Add profile app.py

app.py

@@ -0,0 +1,104 @@
+from transformers import AlbertForMaskedLM, AlbertTokenizer, pipeline
+import numpy as np
+import gradio as gr
+
+
+## Load the model
+tokenizer = AlbertTokenizer.from_pretrained("Rostlab/prot_albert", do_lower_case=False )
+model = AlbertForMaskedLM.from_pretrained("Rostlab/prot_albert")
+
+#pipeline
+fill_mask = pipeline("fill-mask", model = model, tokenizer=tokenizer, top_k = 21)
+
+## Initialization
+header=['SeqNo','PDB','No','V','L','I','M','F','W','Y','G','A','P','S','T','C','H','R','K','Q','E','N','D','NOCC','NDEL','NINS','ENTROPY','RELENT','WEIGHT','CHAIN','AUTHCHAIN']
+rem=[]
+
+Hash1={
+    
+    'V':0,
+    'L':1, 
+    'I':2,
+    'M':3,
+    'F':4,
+    'W':5,
+    'Y':6,
+    'G':7,
+    'A':8,
+    'P':9,
+    'S':10,
+    'T':11,
+    'C':12,
+    'H':13,
+    'R':14,
+    'K':15,
+    'Q':16,
+    'E':17,
+    'N':18,
+    'D':19,
+    'X':20
+}
+
+
+
+def ReadfastaFile(filename):
+    seq=[]
+    name=[]
+    human=""
+    fn=open(filename,"r")
+    S=""
+    for h in fn:
+        h=h.rstrip()
+        if not ">" in h:
+            S=S+h
+    fn.close() 
+    S=S.upper()
+    return(S)
+
+
+def Predict_profile(sequence, header = header,rem=rem,Hash1 = Hash1):
+    f=list()
+    f.append("PDBNO"+"\t")
+    for i in range(3,23):
+        f.append(header[i]+"\t")
+    f.append("X\n")    
+   
+    a = (len(Hash1),len(sequence))
+    pred_Profile=np.zeros(a)
+    for i  in range(len(S)):  
+        if i not in rem:
+            T=np.copy(list(sequence))
+            T=" ".join(T)
+            T=T.split(" ")
+            T[i]='[MASK]'
+            T=" ".join(T)
+            l=fill_mask(T)
+            number=len(l)
+            for k in range(number):
+                token=l[k]['token_str']
+                token=token.replace("▁","")
+                score=l[k]['score']
+                if token not in Hash1:
+                    print(i,token)
+                
+                else:   
+                    pred_Profile[Hash1[token]][i]=int(score*100)
+            f.append(str(i+1))
+            for k in range(len(Hash1)):  #without X
+                f.append("\t"+str(pred_Profile[k][i]))
+            f.append("\n")
+            print(i)
+    if len(rem)!=0:
+        pred_Profile=np.delete(pred_Profile,rem,1)
+    return(pred_Profile)
+
+
+
+
+
+iface = gr.Interface(fn=Predict_profile,
+                     inputs=["text"], 
+                     outputs="text",
+                     description="Please enter the sequence",
+                     title="Protein profile prediction using ProtAbert")
+iface.launch()

requirements.txt

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+numpy
+transformers