Update README.md

README.md

@@ -16,9 +16,10 @@ tags:
 [![Code License](https://img.shields.io/badge/Code%20License-MIT-green.svg)](https://github.com/zjunlp/ChatCell/blob/main/LICENSE)
 [![Data License](https://img.shields.io/badge/Data%20License-CC%20BY%204.0-red.svg)](https://github.com/zjunlp/ChatCell/blob/main/DATA_LICENSE)
 
-![image.png](./figure/logo.png)
 
-<h2 align="center">  <img src="figure/logo.png" width="8%" height="18%"> ChatCell: Facilitating Single-Cell Analysis with Natural Language </h2>
+![image.png](./figures/logo.png)
+
+<h2 align="center"> ChatCell: Facilitating Single-Cell Analysis with Natural Language </h2>
 
 <p align="center">
   <a href="https://www.zjukg.org/project/ChatCell">💻 Project Page</a> •
@@ -31,8 +32,7 @@ tags:
 </p>
 
 
-
-<div align=center><img src="figure/intro.gif" width="60%" height="100%" /></div>
+<img src="./figures/intro.jpg" alt="image" width=45%>
 <b>ChatCell</b> allows researchers to input instructions in either natural or single-cell language, thereby facilitating the execution of necessary tasks in single-cell analysis. Black and red texts denote human and single-cell language, respectively.
 
 </div>
@@ -60,7 +60,7 @@ tags:
 - Traditional single-cell foundation models leverage extensive scRNA-seq datasets, applying NLP techniques to analyze gene expression matrices—structured formats that simplify scRNA-seq data into computationally tractable representations—during pre-training. They are subsequently fine-tuned for distinct single-cell analysis tasks, as shown in Figure (a).
 
 <p align="center">
-<img src="figure/overview.jpg"  width="100%" height="60%">
+<img src="./figures/overview.jpg" alt="image" width=100%>
 </p>
 <div align="center">
 Figure 1:  (a) Comparison of traditional single-cell engineering and <b>ChatCell</b>. (b) Overview of <b>ChatCell</b>.
@@ -82,7 +82,7 @@ We concentrate on the following single-cell tasks:
 Random cell sentence generation challenges the model to create cell sentences devoid of predefined biological conditions or constraints. This task aims to evaluate the model's ability to generate valid and contextually appropriate cell sentences, potentially simulating natural variations in cellular behavior. 
 
 <p align="center">
-<img src="figure/example1.jpg"  width="80%" height="60%">
+<img src="./figures/example1.jpg" alt="image" width=100%>
 </p>