prepare demo and documents

Author: RCFans

README.md

@@ -46,8 +46,34 @@ A complete reasoning session typically answers the following questions:
 
 ---
 
+## 🫧 Online Demo
+
+If you are a:
+- Strategy Consultant
+- C-level Executive
+- Industry Analyst
+- Product Manager
+- Other non-technical users
+
+Want to quickly experience Omen without local installation? Please visit our online demo deployed on Streamlit Cloud to quickly see the effect.
+
+You can explore the strategic reasoning flow directly online:
+
+[👉 Explore Omen on Streamlit Cloud](https://omen-demo.streamlit.app/)
+
+---
+
 ## 🚀 Quick Start
 
+If you are:
+
+- Data Scientist
+- AI Researcher
+- Strategy Analyst
+- Other technical users
+
+Want to run Omen in your local environment? Please follow the installation and running guide below.
+
 ### 🏗️ Installation
 
 Environment requirements: Python 3.12+ with `pip` package manager.
@@ -59,19 +85,21 @@ pip install --upgrade pip setuptools wheel
 pip install -e .
 ```
 
-### 🌰 View Example
+### 🌰 Run Demo
 
-If you want to quickly see Omen in action, a visualized sample case and its results are available in the `sample` directory. Run:
+If you want to quickly see Omen in action, a visualized sample case and its results are available in the `demo` directory. Run:
 
 ```bash
-streamlit run sample/app/scenario_planning.py
+streamlit run demo/app/scenario_planning.py
 ```
 
 Then open `http://localhost:8501` in your browser to explore the full strategic reasoning flow.
 
 ### 🎵 Run Built-in Case
 
-If you want to run a complete end-to-end workflow, we have prepared a built-in case simulating SAP's acquisition of Reltio in March 2026. The case document is `cases/sap_reltio_acquisition.md`, and it can be run end-to-end with the following commands:
+If you want to run a complete **Analyze - Simulate - Explain** workflow, we have prepared a built-in case simulating SAP's acquisition of Reltio in March 2026. 
+
+The case document is `cases/situations/sap_reltio_acquisition.md`, and it can be run end-to-end with the following commands:
 
 #### Step 1. Analyze
 
@@ -153,17 +181,17 @@ Omen is built for the following roles:
 
 ### Strategic Actor Analyze
 
-*  [👤 Elon Musk](cases/actors/elon-musk.md)
-*  [👤 Jeff Bezos](cases/actors/jeff-bezos.md)
-*  [👤 Steve Jobs](cases/actors/steve-jobs.md)
-*  [👤 Jack Ma](cases/actors/jack-ma.md)
-*  [👤 Chen Jiaxing (me)](cases/actors/chen-jiaxing.md)
+*  👤 [Elon Musk](cases/actors/elon-musk.md)
+*  👤 [Jeff Bezos](cases/actors/jeff-bezos.md)
+*  👤 [Steve Jobs](cases/actors/steve-jobs.md)
+*  👤 [Jack Ma](cases/actors/jack-ma.md)
+*  👤 [Chen Jiaxing (me)](cases/actors/chen-jiaxing.md)
 
 ### Strategic Reasoning Cases
 
-*  [🧩 Acquisition: SAP vs Reltio](cases/situations/sap_reltio_acquisition.md)
-*  [🗺️ Ontology Games: Database vs AI Memory](cases/ontology.md)
-*  [⚔️ Vector Database vs AI Memory](cases/vector-memory.md)   
+*  🧩 [Acquisition: SAP vs Reltio](cases/situations/sap_reltio_acquisition.md)
+*  🗺️ [Ontology Games: Database vs AI Memory](cases/ontology.md)
+*  ⚔️ [Vector Database vs AI Memory](cases/vector-memory.md)   
 
 More scenarios are under development (contributions welcome):
 *   `Agent Infrastructure` vs `Workflow Platforms`

README.zh.md

@@ -47,7 +47,34 @@ Omen **不预测**单一未来，它是**为复杂性而生**的推演引擎。
 
 ---
 
-## 🚀 快速开始
+## 🫧 在线演示
+
+如果你是：
+
+- 战略顾问
+- C级决策者
+- 行业分析师
+- 产品经理
+- 其它非技术用户
+
+希望快速体验 Omen 而勿须本地安装，请访问我们部署在 Streamlit Cloud 的在线演示应用，快速查看效果。
+
+你可以直接在线探索战略推演流程：
+
+[👉 在线体验 Omen](https://omen-demo.streamlit.app)
+
+---
+
+## 🚀 快速上手
+
+如果你是：
+
+- 数据科学家
+- AI 研究者
+- 技术战略分析师
+- 其它技术用户
+
+希望在本地环境中运行 Omen，请继续阅读以下安装和运行指南。
 
 ### 🏗️ 安装
 
@@ -60,19 +87,21 @@ pip install --upgrade pip setuptools wheel
 pip install -e .
 ```
 
-### 🌰 看个例子
+### 🌰 看例子
 
-如果你希望快速查看 Omen 的运行效果，`sample` 目录中提供了可视化案例及其结果。运行：
+如果你希望快速查看 Omen 的运行效果，`demo` 目录中提供了可视化案例及其结果。运行：
 
 ```bash
-streamlit run sample/app/scenario_planning.py
+streamlit run demo/app/scenario_planning.py
 ```
 
-然后在浏览器中打开 `http://localhost:8501`，即可查看完整的战略推演流程。
+在浏览器中打开 `http://localhost:8501`，即可查看完整的战略推演流程。
 
 ### 🎵 亲自运行
 
-如果你希望亲自运行一遍完整的流程，请使用我们准备的内置案例，用于模拟 2026 年 3 月 SAP 收购 Reltio 的情境。案例文档位于 `cases/sap_reltio_acquisition.md`，可以通过以下步骤端到端运行。
+如果你希望亲自运行一遍完整的**分析——模拟——解释**流程，请使用我们准备的内置案例，用于模拟 2026 年 3 月 SAP 收购 Reltio 的情境。
+
+案例文档位于 `cases/situations/sap_reltio_acquisition.md`，可以通过以下步骤端到端运行。
 
 #### 第一步：分析
 
@@ -152,17 +181,17 @@ Omen 专为以下角色打造：
 
 ### 战略主体分析
 
-*  [👤 Elon Musk](cases/actors/elon-musk.md)
-*  [👤 Jeff Bezos](cases/actors/jeff-bezos.md)
-*  [👤 Steve Jobs](cases/actors/steve-jobs.md)
-*  [👤 Jack Ma](cases/actors/jack-ma.md)
-*  [👤 Chen Jiaxing (me)](cases/actors/chen-jiaxing.md)
+*  👤 [Elon Musk](cases/actors/elon-musk.md)
+*  👤 [Jeff Bezos](cases/actors/jeff-bezos.md)
+*  👤 [Steve Jobs](cases/actors/steve-jobs.md)
+*  👤 [Jack Ma](cases/actors/jack-ma.md)
+*  👤 [Chen Jiaxing (me)](cases/actors/chen-jiaxing.md)
 
 ### 战略推演案例
 
-*   [🧩 SAP 收购 Reltio：主数据迷雾](cases/situations/sap_reltio_acquisition.md)
-*   [🗺️ 本体论博弈：数据库 vs AI 记忆](cases/ontology.md)
-*   [⚔️ 向量数据库 vs AI 记忆](cases/vector-memory.md)
+*   🧩 [SAP 收购 Reltio：主数据迷雾](cases/situations/sap_reltio_acquisition.md)
+*   🗺️ [本体论博弈：数据库 vs AI 记忆](cases/ontology.md)
+*   ⚔️ [向量数据库 vs AI 记忆](cases/vector-memory.md)
 
 更多场景持续构建中（欢迎贡献）：
 *   `智能体基础设施` vs `工作流平台`

sample/app/scenario_planning.py demo/app/scenario_planning.pysample/app/scenario_planning.py renamed to demo/app/scenario_planning.py

@@ -63,8 +63,8 @@ def _read_json_file(*, base_dir: Path, pack_id: str, filename: str) -> dict[str,
 
 def _load_sample_actor_payloads(pack_id: str) -> dict[str, Any]:
     safe_pack_id = _normalize_pack_id(pack_id)
-    actor_dir = Path("sample/data/actors") / safe_pack_id
-    actor_base = Path("sample/data/actors")
+    actor_dir = Path("demo/data/actors") / safe_pack_id
+    actor_base = Path("demo/data/actors")
     actor_profile_path = actor_dir / "actor_ontology.json"
     persona_path = actor_dir / "analyze_persona.json"
     status_path = actor_dir / "analyze_status.json"
@@ -585,9 +585,9 @@ def _render_flow_fallback(payloads: dict[str, Any]) -> None:
 
 with st.sidebar:
     st.header("Sample Data Source")
-    st.warning("Demo mode: this app reads bundled sample artifacts only.")
-    DATA_ROOT = "sample/data/scenarios"
-    OUTPUT_ROOT = "sample/output"
+    st.warning("Demo mode: this app reads bundled demo artifacts only.")
+    DATA_ROOT = "demo/data/scenarios"
+    OUTPUT_ROOT = "demo/output"
     st.caption(f"Data root: {DATA_ROOT}")
     st.caption(f"Output root: {OUTPUT_ROOT}")