update readme

README.md

@@ -100,19 +100,11 @@ Refer [here](XAgentServer/README.md) for the detailed information about our GUI
 <div><a id="Demo"></a></div>
 
 # 🎬 Demo
-You can check our live demo on [XAgent Official Website](https://www.x-agent.net/).
-
-We also provide a video demo of using XAgent here.
-
-<div align="center">
-
-<video width="320" height="240" controls>
+You can check our live demo on [XAgent Official Website](https://www.x-agent.net/). We also provide a video demo and show cases of using XAgent here:
+<video width="640" height="480" controls>
   <source src="assets/readme/demo.mp4" type="video/mp4">
 </video>
 
-</div>
-
-Here we also show some cases of solving tasks by XAgent:
 ## Case 1. Data Analysis: Demonstrating the Effectiveness of Dual-Loop Mechanism
 We start with a case of aiding users in intricate data analysis. Here, our user submitted an `iris.zip` file to XAgent, seeking assistance in data analysis. XAgent swiftly broke down the task into four sub-tasks: (1) data inspection and comprehension, (2) verification of the system's Python environment for relevant data analysis libraries, (3) crafting data analysis code for data processing and analysis, and (4) compiling an analytical report based on the Python code's execution results.
 Here is a figure drawn by XAgent.
@@ -121,10 +113,12 @@ Here is a figure drawn by XAgent.
 
 ## Case 2. Recommendation: A New Paradigm of Human-Agent Interaction
 Empowered with the unique capability to actively seek human assistance and collaborate in problem-solving, XAgent continues to redefine the boundaries of human-agent cooperation. As depicted in screenshot below, a user sought XAgent's aid in recommending some great restaurants for a friendly gathering, yet failed to provide specific details. Recognizing the insufficiency of the provided information, XAgent employed the AskForHumanHelp tool, prompting human intervention to elicit the user's preferred location, budget constraints, culinary preferences, and any dietary restrictions. Armed with this valuable feedback, XAgent seamlessly generated tailored restaurant recommendations, ensuring a personalized and satisfying experience for the user and their friends.
+
 ![Illustration of Ask for Human Help of XAgent](assets/readme/ask_for_human_help.png)
 
 ## Case 3. Training Model: A Sophisticated Tool User
 XAgent not only tackles mundane tasks but also serves as an invaluable aid in complex tasks such as model training. Here we show a scenario where a user desires to analyze movie reviews and evaluate the public sentiment surrounding particular films. In response, XAgent promptly initiates the process by downloading the IMDB dataset to train a cutting-edge BERT model (see screenshot below), harnessing the power of deep learning. Armed with this trained BERT model, XAgent seamlessly navigates the intricate nuances of movie reviews, offering insightful predictions regarding the public's perception of various films.
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 ![bert_1](assets/readme/bert_1.png)
 ![bert_2](assets/readme/bert_2.png)
 ![bert_3](assets/readme/bert_3.png)