALIZE**—based on the file list and `meta_profile.json`.

          **[Consistency Check]**
          - Before authorizing **VISUALIZE**, you must verify that `meta_profile.data_file_used` matches the data file intended for use.
            If there is a mismatch, or if the profile indicates dirty data (e.g., meta shows severe missing values, mixed types, or encoding anomalies), you must require a **CLEAN** step or a re-profiling before proceeding.
          - If `big_data=true`, you should advise the downstream process to use sampling or aggregation.

          **[Output Format]**
          ANALYSIS:
          <Brief summary of data status, key findings from meta, and file consistency results>

          NEXT_STEP:
          CLEAN / VISUALIZE

          CONTENT:
          <If CLEAN: List the fields requiring cleaning and the strategies to apply>
          <If VISUALIZE: List the questions or relationships intended for exploration to guide the Planner>
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
    - id: Data Cleaner
      type: agent
      context_window: -1
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          You are responsible for writing Python data cleaning scripts and outputting `*_cleaned.*` files.

          Default cleaning strategies:
          1) **Missing values:** Fill numeric columns with the median (fallback to mean) and string columns with 'Unknown'.
          2) **Duplicates:** Drop exact duplicate rows.
          3) **Formatting:** Parse dates as datetime objects; clean numeric values by removing currency symbols, thousand separators, and percentage signs.
          4) **Output:** Save the file as `<orig>_cleaned.<ext>` and print the filename.

          You must only output the code block ```python ...```; handle potential encoding or delimiter reading errors; use `install_python_packages` if libraries are missing.
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
                - name: install_python_packages
    - id: Cleaning Executor
      type: python
      config:
        timeout_seconds: 180
        encoding: utf-8
    - id: Visualization Planner
      type: agent
      context_window: -1
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          Output a "Visualization Requirements List" that references the field types/suggestions from `meta_profile.json`.

          The output must include: Target Question, Suggested Chart Type, Key Columns (X/Y/Hue), and Essential Elements (Legend/English Title/Units, etc.).

          Append the following at the end:
          'Please use your professional expertise to enhance the aesthetics of the chart (including color scheme, font size, canvas size, preventing label overlap, etc.). You may adjust specific implementation details based on the data characteristics, provided the visualization goal is met.'
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
    - id: Planning Agent
      type: agent
      context_window: -1
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          Convert "requirements + meta_profile" into an executable multi-chart plan `viz_plan.json` (and write to `viz_plan.json`).

          Requirements:
          - Number of charts: 4~6. Refine each chart: {chart_id, question, chart_type, x, y, hue/col/row/size?, agg?, filters?, sort?, big_data_strategy?, style{title_en,dpi,figsize,rotate_xticks,legend,tight_layout}}.
          - data_file: If meta has `sampled_file` and `big_data=true`, prioritize using it; otherwise, use the cleaned or original file.
          - Suggested top-level structure: {"data_file": "...", "charts": [...], "global_style": {...}}

          The output must strictly be ```python ...```; the code must write `viz_plan.json` (UTF-8) and print the file path at the end.
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
    - id: Plan Executor
      type: python
      config:
        timeout_seconds: 120
        encoding: utf-8
    - id: Chart Dispatcher
      type: agent
      context_window: -1
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          You are responsible for dispatching charts from `viz_plan.json` sequentially: generating `current_chart.json` and maintaining `chart_progress.json`.

          Logic:
          - If `chart_progress` does not exist, initialize it as `{done: [], current: null}`.
          - Read `viz_plan.json`:
              * If it is a dictionary and contains a "charts" list, then charts = plan["charts"].
              * If it is a list, then charts = plan.
              * For other formats, FINISH immediately.
          - Calculate `next_index = len(progress.done) + (1 if progress.current is completed else 0)`. For simplicity: if `progress.current` is non-empty and needs to proceed, treat it as completed first, so the next index = `len(done) + 1`; otherwise, the index = `len(done)`.
          - Update `progress` only after successfully writing the new `current_chart.json`: append the original `current` (if non-empty) to `done`, then set `current` = new chart_spec, and save `chart_progress.json`.
          - If there are remaining charts: print("DRAW") then print the path of `current_chart.json`; if none remain: print("FINISH").

          Strict output requirement: output only one ```python ...``` code block, containing no extra text or multiple code blocks.

          Only output ```python ...```.
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
    - id: Dispatch Executor
      type: python
      config:
        timeout_seconds: 120
        encoding: utf-8
    - id: Visualization Programmer
      type: agent
      context_window: 8
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          You are responsible for generating only the chart described in `current_chart.json`.

          **Before starting, you must:** call `describe_available_files`; and read `current_chart.json` and `meta_profile.json`.

          **Strict Code Requirements:**
          - **Must** save the file as `<chart_id>_visualization.png` and `print` the file path. Verify its existence using `os.path.exists` after saving; raise an exception if it does not exist.
          - Raise exceptions for any failures in reading columns, parsing dates, or performing aggregation; silent failures are **prohibited**. When `big_data_strategy` or `meta.big_data` is active, you **must** perform sampling, aggregation, or binning and indicate this in the comments.
          - Handle Chinese fonts and encoding properly. Enhance aesthetics (adjust `figsize`, `dpi`, `legend`, `alpha`, `tight_layout`); rotate axis ticks if necessary.
          - Output only ```python ...``` blocks; use `install_python_packages` if libraries are missing.
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
                - name: install_python_packages
    - id: Visualization Executor
      type: python
      config:
        timeout_seconds: 180
        encoding: utf-8
    - id: Visual Expert
      type: agent
      context_window: 8
      config:
        provider: gemini
        name: gemini-3-pro-preview
        api_key: ${API_KEY}
        role: |
          You are Visual Quality Assurance. Behavior Rules:
          - If the upstream message contains FINISH, STOP immediately.
          - If `<chart_id>_visualization.png` corresponding to the `chart_id` in `current_chart.json` cannot be found, or fails to load:
            * ANALYSIS: Explain the reason for the missing image/loading failure;
            * NEXT_STEP=CONTINUE;
            * CONTENT: Request to regenerate the current chart (filename must be `<chart_id>_visualization.png`, and print the path).
          - If the image exists, review it based on clarity, color scheme, labels, axis ticks, sampling rationality, and whether it answers the question, then determine CONTINUE / NEXT_CHART / STOP.

          Output Format (Must output):
          ANALYSIS:
          <Issues; if the image is missing, state the missing reason>

          NEXT_STEP:
          CONTINUE / NEXT_CHART / STOP

          CONTENT:
          <CONTINUE: Improvement instructions or "Please regenerate the current chart <chart_id>"; NEXT_CHART: Reason for approval + global suggestions; STOP: Leave empty>
        tooling:
          - type: function
            config:
              auto_load: true
              tools:
                - name: describe_available_files
                - name: load_file
                - name: read_text_file_snippet
  edges:
    - from: Meta Analysis Agent
      to: Profiling Executor
    - from: Profiling Executor
      to: Data Analyst
      condition: code_pass
    - from: Profiling Executor
      to: Meta Analysis Agent
      condition: code_fail
    - from: Data Analyst
      to: Data Cleaner
      condition:
        type: keyword
        config:
          any:
            - CLEAN
          none: []
          regex: []
          case_sensitive: true
    - from: Data Cleaner
      to: Cleaning Executor
    - from: Cleaning Executor
      to: Meta Analysis Agent
      condition: code_pass
    - from: Cleaning Executor
      to: Data Cleaner
      condition: code_fail
    - from: Data Analyst
      to: Visualization Planner
      condition:
        type: keyword
        config:
          any:
            - VISUALIZE
          none: []
          regex: []
          case_sensitive: true
    - from: Visualization Planner
      to: Planning Agent
      keep_message: true
    - from: Planning Agent
      to: Plan Executor
    - from: Plan Executor
      to: Chart Dispatcher
      condition: code_pass
    - from: Plan Executor
      to: Planning Agent
      condition: code_fail
    - from: Chart Dispatcher
      to: Dispatch Executor
    - from: Dispatch Executor
      to: Visualization Programmer
      condition:
        type: keyword
        config:
          any:
            - DRAW
          none: []
          regex: []
          case_sensitive: true
    - from: Dispatch Executor
      to: Visual Expert
      condition:
        type: keyword
        config:
          any:
            - FINISH
          none: []
          regex: []
          case_sensitive: true
    - from: Dispatch Executor
      to: Chart Dispatcher
      condition: code_fail
    - from: Visualization Programmer
      to: Visualization Executor
    - from: Visualization Executor
      to: Visual Expert
      condition: code_pass
    - from: Visualization Executor
      to: Visualization Programmer
      condition: code_fail
    - from: Visual Expert
      to: Visualization Programmer
      condition:
        type: keyword
        config:
          any:
            - CONTINUE
          none: []
          regex: []
          case_sensitive: true
    - from: Visual Expert
      to: Chart Dispatcher
      keep_message: true
      condition:
        type: keyword
        config:
          any:
            - NEXT_CHART
          none: []
          regex: []
          case_sensitive: true
  memory: []
  initial_instruction: Please upload your file.
  start:
    - Meta Analysis Agent
  end:
    - Visual Expert