Update README.md

README.md

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-## Introduction
-<p align="center">
-    <br>
-    <img src="assets/FAPM.png"/>
-    <br>
-<p>  
-    
-Huggingface repo: *https://huggingface.co/wenkai/FAPM/*  
-
-## Installation
-
-1. (Optional) Creating conda environment
-
-```bash
-conda create -n lavis python=3.8
-conda activate lavis
-```
- 
-2. for development, you may build from source
-
-```bash
-git clone https://github.com/xiangwenkai/FAPM.git
-cd FAPM
-pip install -e .
-
-# if needed
-# pip install Biopython
-# pip install fair-esm
-```
-
-### Datasets
-#### 1.raw dataset
-Raw data are avaliable at *https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2023_04/knowledgebase/*, this file is very large and need to be processed to get its name, sequence, GO label, function description and prompt.  
-The domain level protein dataset we used are avaliable at *https://ftp.ebi.ac.uk/pub/databases/interpro/releases/95.0/protein2ipr.dat.gz*  
-In this respository, We provide the experimental train/val/test sets of Swiss-Prot, which are avaliable at data/swissprot_exp  
-#### 2.ESM2 embeddings  
-Source code for ESM2 embeddings generation: *https://github.com/facebookresearch/esm*  
-The generation command:  
-```bash
-conda activate FAPM
-python esm_scripts/extract.py esm2_t36_3B_UR50D you_path/protein.fasta you_path_to_save_embedding_files --repr_layers 36 --truncation_seq_length 1024 --include per_tok
-```
-Example:
-```
-conda activate FAPM
-python esm_scripts/extract.py esm2_t36_3B_UR50D data/fasta/example.fasta data/emb_esm2_3b --repr_layers 36 --truncation_seq_length 1024 --include per_tok
-```  
-The default path to save embedding files is **data/emb_esm2_3b**
-You can refer to *data/fasta/prepare_custom_fasta.py* to prepare your custom fasta data.  
-
-
-## Pretraining language models  
-Source: *https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B*
-
-## Training
-data config: lavis/configs/datasets/protein/GO_defaults_cap.yaml  
-stage1 config: lavis/projects/blip2/train/protein_pretrain_stage1.yaml  
-stage1 training command: run_scripts/blip2/train/protein_pretrain_domain_stage1.sh  
-stage2 config: lavis/projects/blip2/train/protein_pretrain_stage2.yaml  
-stage2 training/finetuning command: run_scripts/blip2/train/protein_pretrain_domain_stage2.sh  
-
-## Trained models
-The models are avaliable at **https://huggingface.co/wenkai/FAPM/tree/main/model**  
-You can also download our trained models from google drive: *https://drive.google.com/drive/folders/1aA0eSYxNw3DvrU5GU1Cu-4q2kIxxAGSE?usp=drive_link*  
-
-## Testing
-config: lavis/projects/blip2/eval/caption_protein_eval.yaml  
-command: run_scripts/blip2/eval/eval_cap_protein.sh  
-
-## Inference example
-```
-python FAPM_inference.py \
---model_path model/checkpoint_mf2.pth \
---example_path data/emb_esm2_3b/P18281.pt \
---device cuda \
---prompt Acanthamoeba \
---prop True
-```  
-
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+title: FAPM demo
+emoji: 📊
+colorFrom: green
+colorTo: purple
+sdk: gradio
+sdk_version: 4.12.0
+app_file: app.py
+pinned: false