Merge pull request #1262 from zjyhhhher/ospp/chart-visualization-joy

OSPP/feat: Integrate VMind to enable automated insight annotation and enhance report generation pipeline

.gitignore

@@ -8,6 +8,8 @@ data/
 # Workspace
 workspace/
 
+config/
+
 ### Python ###
 # Byte-compiled / optimized / DLL files
 __pycache__/

.vscode/settings.json

@@ -16,5 +16,6 @@
     },
     "files.insertFinalNewline": true,
     "files.trimTrailingWhitespace": true,
-    "editor.formatOnSave": true
+    "editor.formatOnSave": true,
+    "liveServer.settings.port": 5501
 }

README.md

@@ -185,7 +185,7 @@ OpenManus is built by contributors from MetaGPT. Huge thanks to this agent commu
 ## Cite
 ```bibtex
 @misc{openmanus2025,
-  author = {Xinbin Liang and Jinyu Xiang and Zhaoyang Yu and Jiayi Zhang and Sirui Hong and Sheng Fan and Xiao Tang and Bang Liu and Yuyu Luo and Chenglin Wu},
+  author = {Xinbin Liang and Jinyu Xiang and Zhaoyang Yu and Jiayi Zhang and Sirui Hong and Sheng Fan and Xiao Tang},
   title = {OpenManus: An open-source framework for building general AI agents},
   year = {2025},
   publisher = {Zenodo},

app/agent/data_analysis.py

@@ -4,9 +4,23 @@ from app.agent.toolcall import ToolCallAgent
 from app.config import config
 from app.prompt.visualization import NEXT_STEP_PROMPT, SYSTEM_PROMPT
 from app.tool import Terminate, ToolCollection
-from app.tool.chart_visualization.chart_prepare import VisualizationPrepare
-from app.tool.chart_visualization.data_visualization import DataVisualization
 from app.tool.chart_visualization.python_execute import NormalPythonExecute
+# from app.tool.chart_visualization.chart_prepare import VisualizationPrepare
+# from app.tool.chart_visualization.data_visualization import DataVisualization
+# from app.tool.chart_visualization.initial_report_generation import GenerateInitialReport
+# from app.tool.chart_visualization.final_report_generation import GenerateFinalReport
+# from app.tool.chart_visualization.search_report_template import SearchReportTemplate
+# from app.tool.chart_visualization.report_template_generation import ReportTemplateGeneration
+# from app.tool.chart_visualization.initial_information_collection import InitialInformationCollection
+from app.tool.chart_visualization.chart_prepare import VisualizationPrepare
+from app.tool.chart_visualization.select_insights import SelectInsights
+from app.tool.chart_visualization.add_insights import AddInsights
+from app.tool.chart_visualization.data_visualization import DataVisualization
+from app.tool.chart_visualization.v2.search_html_library import SearchHtmlLibrary
+from app.tool.chart_visualization.v2.initial_report_generation import GenerateInitialReport
+from app.tool.chart_visualization.v2.report_template_generation import ReportTemplateGeneration
+from app.tool.chart_visualization.v2.final_report_generation import GenerateFinalReport
+from app.tool.chart_visualization.v2.report_beautify import ReportBeautify
 
 
 class DataAnalysis(ToolCallAgent):
@@ -18,7 +32,34 @@ class DataAnalysis(ToolCallAgent):
     """
 
     name: str = "Data_Analysis"
-    description: str = "An analytical agent that utilizes python and data visualization tools to solve diverse data analysis tasks"
+    description: str = """
+    A data science agent specializing in Python-based analytics and advanced visualization techniques
+    for solving complex data analysis challenges.
+
+    Standard Report Generation Workflow:
+    1. Template Preparation:
+       - SearchHtmlLibrary: Identify suitable visualization templates
+       - ReportTemplateGeneration & GenerateInitialReport: Create initial report structure
+
+    2. Visualization Pipeline:
+       - VisualizationPrepare: Configure data for visualization
+       - DataVisualization: Generate interactive charts and graphs
+
+    3. Insight Enhancement:
+       - SelectInsights: Extract key findings from visualizations
+       - AddInsights: Annotate charts with analytical insights
+
+    4. Report Finalization:
+       - GenerateFinalReport: Replace the placeholders with charts
+       - ReportBeautify: Apply professional styling and formatting
+
+    Operational Protocol:
+    - First determine optimal visualization types based on dataset characteristics
+    - Utilize HTML template library to establish report framework
+    - Execute visualization pipeline to create data representations
+    - Enhance each chart with key insights you selected
+    - Assemble final report by embedding enriched visualizations
+    """
 
     system_prompt: str = SYSTEM_PROMPT.format(directory=config.workspace_root)
     next_step_prompt: str = NEXT_STEP_PROMPT
@@ -30,8 +71,15 @@ class DataAnalysis(ToolCallAgent):
     available_tools: ToolCollection = Field(
         default_factory=lambda: ToolCollection(
             NormalPythonExecute(),
+            SearchHtmlLibrary(),
+            ReportTemplateGeneration(),
+            GenerateInitialReport(),
+            GenerateFinalReport(),
+            ReportBeautify(),
+            AddInsights(),
             VisualizationPrepare(),
             DataVisualization(),
+            SelectInsights(),
             Terminate(),
         )
     )

app/prompt/visualization.py

@@ -1,7 +1,33 @@
 SYSTEM_PROMPT = """You are an AI agent designed to data analysis / visualization task. You have various tools at your disposal that you can call upon to efficiently complete complex requests.
 # Note:
 1. The workspace directory is: {directory}; Read / write file in workspace
-2. Generate analysis conclusion report in the end"""
+2. Generate analysis conclusion report in the end
+
+Standard Report Generation Workflow:
+1. Template Preparation:
+    - SearchHtmlLibrary: Identify suitable visualization templates
+    - ReportTemplateGeneration & GenerateInitialReport: Create initial report structure
+
+2. Visualization Pipeline:
+    - VisualizationPrepare: Configure data for visualization
+    - DataVisualization: Generate interactive charts and graphs
+
+3. Insight Enhancement:
+    - SelectInsights: Extract key findings from visualizations
+    - AddInsights: Annotate charts with analytical insights
+
+4. Report Finalization:
+    - GenerateFinalReport: Replace the placeholders with charts
+    - ReportBeautify: Apply professional styling and formatting
+
+Operational Protocol:
+- First determine optimal visualization types based on dataset characteristics
+- Utilize HTML template library to establish report framework
+- Execute visualization pipeline to create data representations
+- Enhance each chart with key insights you selected
+- Assemble final report by embedding enriched visualizations
+
+"""
 
 NEXT_STEP_PROMPT = """Based on user needs, break down the problem and use different tools step by step to solve it.
 # Note

app/tool/chart_visualization/__init__.py

@@ -1,6 +1,3 @@
 from app.tool.chart_visualization.chart_prepare import VisualizationPrepare
 from app.tool.chart_visualization.data_visualization import DataVisualization
 from app.tool.chart_visualization.python_execute import NormalPythonExecute
-
-
-__all__ = ["DataVisualization", "VisualizationPrepare", "NormalPythonExecute"]

app/tool/chart_visualization/add_insights.py

@@ -0,0 +1,228 @@
+import sys
+import asyncio
+import json
+import os
+print(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
+
+from typing import Any, Hashable
+
+import pandas as pd
+from pydantic import Field, model_validator
+
+from app.config import config
+from app.llm import LLM
+from app.logger import logger
+from app.tool.base import BaseTool
+
+
+class AddInsights(BaseTool):
+    name: str = "add_insights"
+    description: str = (
+        "Enhances charts by adding insights markers and annotations "
+        "using JSON data generated by the insights_selection tool. "
+        "This creates the final annotated visualization output."
+    )
+
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "json_path": {
+                "type": "string",
+                "description": """Path to the JSON file generated by insights_selection tool.
+Contains chart insights data in format:
+{
+  "chartPath": string,
+  "insights_id": number[]
+}""",
+            },
+            "output_type": {
+                "type": "string",
+                "description": "Visualization output format selection",
+                "default": "html",
+                "enum": [
+                    "png",    # Static image format
+                    "html"    # Interactive web format (recommended)
+                ],
+            },
+        },
+        "required": ["json_path"],
+    }
+    llm: LLM = Field(default_factory=LLM, description="Language model instance")
+
+    @model_validator(mode="after")
+    def initialize_llm(self):
+        """Initialize llm with default settings if not provided."""
+        if self.llm is None or not isinstance(self.llm, LLM):
+            self.llm = LLM(config_name=self.name.lower())
+        return self
+
+    def load_chart_with_css(self, chart_path):
+        # 读取 HTML 文件
+        with open(chart_path, 'r', encoding='utf-8') as f:
+            html_content = f.read()
+        html_content = html_content.replace('`', "'")
+
+        # 在 <head> 里插入 CSS
+        css = """
+        <style>
+            body, html {
+                margin: 0;
+                padding: 0;
+                height: 100%;
+                overflow: hidden;
+            }
+            #chart-container {
+                width: 100%;
+                height: 100%;
+            }
+        </style>
+        """
+
+        # 如果原文件没有 <head>，直接插入到最前面
+        if "<head>" in html_content:
+            html_content = html_content.replace("<head>", "<head>" + css)
+        else:
+            html_content = css + html_content
+
+        with open(chart_path, 'w', encoding='utf-8') as f:
+            f.write(html_content)
+
+    def get_file_path(
+        self,
+        json_info: list[dict[str, str]],
+        path_str: str,
+        directory: str = None,
+    ) -> list[str]:
+        res = []
+        for item in json_info:
+            if os.path.exists(item[path_str]):
+                res.append(item[path_str])
+            elif os.path.exists(
+                os.path.join(f"{directory or config.workspace_root}", item[path_str])
+            ):
+                res.append(
+                    os.path.join(
+                        f"{directory or config.workspace_root}", item[path_str]
+                    )
+                )
+            else:
+                raise Exception(f"No such file or directory: {item[path_str]}")
+        return res
+
+    async def add_insights(
+        self, json_info: list[dict[str, str]], output_type: str
+    ) -> str:
+        data_list = []
+        chart_file_path = self.get_file_path(
+            json_info, "chartPath", os.path.join(config.workspace_root, "visualization")
+        )
+        for index, item in enumerate(json_info):
+            if "insights_id" in item:
+                data_list.append(
+                    {
+                        "file_name": os.path.basename(chart_file_path[index]).replace(
+                            f".{output_type}", ""
+                        ),
+                        "insights_id": item["insights_id"],
+                    }
+                )
+        tasks = [
+            self.invoke_vmind(
+                insights_id=item["insights_id"],
+                file_name=item["file_name"],
+                output_type=output_type,
+                task_type="insight",
+            )
+            for item in data_list
+        ]
+        results = await asyncio.gather(*tasks)
+        error_list = []
+        success_list = []
+        for index, result in enumerate(results):
+            chart_path = chart_file_path[index]
+            if "error" in result and "chart_path" not in result:
+                error_list.append(f"Error in {chart_path}: {result['error']}")
+            else:
+                success_list.append(chart_path)
+                self.load_chart_with_css(chart_path)
+
+        success_template = (
+            f"# Charts Update with Insights\n{','.join(success_list)}"
+            if len(success_list) > 0
+            else ""
+        )
+        if len(error_list) > 0:
+            return {
+                "observation": f"# Error in chart insights:{'\n'.join(error_list)}\n{success_template}",
+                "success": False,
+            }
+        else:
+            return {"observation": f"{success_template}"}
+
+    async def execute(
+        self,
+        json_path: str,
+        output_type: str | None = "html",
+        tool_type: str | None = "visualization",
+        language: str | None = "en",
+    ) -> str:
+        try:
+            logger.info(f"📈 data_visualization with {json_path} in: {tool_type} ")
+            with open(json_path, "r", encoding="utf-8") as file:
+                json_info = json.load(file)
+                return await self.add_insights(json_info, output_type)
+        except Exception as e:
+            return {
+                "observation": f"Error: {e}",
+                "success": False,
+            }
+
+    async def invoke_vmind(
+        self,
+        file_name: str,
+        output_type: str,
+        task_type: str,
+        insights_id: list[str] = None,
+        dict_data: list[dict[Hashable, Any]] = None,
+        chart_description: str = None,
+        language: str = "en",
+    ):
+        llm_config = {
+            "base_url": self.llm.base_url,
+            "model": self.llm.model,
+            "api_key": self.llm.api_key,
+        }
+        vmind_params = {
+            "llm_config": llm_config,
+            "user_prompt": chart_description,
+            "dataset": dict_data,
+            "file_name": file_name,
+            "output_type": output_type,
+            "insights_id": insights_id,
+            "task_type": task_type,
+            "directory": str(config.workspace_root),
+            "language": language,
+        }
+
+        process = await asyncio.create_subprocess_exec(
+            "npx",
+            "ts-node",
+            "src/chartVisualize.ts",
+            stdin=asyncio.subprocess.PIPE,
+            stdout=asyncio.subprocess.PIPE,
+            stderr=asyncio.subprocess.PIPE,
+            cwd=os.path.dirname(__file__),
+        )
+        input_json = json.dumps(vmind_params, ensure_ascii=False).encode("utf-8")
+        try:
+            stdout, stderr = await process.communicate(input_json)
+            stdout_str = stdout.decode("utf-8")
+            stderr_str = stderr.decode("utf-8")
+            if process.returncode == 0:
+                return json.loads(stdout_str)
+            else:
+                return {"error": f"Node.js Error: {stderr_str}"}
+        except Exception as e:
+            return {"error": f"Subprocess Error: {str(e)}"}
+

app/tool/chart_visualization/chart_prepare.py

@@ -1,36 +1,36 @@
 from app.tool.chart_visualization.python_execute import NormalPythonExecute
-
-
 class VisualizationPrepare(NormalPythonExecute):
     """A tool for Chart Generation Preparation"""
 
     name: str = "visualization_preparation"
-    description: str = "Using Python code to generates metadata of data_visualization tool. Outputs: 1) JSON Information. 2) Cleaned CSV data files (Optional)."
+    description: str = """
+    You need some charts to replace initial report's placeholders. So you need to use this tool first to prepare metadata for data_visualization tool.
+    Using Python code to generates metadata of data_visualization tool. Outputs: 1) JSON Information. 2) Cleaned CSV data files (Optional).
+    """
     parameters: dict = {
         "type": "object",
         "properties": {
             "code_type": {
-                "description": "code type, visualization: csv -> chart; insight: choose insight into chart",
+                "description": "code type, visualization: csv -> chart",
                 "type": "string",
-                "default": "visualization",
-                "enum": ["visualization", "insight"],
+                "default": "visualization"
             },
             "code": {
                 "type": "string",
                 "description": """Python code for data_visualization prepare.
-## Visualization Type
+
+## Visualization Type (Initial Step)
 1. Data loading logic
 2. Csv Data and chart description generate
-2.1 Csv data (The data you want to visulazation, cleaning / transform from origin data, saved in .csv)
-2.2 Chart description of csv data (The chart title or description should be concise and clear. Examples: 'Product sales distribution', 'Monthly revenue trend'.)
+   2.1 Csv data (The data you want to visulazation, cleaning / transform from origin data, saved in .csv)
+   2.2 Chart description of csv data (The chart title or description should be concise and clear. Examples: 'Product sales distribution', 'Monthly revenue trend'.)
 3. Save information in json file.( format: {"csvFilePath": string, "chartTitle": string}[])
-## Insight Type
-1. Select the insights from the data_visualization results that you want to add to the chart.
-2. Save information in json file.( format: {"chartPath": string, "insights_id": number[]}[])
-# Note
-1. You can generate one or multiple csv data with different visualization needs.
-2. Make each chart data esay, clean and different.
-3. Json file saving in utf-8 with path print: print(json_path)
+
+
+# Best Practices
+1. Generate one or multiple csv data with different visualization needs based on the initial report
+2. Make each chart data simple, clean and distinct
+4. Json file saving in utf-8 with path print: print(json_path)
 """,
             },
         },

app/tool/chart_visualization/data_visualization.py

@@ -14,12 +14,8 @@ from app.tool.base import BaseTool
 
 class DataVisualization(BaseTool):
     name: str = "data_visualization"
-    description: str = """Visualize statistical chart or Add insights in chart with JSON info from visualization_preparation tool. You can do steps as follows:
-1. Visualize statistical chart
-2. Choose insights into chart based on step 1 (Optional)
-Outputs:
-1. Charts (png/html)
-2. Charts Insights (.md)(Optional)"""
+    description: str = """Visualize statistical chart with JSON info from visualization_preparation tool.
+Outputs: Charts (png/html)"""
     parameters: dict = {
         "type": "object",
         "properties": {
@@ -34,10 +30,9 @@ Outputs:
                 "enum": ["png", "html"],
             },
             "tool_type": {
-                "description": "visualize chart or add insights",
+                "description": "visualize",
                 "type": "string",
                 "default": "visualization",
-                "enum": ["visualization", "insight"],
             },
             "language": {
                 "description": "english(en) / chinese(zh)",
@@ -79,11 +74,43 @@ Outputs:
                 raise Exception(f"No such file or directory: {item[path_str]}")
         return res
 
+    def load_chart_with_css(self, chart_path):
+        # 读取 HTML 文件
+        with open(chart_path, 'r', encoding='utf-8') as f:
+            html_content = f.read()
+
+        # 在 <head> 里插入 CSS
+        css = """
+        <style>
+            body, html {
+                margin: 0;
+                padding: 0;
+                height: 100%;
+                overflow: hidden;
+            }
+            #chart-container {
+                width: 100%;
+                height: 100%;
+            }
+        </style>
+        """
+
+        # 如果原文件没有 <head>，直接插入到最前面
+        if "<head>" in html_content:
+            html_content = html_content.replace("<head>", "<head>" + css)
+        else:
+            html_content = css + html_content
+
+        with open(chart_path, 'w', encoding='utf-8') as f:
+            f.write(html_content)
+
     def success_output_template(self, result: list[dict[str, str]]) -> str:
         content = ""
         if len(result) == 0:
             return "Is EMPTY!"
         for item in result:
+            chart_path=item['chart_path']
+            self.load_chart_with_css(chart_path)
             content += f"""## {item['title']}\nChart saved in: {item['chart_path']}"""
             if "insight_path" in item and item["insight_path"] and "insight_md" in item:
                 content += "\n" + item["insight_md"]
@@ -145,7 +172,7 @@ Outputs:
         else:
             return {"observation": f"{self.success_output_template(success_list)}"}
 
-    async def add_insighs(
+    async def add_insights(
         self, json_info: list[dict[str, str]], output_type: str
     ) -> str:
         data_list = []
@@ -207,7 +234,7 @@ Outputs:
             if tool_type == "visualization":
                 return await self.data_visualization(json_info, output_type, language)
             else:
-                return await self.add_insighs(json_info, output_type)
+                return await self.add_insights(json_info, output_type)
         except Exception as e:
             return {
                 "observation": f"Error: {e}",

app/tool/chart_visualization/package-lock.json

@@ -10,7 +10,7 @@
       "license": "ISC",
       "dependencies": {
         "@visactor/vchart": "^1.13.7",
-        "@visactor/vmind": "2.0.5",
+        "@visactor/vmind": "2.0.6-alpha.2",
         "get-stdin": "^9.0.0",
         "puppeteer": "^24.9.0"
       },
@@ -6319,9 +6319,9 @@
       }
     },
     "node_modules/@visactor/calculator": {
-      "version": "2.0.5",
-      "resolved": "https://registry.npmjs.org/@visactor/calculator/-/calculator-2.0.5.tgz",
-      "integrity": "sha512-/NBDB/wBQLeQuSspDBuiEAbbyfJS/xPX6mubVsLGhfy65UwUBojAQgmX25FcRJnUsRXooK5heshni19DBBf8xA==",
+      "version": "2.0.6-alpha.2",
+      "resolved": "https://registry.npmjs.org/@visactor/calculator/-/calculator-2.0.6-alpha.2.tgz",
+      "integrity": "sha512-eSihYc5cTOeH3gFIW5lBBSWk1PDPDrO/dhaz3G6ZfRRx/wLNf5K1W1jCUaKeNcfcLyydB+6JH7u1k8vm2oIznw==",
       "dependencies": {
         "@visactor/vutils": "~0.19.3",
         "node-sql-parser": "~4.17.0",
@@ -6329,13 +6329,26 @@
       }
     },
     "node_modules/@visactor/chart-advisor": {
-      "version": "2.0.5",
-      "resolved": "https://registry.npmjs.org/@visactor/chart-advisor/-/chart-advisor-2.0.5.tgz",
-      "integrity": "sha512-pvHceRlworB7kDSmbWXUtherLLXh5nMj0aEGuxtzKQyHmeO0sjuu9gGXBFIgscGliSZM4tmeNrFU9eBLGJ8dxw==",
+      "version": "2.0.6-alpha.2",
+      "resolved": "https://registry.npmjs.org/@visactor/chart-advisor/-/chart-advisor-2.0.6-alpha.2.tgz",
+      "integrity": "sha512-QlhM5s3o48QtUDn0VmJB6xwYwBPgUh2SfxYosGKrbmajRFUb8e6I6LDKTNOda2dQ3/e0a04+zZIiDTpViMxkCw==",
+      "license": "MIT",
       "dependencies": {
         "@visactor/vutils": "~0.19.3"
       }
     },
+    "node_modules/@visactor/generate-vchart": {
+      "version": "2.0.6-alpha.2",
+      "resolved": "https://registry.npmjs.org/@visactor/generate-vchart/-/generate-vchart-2.0.6-alpha.2.tgz",
+      "integrity": "sha512-Md62wBLtAwIZ/a04xRyCfgeFDy0sgwUiBei6vD6FoE+vgEkpp9+Vf0jpMXK1sTuxFa3nxi8GxcUsDmiNwEZJ9w==",
+      "dependencies": {
+        "@visactor/vchart-theme": "~1.12.2",
+        "@visactor/vutils": "~0.19.3",
+        "dayjs": "~1.11.10",
+        "node-sql-parser": "~4.17.0",
+        "ts-pattern": "~4.1.4"
+      }
+    },
     "node_modules/@visactor/vchart": {
       "version": "1.13.8",
       "resolved": "https://registry.npmjs.org/@visactor/vchart/-/vchart-1.13.8.tgz",
@@ -6496,14 +6509,16 @@
       }
     },
     "node_modules/@visactor/vmind": {
-      "version": "2.0.5",
-      "resolved": "https://registry.npmjs.org/@visactor/vmind/-/vmind-2.0.5.tgz",
-      "integrity": "sha512-QztQaeSkdeRZYOUlB4qaBpx3/swyO3JzFH8eYvSgvptS/rf8aQDZiufAUasafDLkcME5N6RpBGkcGYIDkmt74Q==",
+      "version": "2.0.6-alpha.2",
+      "resolved": "https://registry.npmjs.org/@visactor/vmind/-/vmind-2.0.6-alpha.2.tgz",
+      "integrity": "sha512-bPuZ4U7dIxHbYnu1oSuddJc5HvRN194Yair9kYqlx4fBqNukHAqWAUEyCdir9YzyJVDvSdEJ7xqmvXecW5W3WQ==",
+      "license": "MIT",
       "dependencies": {
         "@stdlib/stats-base-dists-t-quantile": "0.2.1",
-        "@visactor/calculator": "2.0.5",
-        "@visactor/chart-advisor": "2.0.5",
-        "@visactor/vchart-theme": "^1.11.2",
+        "@visactor/calculator": "2.0.6-alpha.2",
+        "@visactor/chart-advisor": "2.0.6-alpha.2",
+        "@visactor/generate-vchart": "2.0.6-alpha.2",
+        "@visactor/vchart-theme": "~1.12.2",
         "@visactor/vdataset": "~0.19.3",
         "@visactor/vutils": "~0.19.3",
         "alasql": "~4.3.2",

app/tool/chart_visualization/package.json

@@ -9,7 +9,7 @@
   },
   "dependencies": {
     "@visactor/vchart": "^1.13.7",
-    "@visactor/vmind": "2.0.5",
+    "@visactor/vmind": "2.0.6-alpha.2",
     "get-stdin": "^9.0.0",
     "puppeteer": "^24.9.0"
   },

app/tool/chart_visualization/select_insights.py

@@ -0,0 +1,54 @@
+from app.tool.chart_visualization.python_execute import NormalPythonExecute
+class SelectInsights(NormalPythonExecute):
+    name: str = "insights_selection"
+    description: str = (
+        "This tool analyzes data_visualization tool's outputs and identifies key data insights for each chart."
+        "based on their importance ranking. Insights are prioritized in three tiers:\n"
+        "1 **Critical Insights**: 'abnormal_trend', 'abnormal_band', 'turning_point', 'overall_trend'\n"
+        "2 **Important Insights**: 'outlier', 'extreme_value', 'majority_value', 'avg'\n"
+        "3 **Basic Insights**: 'min', 'max'\n\n"
+        "**!Must be called immediately after data_visualization completes!**"
+        "**!All insights_id must come from the data_visualization analysis results!**"
+
+    )
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "code": {
+                "type": "string",
+                "description": """Python code to analyze visualized charts and extract insights.
+
+# PRIORITY REQUIREMENTS
+Insights must be selected and ranked according to these importance tiers:
+1. **First Priority**: Always include 'abnormal_trend', 'abnormal_band', 'turning_point', 'overall_trend' when present
+2. **Second Priority**: Include 'outlier', 'extreme_value', 'majority_value', 'avg' if no first-tier insights exist
+3. **Third Priority**: Fall back to 'min', 'max' only when no higher-priority insights are available
+
+# EXECUTION REQUIREMENTS
+1. **Timing**: MUST be called immediately after data_visualization completes
+2. **Dependency**: MUST use insights from data_visualization output as the only source for insights_id
+
+
+# CODE REQUIREMENTS
+Your Python code must:
+1. Analyze the data_visualization results to identify significant insights for each chart.
+2. Save the findings in JSON format:
+   ```json
+   [
+    {
+     "chartPath": "string",  // Path to the generated chart
+     "insights_id": number[] // Array of key insight IDs FROM DATA_VISUALIZATION RESULTS
+    },
+    {
+     "chartPath": "string",  // Path to the generated chart
+     "insights_id": number[] // Array of key insight IDs FROM DATA_VISUALIZATION RESULTS
+    },
+    ...
+    ]
+    ```
+Json file saving in utf-8 with path print: print(json_path)
+""",
+            },
+        },
+        "required": ["code"],
+    }

app/tool/chart_visualization/src/chartVisualize.ts

@@ -1,7 +1,8 @@
 import path from "path";
 import fs from "fs";
 import puppeteer from "puppeteer";
-import VMind, { ChartType, DataTable } from "@visactor/vmind";
+import VMind, { ChartType } from "@visactor/vmind";
+import type { DataTable } from '@visactor/generate-vchart';
 import { isString } from "@visactor/vutils";
 
 enum AlgorithmType {
@@ -63,8 +64,7 @@ function getHtmlVChart(spec: any, width?: number, height?: number) {
     <script src="https://unpkg.com/@visactor/vchart/build/index.min.js"></script>
 </head>
 <body>
-    <div id="chart-container" style="width: ${
-      width ? `${width}px` : "100%"
+    <div id="chart-container" style="width: ${width ? `${width}px` : "100%"
     }; height: ${height ? `${height}px` : "100%"};"></div>
     <script>
       // parse spec with function
@@ -82,7 +82,7 @@ function getHtmlVChart(spec: any, width?: number, height?: number) {
           return v;
         });
       }
-      const spec = parseSpec(\`${serializeSpec(spec)}\`);
+      const spec = parseSpec(\'${serializeSpec(spec)}\');
       const chart = new VChart.VChart(spec, {
           dom: 'chart-container'
       });
@@ -242,7 +242,7 @@ async function generateChart(
         ChartType.AreaChart,
         ChartType.ScatterPlot,
         ChartType.DualAxisChart,
-      ].includes(chartType)
+      ].includes(chartType as ChartType)
     ) {
       const { insights: vmindInsights } = await vmind.getInsights(spec, {
         maxNum: 6,
@@ -295,11 +295,14 @@ async function updateChartWithInsight(
   }
 ) {
   const { directory, outputType, fileName, insightsId } = options;
+  //???????为什么打印不出来？？？？？？？
+  //console.log(options)
   let res: { error?: string; chart_path?: string } = {};
   try {
     const specPath = getSavedPathName(directory, fileName, "json", true);
     const spec = JSON.parse(fs.readFileSync(specPath, "utf8"));
     // llm select index from 1
+    //console.log(spec)
     const insights = (spec.insights || []).filter(
       (_insight: any, index: number) => insightsId.includes(index + 1)
     );

app/tool/chart_visualization/test/chart_demo.py

@@ -1,5 +1,8 @@
 import asyncio
-
+import os
+import sys
+print(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
 from app.agent.data_analysis import DataAnalysis
 from app.logger import logger
 
@@ -7,173 +10,87 @@ from app.logger import logger
 prefix = "Help me generate charts and save them locally, specifically:"
 tasks = [
     {
-        "prompt": "Help me show the sales of different products in different regions",
-        "data": """Product Name,Region,Sales
-Coke,South,2350
-Coke,East,1027
-Coke,West,1027
-Coke,North,1027
-Sprite,South,215
-Sprite,East,654
-Sprite,West,159
-Sprite,North,28
-Fanta,South,345
-Fanta,East,654
-Fanta,West,2100
-Fanta,North,1679
-Xingmu,South,1476
-Xingmu,East,830
-Xingmu,West,532
-Xingmu,North,498
-""",
-    },
-    {
-        "prompt": "Show market share of each brand",
-        "data": """Brand Name,Market Share,Average Price,Net Profit
-Apple,0.5,7068,314531
-Samsung,0.2,6059,362345
-Vivo,0.05,3406,234512
-Nokia,0.01,1064,-1345
-Xiaomi,0.1,4087,131345""",
-    },
-    {
-        "prompt": "Please help me show the sales trend of each product",
-        "data": """Date,Type,Value
-2023-01-01,Product A,52.9
-2023-01-01,Product B,63.6
-2023-01-01,Product C,11.2
-2023-01-02,Product A,45.7
-2023-01-02,Product B,89.1
-2023-01-02,Product C,21.4
-2023-01-03,Product A,67.2
-2023-01-03,Product B,82.4
-2023-01-03,Product C,31.7
-2023-01-04,Product A,80.7
-2023-01-04,Product B,55.1
-2023-01-04,Product C,21.1
-2023-01-05,Product A,65.6
-2023-01-05,Product B,78
-2023-01-05,Product C,31.3
-2023-01-06,Product A,75.6
-2023-01-06,Product B,89.1
-2023-01-06,Product C,63.5
-2023-01-07,Product A,67.3
-2023-01-07,Product B,77.2
-2023-01-07,Product C,43.7
-2023-01-08,Product A,96.1
-2023-01-08,Product B,97.6
-2023-01-08,Product C,59.9
-2023-01-09,Product A,96.1
-2023-01-09,Product B,100.6
-2023-01-09,Product C,66.8
-2023-01-10,Product A,101.6
-2023-01-10,Product B,108.3
-2023-01-10,Product C,56.9""",
-    },
-    {
-        "prompt": "Show the popularity of search keywords",
-        "data": """Keyword,Popularity
-Hot Word,1000
-Zao Le Wo Men,800
-Rao Jian Huo,400
-My Wish is World Peace,400
-Xiu Xiu Xiu,400
-Shenzhou 11,400
-Hundred Birds Facing the Wind,400
-China Women's Volleyball Team,400
-My Guan Na,400
-Leg Dong,400
-Hot Pot Hero,400
-Baby's Heart is Bitter,400
-Olympics,400
-Awesome My Brother,400
-Poetry and Distance,400
-Song Joong-ki,400
-PPAP,400
-Blue Thin Mushroom,400
-Rain Dew Evenly,400
-Friendship's Little Boat Says It Flips,400
-Beijing Slump,400
-Dedication,200
-Apple,200
-Dog Belt,200
-Old Driver,200
-Melon-Eating Crowd,200
-Zootopia,200
-City Will Play,200
-Routine,200
-Water Reverse,200
-Why Don't You Go to Heaven,200
-Snake Spirit Man,200
-Why Don't You Go to Heaven,200
-Samsung Explosion Gate,200
-Little Li Oscar,200
-Ugly People Need to Read More,200
-Boyfriend Power,200
-A Face of Confusion,200
-Descendants of the Sun,200""",
-    },
-    {
-        "prompt": "Help me compare the performance of different electric vehicle brands using a scatter plot",
-        "data": """Range,Charging Time,Brand Name,Average Price
-2904,46,Brand1,2350
-1231,146,Brand2,1027
-5675,324,Brand3,1242
-543,57,Brand4,6754
-326,234,Brand5,215
-1124,67,Brand6,654
-3426,81,Brand7,159
-2134,24,Brand8,28
-1234,52,Brand9,345
-2345,27,Brand10,654
-526,145,Brand11,2100
-234,93,Brand12,1679
-567,94,Brand13,1476
-789,45,Brand14,830
-469,75,Brand15,532
-5689,54,Brand16,498
-""",
-    },
-    {
-        "prompt": "Show conversion rates for each process",
-        "data": """Process,Conversion Rate,Month
-Step1,100,1
-Step2,80,1
-Step3,60,1
-Step4,40,1""",
-    },
-    {
-        "prompt": "Show the difference in breakfast consumption between men and women",
-        "data": """Day,Men-Breakfast,Women-Breakfast
-Monday,15,22
-Tuesday,12,10
-Wednesday,15,20
-Thursday,10,12
-Friday,13,15
-Saturday,10,15
-Sunday,12,14""",
-    },
-    {
-        "prompt": "Help me show this person's performance in different aspects, is he a hexagonal warrior",
-        "data": """dimension,performance
-Strength,5
-Speed,5
-Shooting,3
-Endurance,5
-Precision,5
-Growth,5""",
-    },
-    {
-        "prompt": "Show data flow",
-        "data": """Origin,Destination,value
-Node A,Node 1,10
-Node A,Node 2,5
-Node B,Node 2,8
-Node B,Node 3,2
-Node C,Node 2,4
-Node A,Node C,2
-Node C,Node 1,2""",
-    },
+        "prompt": "Help me show the daily sales performance metrics over time. ",
+        "data":
+            """Table: 每日销售数据明细​
+            OrderDate,RegionCode,SalesAmount,DataQuality
+            2023-04-01,SW,25860.24,正常
+            2023-04-01,NE,5877.65,正常
+            2023-04-01,C,34271.58,正常
+            2023-04-01,N,6251.42,正常
+            2023-04-01,E,3113.46,正常
+            2023-04-02,NW,87717.84,正常
+            2023-04-02,E,53058.96,正常
+            2023-04-02,N,14409.92,正常
+            2023-04-02,NE,5540.92,正常
+            2023-04-03,C,41802.49,正常
+            2023-04-03,NE,9202.20,正常
+            2023-04-03,SW,583.30,正常
+            2023-04-03,N,560.56,正常
+            2023-04-03,E,96269.32,正常
+            2023-04-04,NE,106208.48,正常
+            2023-04-04,C,6231.62,正常
+            2023-04-04,E,84454.83,正常
+            2023-04-05,NE,312.82,正常
+            2023-04-05,SW,5718.89,正常
+            2023-04-05,C,39811.91,正常
+            2023-04-05,E,244163.47,正常
+            2023-04-05,N,79487.41,正常
+            2023-04-06,C,28648.34,正常
+            2023-04-06,N,18288.76,正常
+            2023-04-08,SW,67434.58,正常
+            2023-04-08,C,1176.00,正常
+            2023-04-08,NW,81264.54,正常
+            2023-04-08,E,87750.96,正常
+            2023-04-09,SW,67434.58,正常
+            2023-04-09,C,5902.34,正常
+            2023-04-09,E,22252.27,正常
+            2023-04-09,NE,98844.76,正常
+            2023-04-10,E,2677.36,正常
+            2023-04-10,SW,1444.52,正常
+            2023-04-10,NE,62082.61,正常
+            2023-04-10,C,677.38,正常
+            2023-04-11,SW,776.16,正常
+            2023-04-11,C,7487.00,正常
+            2023-04-11,E,57016.40,正常
+            2023-04-12,SW,7131.85,正常
+            2023-04-12,E,11837.81,正常
+            2023-04-12,C,207763.14,正常
+            2023-04-13,C,24299.30,正常
+            2023-04-13,E,9847.04,正常
+            2023-04-13,NE,15919.12,正常
+            2023-04-15,SW,33544.42,正常
+            2023-04-15,C,39935.98,正常
+            2023-04-15,N,60416.80,正常
+            2023-04-15,NE,1234.80,正常
+            2023-04-15,E,36007.16,正常
+            2023-04-16,E,108484.04,正常
+            2023-04-16,SW,2450.00,正常
+            2023-04-17,E,78099.14,正常
+            2023-04-17,C,49350.84,正常
+            2023-04-17,N,18480.84,正常
+            2023-04-18,NE,48419.84,正常
+            2023-04-18,C,118951.67,正常
+            2023-04-18,N,10853.89,正常
+            2023-04-18,SW,1627.58,正常
+            2023-04-18,E,32777.28,正常
+            2023-04-19,N,41905.78,正常
+            2023-04-19,E,47942.58,正常
+            2023-04-19,NE,48259.51,正常
+            2023-04-19,C,18021.22,正常
+            2023-04-22,NE,58530.50,正常
+            2023-04-22,E,22004.72,正常
+            2023-04-22,C,7729.26,正常
+            2023-04-23,E,49218.54,正常
+            2023-04-23,NE,13944.42,正常
+            2023-04-23,SW,3843.56,正常
+            2023-04-24,SW,16754.08,正常
+            2023-04-24,NE,2343.18,正常
+            2023-04-24,E,41413.82,正常
+            2023-04-24,C,723.24,正常
+            2023-04-25,C,10678.08,正常
+            2023-04-25,E,44791.10,正常"""
+    }
 ]
 
 

app/tool/chart_visualization/test/education_report.py

@@ -0,0 +1,84 @@
+import asyncio
+import os
+import sys
+print(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
+from app.agent.data_analysis import DataAnalysis
+from app.agent.manus import Manus
+
+
+# from app.agent.manus import Manus
+
+
+async def main():
+    # agent = DataAnalysis()
+    agent = Manus()
+    await agent.run(
+"""Requirement: Analyze the following data and generate a simple data report with some charts in HTML format.
+
+Table1: 学生基本信息
+Name,Age,Gender,Grade,Class,StudentID,EnrollmentDate,GuardianName,GuardianPhone,Address,PreviousSchool
+王悦,16,女,高一,3班,S20230147,2023-09-01,王建国,138-1234-5678,北京市海淀区学院路101号,海淀实验中学
+
+Table2: 学科成绩详细数据
+Subject,Teacher,TestType,TestDate,Score,ClassRank,GradeRank,ScoreChange,DifficultyLevel
+数学,张老师,单元测验,2023-09-05,82,12,45,-,中等
+数学,张老师,周测,2023-09-12,85,10,38,+3,中等
+数学,张老师,月考,2023-09-28,87,8,35,+2,中等偏难
+数学,张老师,期中考试,2023-10-15,92,5,22,+5,难
+语文,李老师,单元测验,2023-09-06,80,15,50,-,中等
+语文,李老师,周测,2023-09-13,83,12,42,+3,中等
+语文,李老师,月考,2023-09-29,85,10,40,+2,中等偏难
+语文,李老师,期中考试,2023-10-16,88,8,35,+3,难
+英语,王老师,单元测验,2023-09-07,87,8,30,-,中等
+英语,王老师,周测,2023-09-14,89,6,25,+2,中等
+英语,王老师,月考,2023-09-30,90,5,20,+1,中等偏难
+英语,王老师,期中考试,2023-10-17,93,4,18,+3,难
+物理,刘老师,单元测验,2023-09-08,75,18,65,-,难
+物理,刘老师,周测,2023-09-15,77,16,60,+2,难
+物理,刘老师,月考,2023-10-01,78,15,58,+1,难
+物理,刘老师,期中考试,2023-10-18,85,12,40,+7,中等偏难
+化学,陈老师,单元测验,2023-09-09,79,14,55,-,中等偏难
+化学,陈老师,周测,2023-09-16,81,12,50,+2,中等偏难
+化学,陈老师,月考,2023-10-02,82,11,48,+1,中等偏难
+化学,陈老师,期中考试,2023-10-19,84,10,45,+2,中等
+
+Table3: 学习行为日数据(2023年9月)
+Date,Weekday,StudyHours,HomeworkHours,ReadingMinutes,ScreenTime,PhysicalActivity,ClassAttendance,ParticipationScore,SleepHours,MoodScore
+2023-09-01,周五,3.5,2.0,45,1.5,1.0,出勤,85,8.2,4
+2023-09-02,周六,4.0,1.5,30,2.0,0.8,出勤,90,7.8,5
+2023-09-03,周日,3.0,2.5,60,1.0,1.2,出勤,88,8.5,4
+2023-09-04,周一,5.0,2.0,50,1.2,0.7,出勤,92,7.9,5
+2023-09-05,周二,4.5,1.8,40,1.8,1.0,出勤,87,8.0,4
+2023-09-06,周三,3.8,2.2,55,1.3,0.9,出勤,89,8.3,5
+2023-09-07,周四,4.2,1.7,35,1.6,1.1,出勤,91,7.7,4
+2023-09-08,周五,3.5,2.1,48,1.4,0.8,出勤,86,8.1,3
+2023-09-09,周六,4.8,1.9,42,1.7,1.3,出勤,93,7.6,5
+2023-09-10,周日,3.2,2.3,52,1.1,0.7,出勤,88,8.4,4
+2023-09-11,周一,4.5,1.6,38,1.9,1.0,出勤,90,7.9,5
+2023-09-12,周二,3.9,2.4,57,1.2,0.9,出勤,87,8.2,4
+2023-09-13,周三,4.1,1.8,44,1.5,1.2,出勤,91,7.8,5
+2023-09-14,周四,3.7,2.6,49,1.4,0.8,出勤,89,8.3,4
+2023-09-15,周五,4.3,1.9,36,1.8,1.1,出勤,92,7.7,5
+2023-09-16,周六,3.4,2.2,53,1.3,0.9,出勤,86,8.1,4
+2023-09-17,周日,4.6,1.7,41,1.6,1.3,出勤,90,7.6,5
+2023-09-18,周一,3.8,2.5,47,1.2,0.7,出勤,88,8.4,4
+2023-09-19,周二,4.2,1.8,39,1.7,1.0,出勤,91,7.9,5
+2023-09-20,周三,3.9,2.3,51,1.4,0.8,出勤,87,8.2,4
+2023-09-21,周四,4.4,1.6,43,1.5,1.2,出勤,93,7.8,5
+2023-09-22,周五,3.6,2.4,46,1.3,0.9,出勤,89,8.3,4
+2023-09-23,周六,4.7,1.9,37,1.8,1.1,出勤,86,7.7,5
+2023-09-24,周日,3.5,2.1,50,1.4,0.8,出勤,90,8.1,4
+2023-09-25,周一,4.3,1.7,42,1.6,1.3,出勤,88,7.6,5
+2023-09-26,周二,3.8,2.6,48,1.2,0.7,出勤,91,8.4,4
+2023-09-27,周三,4.1,1.8,40,1.7,1.0,出勤,87,7.9,5
+2023-09-28,周四,3.9,2.3,52,1.5,0.9,出勤,92,8.2,4
+2023-09-29,周五,4.5,1.6,45,1.4,1.2,出勤,89,7.8,5
+2023-09-30,周六,3.7,2.4,44,1.3,0.8,出勤,86,8.3,4
+
+"""
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

app/tool/chart_visualization/test/health_report.py

@@ -0,0 +1,75 @@
+import asyncio
+import os
+import sys
+print(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
+from app.agent.data_analysis import DataAnalysis
+from app.agent.manus import Manus
+
+
+# from app.agent.manus import Manus
+
+
+async def main():
+    # agent = DataAnalysis()
+    agent = Manus()
+    await agent.run(
+"""Requirement: Analyze the following data and generate a graphical data report in HTML format. The final product should be a data report.
+Table1: 用户基本信息
+Name,Age,Gender,Height(cm),Weight(kg),MeasureDate
+张三,45,男,170,98.6,2023-10-01
+
+Table2: 身体成分数据
+Date,BodyFat(%),MuscleMass(kg),WaterContent(%),BoneMass(kg),VisceralFat,Protein(%)
+2023-10-01,35.2,42.3,43.1,2.8,18,14.2
+
+Table3: 每日营养活动数据
+Date,CaloriesIntake(kcal),CaloriesBurned(kcal),ProteinIntake(g),CarbIntake(g),FatIntake(g),Steps,ActiveMinutes,SleepHours,MorningWeight(kg)
+2023-09-01,3850,1250,85,480,120,3200,12,5.2,99.2
+2023-09-02,4200,1100,90,520,135,2800,8,4.8,99.5
+2023-09-03,3600,950,78,450,110,2500,5,5.5,99.8
+2023-09-04,3950,1050,82,490,125,2900,10,4.5,100.1
+2023-09-05,4100,1150,88,510,130,3100,15,5.0,100.3
+2023-09-06,3750,980,80,460,115,2600,6,4.7,100.6
+2023-09-07,4300,1200,92,540,140,3300,18,4.3,100.9
+2023-09-08,3450,900,75,430,105,2400,4,5.2,101.2
+2023-09-09,4000,1000,84,500,122,2700,9,4.9,101.5
+2023-09-10,3800,975,79,470,118,2550,7,5.1,101.8
+2023-09-11,4150,1100,86,515,128,3000,14,4.6,102.0
+2023-09-12,3550,925,77,440,108,2300,3,5.4,102.3
+2023-09-13,4250,1180,94,530,138,3400,20,4.2,102.6
+2023-09-14,3700,990,81,465,113,2650,8,5.3,102.9
+2023-09-15,4050,1070,87,505,127,2950,13,4.8,103.1
+2023-09-16,3500,910,76,435,104,2250,2,5.6,103.4
+2023-09-17,3900,1020,83,485,120,2750,11,4.7,103.7
+2023-09-18,4350,1250,96,550,145,3500,22,4.0,104.0
+2023-09-19,3650,960,79,455,112,2450,5,5.2,104.3
+2023-09-20,4000,1030,85,495,124,2850,12,4.9,104.5
+2023-09-21,3450,890,74,425,103,2200,1,5.5,104.8
+2023-09-22,3850,995,82,475,119,2700,10,4.6,105.1
+2023-09-23,4100,1120,89,515,131,3050,16,4.3,105.4
+2023-09-24,3550,935,77,440,107,2350,4,5.4,105.7
+2023-09-25,3950,1010,84,490,123,2800,13,4.8,106.0
+2023-09-26,4200,1150,91,525,136,3200,19,4.1,106.3
+2023-09-27,3600,945,78,445,109,2400,6,5.3,106.6
+2023-09-28,4050,1080,87,505,128,2900,15,4.7,106.9
+2023-09-29,3750,970,80,460,116,2600,9,5.0,107.2
+2023-09-30,4300,1220,95,540,142,3350,23,3.9,107.5
+
+Table4: 健康指标参考范围
+Category,SubCategory,Range,Unit
+BMI,Underweight,<18.5,kg/m²
+BMI,Normal,18.5-24.9,kg/m²
+BMI,Overweight,25-29.9,kg/m²
+BMI,Obese,≥30,kg/m²
+BodyFat,Male(40-59),11-22,%
+BodyFat,Female(40-59),23-34,%
+VisceralFat,Normal,1-9,Level
+VisceralFat,High,10-14,Level
+VisceralFat,Very High,≥15,Level
+Sleep,Recommended,7-9,hours
+Steps,Active,≥10000,steps/day
+"""
+    )
+if __name__ == "__main__":
+    asyncio.run(main())

app/tool/chart_visualization/test/production_report.py

@@ -0,0 +1,123 @@
+import asyncio
+import os
+import sys
+print(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))))
+from app.agent.data_analysis import DataAnalysis
+from app.agent.manus import Manus
+
+
+# from app.agent.manus import Manus
+
+
+async def main():
+    # agent = DataAnalysis()
+    agent = Manus()
+    await agent.run(
+"""Requirement: Analyze the following data and generate a graphical data report in HTML format. The final product should be a data report.
+Table1: 销售区域基本信息​
+RegionCode,RegionName,Description
+SW,西南,西南地区销售数据
+NE,东北,东北地区销售数据
+C,中南,中南地区销售数据
+N,华北,华北地区销售数据
+E,华东,华东地区销售数据
+NW,西北,西北地区销售数据
+
+Table2: 每日销售数据明细​
+OrderDate,RegionCode,SalesAmount,DataQuality
+2023-04-01,SW,25860.24,正常
+2023-04-01,NE,5877.65,正常
+2023-04-01,C,34271.58,正常
+2023-04-01,N,6251.42,正常
+2023-04-01,E,3113.46,正常
+2023-04-02,NW,87717.84,正常
+2023-04-02,E,53058.96,正常
+2023-04-02,N,14409.92,正常
+2023-04-02,NE,5540.92,正常
+2023-04-03,C,41802.49,正常
+2023-04-03,NE,9202.20,正常
+2023-04-03,SW,583.30,正常
+2023-04-03,N,560.56,正常
+2023-04-03,E,96269.32,正常
+2023-04-04,NE,106208.48,正常
+2023-04-04,C,6231.62,正常
+2023-04-04,E,84454.83,正常
+2023-04-05,NE,312.82,正常
+2023-04-05,SW,5718.89,正常
+2023-04-05,C,39811.91,正常
+2023-04-05,E,244163.47,正常
+2023-04-05,N,79487.41,正常
+2023-04-06,C,28648.34,正常
+2023-04-06,N,18288.76,正常
+2023-04-08,SW,67434.58,正常
+2023-04-08,C,1176.00,正常
+2023-04-08,NW,81264.54,正常
+2023-04-08,E,87750.96,正常
+2023-04-09,SW,67434.58,正常
+2023-04-09,C,5902.34,正常
+2023-04-09,E,22252.27,正常
+2023-04-09,NE,98844.76,正常
+2023-04-10,E,2677.36,正常
+2023-04-10,SW,1444.52,正常
+2023-04-10,NE,62082.61,正常
+2023-04-10,C,677.38,正常
+2023-04-11,SW,776.16,正常
+2023-04-11,C,7487.00,正常
+2023-04-11,E,57016.40,正常
+2023-04-12,SW,7131.85,正常
+2023-04-12,E,11837.81,正常
+2023-04-12,C,207763.14,正常
+2023-04-13,C,24299.30,正常
+2023-04-13,E,9847.04,正常
+2023-04-13,NE,15919.12,正常
+2023-04-15,SW,33544.42,正常
+2023-04-15,C,39935.98,正常
+2023-04-15,N,60416.80,正常
+2023-04-15,NE,1234.80,正常
+2023-04-15,E,36007.16,正常
+2023-04-16,E,108484.04,正常
+2023-04-16,SW,2450.00,正常
+2023-04-17,E,78099.14,正常
+2023-04-17,C,49350.84,正常
+2023-04-17,N,18480.84,正常
+2023-04-18,NE,48419.84,正常
+2023-04-18,C,118951.67,正常
+2023-04-18,N,10853.89,正常
+2023-04-18,SW,1627.58,正常
+2023-04-18,E,32777.28,正常
+2023-04-19,N,41905.78,正常
+2023-04-19,E,47942.58,正常
+2023-04-19,NE,48259.51,正常
+2023-04-19,C,18021.22,正常
+2023-04-22,NE,58530.50,正常
+2023-04-22,E,22004.72,正常
+2023-04-22,C,7729.26,正常
+2023-04-23,E,49218.54,正常
+2023-04-23,NE,13944.42,正常
+2023-04-23,SW,3843.56,正常
+2023-04-24,SW,16754.08,正常
+2023-04-24,NE,2343.18,正常
+2023-04-24,E,41413.82,正常
+2023-04-24,C,723.24,正常
+2023-04-25,C,10678.08,正常
+2023-04-25,E,44791.10,正常
+
+Table3: 销售指标分析参考​
+Category,SubCategory,Range,Level,Description
+销售金额,日销售额,>50000,优秀,单日销售额超过5万
+销售金额,日销售额,20000-50000,良好,单日销售额2-5万
+销售金额,日销售额,5000-20000,一般,单日销售额0.5-2万
+销售金额,日销售额,<5000,待提升,单日销售额低于5千
+区域表现,华东地区,持续领先,优秀,销售额稳定高位
+区域表现,中南地区,波动较大,关注,销售额波动明显
+区域表现,西南地区,整体偏低,待提升,需要业务拓展
+数据完整性,日期覆盖,4月1-25日,完整,覆盖主要业务日期
+数据质量,金额精度,两位小数,标准,财务数据标准格式
+业务连续性,工作日,正常营业,良好,除周末外正常营业
+"""
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

app/tool/chart_visualization/v2/final_report_generation.py

@@ -0,0 +1,47 @@
+from app.tool.chart_visualization.python_execute import NormalPythonExecute
+
+class GenerateFinalReport(NormalPythonExecute):
+    """A tool for generating final data analysis report"""
+
+    name: str = "generate_final_report"
+    description: str = """Replace the all placeholders in initial report and refine to generate final report.
+    Outputs: 1) HTML report file path"""
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "code_type": {
+                "description": "code type, replacing: html with placeholders -> html with specific chart; check_refine: Make final adjustments and checks on the report",
+                "type": "string",
+                "default": "replacing",
+                "enum": ["replacing", "check_refine"],
+            },
+            "code": {
+                "type": "string",
+                "description": """Python code for replacing chart placeholders with specific chart file path, or for report checking and refining.
+# Replacing Type
+1. Find all placeholders
+2. When replacing the chart path placeholder, use a relative path. ** src="/workspace/visualization/chart_name.html" **
+3. When replacing the text placeholders, generate a detailed text description.
+   **When filling in the key insights below the charts, the insights must correspond with those previously added by the add insight tool.**
+
+## Notice:
+Use the absolute path starting with /workspace, for example, **/workspace/visualization/chart_name.html**
+
+# Check_refine Type:
+1. Check the entire html file
+- Have all placeholders been filled?
+- Whether the path of the chart is: /workspace/visualization/***.html
+- If text-related placeholders still exist, please fill them in as much as possible. If chart path placeholders still exist, please delete that chart part of the report.
+   **When filling in the key insights below the charts, the insights must correspond with those previously added by the add insight tool.**
+
+
+
+## Output Requirements
+1. Generate **report.html** file
+2. Print the file path: print(report_path)
+3. Make sure the HTML includes Bootstrap for responsive design
+""",
+            },
+        },
+        "required": ["code", "code_type"],
+    }

app/tool/chart_visualization/v2/initial_report_generation.py

@@ -0,0 +1,35 @@
+from app.tool.chart_visualization.python_execute import NormalPythonExecute
+from typing import ClassVar
+from pathlib import Path
+
+class GenerateInitialReport(NormalPythonExecute):
+    """A tool for generating initial data analysis reports based on the report template and user's input data"""
+
+    name: str = "generate_initial_report"
+    description: str = """Generates an initial HTML data analysis report based on the report template and user's input data.
+    After searhing and reading the report template, you should dynamically adapt the template content according to user input data,
+    intelligently determine which charts should be included in the report,
+    and automatically populate the fillable placeholders with corresponding data.
+
+    Outputs: 1) HTML report file path"""
+
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "code": {
+                "type": "string",
+                "description": f"""Python code for generating initial HTML data report based on the report template and user's input data.
+
+## Output Requirements
+1. Generate initial_report.html file
+2. Print the file path: print(report_path)
+
+# Notes:
+1. Refer to the searched report templates
+2. Complete the applicable placeholders, and leave any unfilled ones as '[placeholder: ...]'.
+
+""",
+            },
+        },
+        "required": ["code", "code_type"],
+    }

app/tool/chart_visualization/v2/report_beautify.py

@@ -0,0 +1,48 @@
+from app.tool.chart_visualization.python_execute import NormalPythonExecute
+
+class ReportBeautify(NormalPythonExecute):
+    """A tool for transforming a basic health report into a professional, visually appealing final version"""
+
+    name: str = "report_beautify"
+    description: str = """
+    This tool should be called **LAST** in the workflow to perform final beautification of the data report.
+    It will:
+    1. Apply advanced styling and layout enhancements
+    2. Add interactive elements and visual polish
+    3. Ensure mobile responsiveness
+
+    Key beautification features to implement:
+    - colorful and fancy background and color scheme
+    - Modern CSS styling with gradients and shadows
+    - Font Awesome icons for visual cues
+    - Animated progress bars for metrics
+    - Card-based layout with hover effects
+    - Responsive design for all devices
+    - Scroll-triggered animations
+    - Professional typography hierarchy
+
+    ## Output Requirements
+    1. Generate **beautify_report.html** file
+    2. Print the file path: print(report_path)
+    """
+
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "code": {
+                "type": "string",
+                "description": """
+                Python code that beautify the report:
+                1. CSS/JS enhancements for modern styling
+                2. Structure optimization for better readability
+                3. Mobile responsiveness adjustments
+
+                Example tasks:
+                - Add Bootstrap 5 + Font Awesome
+                - Add metric cards with progress bars
+                - Create responsive tables
+                """,
+            },
+        },
+        "required": ["code"],
+    }

app/tool/chart_visualization/v2/report_template_generation.py

@@ -0,0 +1,82 @@
+from app.tool.base import BaseTool, ToolResult
+from typing import ClassVar
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pathlib import Path
+from app.config import config
+from app.tool.chart_visualization.python_execute import NormalPythonExecute
+
+
+
+class ReportTemplateGeneration(NormalPythonExecute):
+    """A tool for generating the report template in users' local file system"""
+
+    name: str = "report_template_generation"
+    description: str = """Generate a customized HTML report template based on user input data and the natural language description of the report from the previous step.
+
+    When user requires a report, you need to use the search_html_library tool first, and then use this tool based on your search result.
+    In this tool, you will need to process two input parameters to generate a customized HTML report.
+    (1) First Input Parameter: Analyze the user-provided dataset for the report and generate: A natural language suggestion for the HTML report customization (e.g., layout, structure, recommended charts, and visualizations).
+    (2) Second Input Parameter: Based on the natural language suggestion, produce: Python code that dynamically generates the corresponding HTML report. The code should also save the HTML file to the local filesystem.
+    Outputs:  HTML report template file path"""
+
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "report_template_description": {
+                "description": "Natural language suggestion for HTML report structure (layout, sections, chart recommendations). Focus on framework only - no actual content needed.",
+                "type": "string",
+            },
+            "code": {
+                "type": "string",
+                "description": """Python code to generate an HTML template with standardized placeholders. Requirements:
+1. **Use placeholder format: [placeholder: description]**
+2. Generate template structure only - no real content or data
+3. Use the components and theme you have searched before.
+4. For text sections: Use placeholders for paragraphs/lists
+5. For charts: Use iframe elements with placeholder paths (/workspace/visualization/[filename].html)
+6. Maintain semantic HTML structure with appropriate classes
+
+## Output Requirements
+1. Generate **report_template.html** file
+2. Print the file path: print(report_path)
+
+Examples:
+<div class="section">
+    <h2>Executive Summary</h2>
+    <div class="card">
+        <p>[placeholder: 1-3 paragraph summary]</p>
+        <div class="highlight">
+            <strong>Key Findings:</strong>
+            <ul>
+                <li>[placeholder: Key insight 1]</li>
+                <li>[placeholder: Key insight 2]</li>
+            </ul>
+        </div>
+    </div>
+</div>
+
+<div class="section">
+    <h2>[placeholder: Section name]</h2>
+    <div class="card">
+        <div class="card-header">[placeholder: Chart title]</div>
+        <div class="card-body">
+            <div class="chart-container">
+                <iframe src="[placeholder: /workspace/visualization/chart_name.html]"
+                    width="100%" height="100%" frameborder="0"></iframe>
+            </div>
+            <div class="mt-3">
+                <h4>Key Insights:</h4>
+                <ul>
+                    <li>[placeholder: Chart insight 1]</li>
+                    <li>[placeholder: Chart insight 2]</li>
+                </ul>
+            </div>
+        </div>
+    </div>
+</div>"""
+            },
+        },
+        "required": ["report_template_description", "code"],
+    }
+    async def execute(self, code: str, report_template_description: str | None = None, timeout=5):
+        return await super().execute(code, timeout)

app/tool/chart_visualization/v2/search_html_library.py

@@ -0,0 +1,142 @@
+from app.tool.base import BaseTool, ToolResult
+from typing import ClassVar, Dict
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pathlib import Path
+from app.config import config
+from app.tool.file_operators import (
+    FileOperator,
+    LocalFileOperator,
+    PathLike,
+    SandboxFileOperator,
+)
+from typing import List
+
+class SearchHtmlLibraryResponse(ToolResult):
+    """Structured response from the SearchHtmlLibrary tool, inheriting ToolResult."""
+
+    report_bootstrap_theme: str = Field(description="The theme of the bootstrap report")
+    components_content: Dict[str, str] = Field(description="The UI components content as a dictionary (component_name: html_content)")
+
+    def __str__(self) -> str:
+        """Formatted string with indented HTML content"""
+        components_info = []
+        for name, content in self.components_content.items():
+            components_info.append(
+                f"【{name}】\n"
+                f"{content.strip()}\n"
+                f"{'-'*40}"
+            )
+
+        return (
+            f"📊 Report Theme: {self.report_bootstrap_theme}\n\n"
+            f"🛠️ Components Content:\n\n"
+            f"{'\n'.join(components_info)}"
+        )
+
+class SearchHtmlLibrary(BaseTool):
+    """A tool for searching the html library in users' local file system"""
+
+    name: str = "search_html_library"
+    description: str = """Check the type of user's input data, and select proper bootstrap theme and component from user's local file system.
+    When user requires a report, you need to use this tool first, to search the corresponding ui components and serve as a reference for subsequent report generation.
+    Then, use other tools to generate fancy report template, specific chart...
+    Outputs: 1) HTML components, 2) Bootstrap theme """
+
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "report_bootstrap_theme":{
+                "description": "The theme of bootstrap template to use.",
+                "enum": [
+                    # Light themes
+                    "Brite"
+                    "Cerulean",
+                    "Materia",
+                    "Cosmo",
+                    "Flatly",
+                    "Journal",
+                    "Litera",
+                    "Lumen",
+                    "Minty",
+                    "Pulse",
+                    "Sandstone",
+                    "Simplex",
+                    "Sketchy",
+                    "Spacelab",
+                    "United",
+                    "Zephyr",
+
+                    # Dark themes
+                    "Cyborg",
+                    "Darkly",
+                    "Slate",
+                    "Solar",
+                    "Superhero",
+                    "Vapor",
+                    "Lux",
+
+                    # Special styles
+                    "Quartz",
+                    "Morph",
+                    "Yeti"
+                ],
+                "default": "Materia",
+                "type": "string",
+            },
+            "components": {
+                "description": "List of components to you will use in the report, you need to decide based on user's input data.",
+                "type": "array",
+                "items": {
+                    "type": "string",
+                    "enum": ["blockquote", "card", "chart", "indicator", "list", "nav", "nvabar", "progress", "table", "typography"]
+                },
+                "default": ["card", "chart", "table"],
+                "minItems": 2,
+                "uniqueItems": True
+            }
+        },
+        "required": ["report_bootstrap_theme", "components"],
+    }
+
+    _local_operator: LocalFileOperator = LocalFileOperator()
+    _sandbox_operator: SandboxFileOperator = SandboxFileOperator()
+
+    # def _get_operator(self, use_sandbox: bool) -> FileOperator:
+    def _get_operator(self) -> FileOperator:
+        """Get the appropriate file operator based on execution mode."""
+        return (
+            self._sandbox_operator
+            if config.sandbox.use_sandbox
+            else self._local_operator
+        )
+
+    async def execute(
+        self,
+        report_bootstrap_theme: str,
+        components: List[str]
+    ) -> SearchHtmlLibraryResponse:
+        """
+        Execute the tool with the given parameters.
+        Reads HTML component files and returns their content in a dictionary.
+        """
+        operator = self._get_operator()
+        components_content = {}  # Initialize an empty dictionary to store component contents
+
+        for component in components:
+            path = f"/home/vm3/JoyZhao/OSPP/OpenManus/workspace/html_library/{component}.html"
+            print(f"Reading component: {path}")
+
+            try:
+                # Read the HTML file content
+                component_content = await operator.read_file(path)
+                # Store in dictionary with component name as key
+                components_content[component] = component_content
+            except Exception as e:
+                print(f"Failed to read component {component}: {str(e)}")
+                components_content[component] = f"Error loading {component} component"
+
+        theme=f"https://cdn.jsdelivr.net/npm/bootswatch@5/dist/{report_bootstrap_theme.lower()}/bootstrap.min.css"
+        return SearchHtmlLibraryResponse(
+            report_bootstrap_theme=theme,
+            components_content=components_content
+        )

config/config.example-model-jiekouai.toml

@@ -1,17 +0,0 @@
-# Global LLM configuration
-[llm] #Jiekou.AI:
-api_type = 'jiekou'
-model = "claude-sonnet-4-5-20250929"                               # The LLM model to use
-base_url = "https://api.jiekou.ai/openai"                          # API endpoint URL
-api_key = "your Jiekou.AI api key"                                 # Your API key
-max_tokens = 64000                                                 # Maximum number of tokens in the response
-temperature = 0.0                                                  # Controls randomness
-
-
-[llm.vision] #Jiekou.AI VISION:
-api_type = 'jiekou'
-model = "claude-sonnet-4-5-20250929"                             # The vision model to use
-base_url = "https://api.jiekou.ai/openai"                        # API endpoint URL for vision model
-api_key = "your Jiekou.AI api key"                               # Your API key for vision model
-max_tokens = 64000                                               # Maximum number of tokens in the response
-temperature = 0.0                                                # Controls randomness for vision model

config/config.example.toml

@@ -31,14 +31,6 @@ temperature = 0.0                          # Controls randomness
 # max_tokens = 4096
 # temperature = 0.0
 
-# [llm] #Jiekou.AI:
-# api_type = 'jiekou'
-# model = "claude-sonnet-4-5-20250929"                               # The LLM model to use
-# base_url = "https://api.jiekou.ai/openai"                          # API endpoint URL
-# api_key = "your Jiekou.AI api key"                                 # Your API key
-# max_tokens = 64000                                                 # Maximum number of tokens in the response
-# temperature = 0.0                                                  # Controls randomness
-
 # Optional configuration for specific LLM models
 [llm.vision]
 model = "claude-3-7-sonnet-20250219"       # The vision model to use

package-lock.json

@@ -0,0 +1,6 @@
+{
+  "name": "OpenManus",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {}
+}

workspace/example.txt

@@ -1 +0,0 @@
-This is a sample file. Files generated by OpenManus are stored in the current folder by default.