Update README.md

README.md

@@ -1,43 +1,55 @@
 ## ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers
 
+![image](https://github.com/ml4bio/ifDEEPre/assets/16831407/cd6aedec-db24-49f2-a93b-29a7e6fd4cfb)
+<p align="center">
+    <em>Figure 1: Architecture of ifDEEPre for accurate enzyme predictions.</em>
+</p>
 This is the tensorflow implementation of ifDEEPre. 
 
-
 ## 1. System Requirements
 
-The `ifDEEPre` package is built under the Linux system with the popular softwares [Anaconda](https://www.anaconda.com/) and [Tensorflow](https://www.tensorflow.org/). The versions of the software dependencies that both packages use are provided in the `environment.yml`.
-
-The versions of the software dependencies and data-analysis packages that ifDEEPre has been tested on are given in the `environment.yml`. Users can conveniently create the same environment by running the command:
-```
-conda env create -f environment.yml
-```
-
-The ifDEEPre package does not require any non-standard hardware.
+The `ifDEEPre` package is built under the Linux system with the popular software:
+- [Anaconda](https://www.anaconda.com/) 
+- [Tensorflow](https://www.tensorflow.org/) >= 1.9
+- Pytorch>= 1.10.2
+- numpy>=1.19.2
+
+The versions of the software dependencies that all packages use are provided in the `environment.yml`.
 
 
 ## 2. Installation Guide
 
-### Install the package
-The environment that we use is given in `environment.yml`. You can create the same environment by running the command:
+### Build the the conda environment an install packages
+The versions of the software dependencies and data-analysis packages that ifDEEPre has been tested on are given in the `environment.yml`. The ifDEEPre package does not require any non-standard hardware. You can create the same environment by running the command:
+
 ```
+git clone https://github.com/ml4bio/ifDEEPre.git
+cd ifDEEPre
 conda env create -f environment.yml
 ```
-
+After successful installation without errors, you can activate the conda environment:
+```
+conda activate tf36_ifdeepre
+```
+### Download pre-trained model
+Download the database and trained models from [Google Drive](https://drive.google.com/drive/folders/1qEMzaDas9M0PaZHrHWNcMby82Ef_Qun1). Save the corresponding files in the ```Database``` folder.
 
 ## 3. Demo and Instructions for Using ifDEEPre
 
 You can use the trained ifDEEPre models to predict enzyme commission numbers by navigating to the `./src_v6_Final_server` folder and running the command:
 ```
+cd ./src_v6_Final_server
 python code_4_ifdeepre_inputExample.py
 ```
 
 
 ## 4. Online Version - ifDEEPre server
 
-The online server of this package is available from this link, [ifDEEPre](https://proj.cse.cuhk.edu.hk/aihlab/ifdeepre/#/), which is freely available without any registration requirement.
+You are strongly encouraged to use the online server if you do not want to get your hands dirty. The online server is available on our lab's website: [ifDEEPre](https://proj.cse.cuhk.edu.hk/aihlab/ifdeepre/#/), which is free to use without any registration requirement.
 
-Users can directly upload their protein sequences and get accurate enzyme commission number prediction results conveniently after a short time of waiting. Furthermore, the prediction results can be directly downloaded for convenient future usage.
 
+Users can directly upload their protein sequences and get accurate enzyme commission number prediction results conveniently after a short time of waiting. Furthermore, the prediction results can be directly downloaded for convenient future usage.
+<img width="667" alt="edfb05503195f57c0dd5a8dc94507b0" src="https://github.com/ml4bio/ifDEEPre/assets/16831407/ad2d43b4-0500-4486-a8dc-a4f2fcfa04fa">