Create app.py

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+title = "Protien Token Classification 🧬."
+description = "Finds the position of Helix and Beta strand in the Protein Sequence."
+article = 'Created from finetuning ESM2_150M'
+
+model = AutoModelForTokenClassification.from_pretrained('./Model')
+tokenizer = AutoTokenizer.from_pretrained('facebook/esm2_t30_150M_UR50D')
+
+example_list = ['MENFTALFGAQADPPPPPTALGFGPGKPPPPPPPPAGGGPGTAPPPTAATAPPGADKSGAGCGPFYLMRELPGSTELTGSTNLITHYNLEQAYNKFCGKKVKEKLSNFLPDLPGMIDLPGSHDNSSLRSLIEKPPILSSSFNPITGTMLAGFRLHTGPLPEQCRLMHIQPPKKKNKHKHKQSRTQDPVPPETPSDSDHKKKKKKKEEDPDRKRKKKEKKKKKNRHSPDHPGMGSSQASSSSSLR',
+ 'MAFSDLTSRTVHLYDNWIKDADPRVEDWLLMSSPLPQTILLGFYVYFVTSLGPKLMENRKPFELKKAMITYNFFIVLFSVYMCYEFVMSGWGIGYSFRCDIVDYSRSPTALRMARTCWLYYFSKFIELLDTIFFVLRKKNSQVTFLHVFHHTIMPWTWWFGVKFAAGGLGTFHALLNTAVHVVMYSYYGLSALGPAYQKYLWWKKYLTSLQLVQFVIVAIHISQFFFMEDCKYQFPVFACIIMSYSFMFLLLFLHFWYRAYTKGQRLPKTVKNGTCKNKDN',
+ 'MYPSNKKKKVWREEKERLLKMTLEERRKEYLRDYIPLNSILSWKEEMKGKGQNDEENTQETSQVKKSLTEKVSLYRGDITLLEVDAIVNAANASLLGGGGVDGCIHRAAGPCLLAECRNLNGCDTGHAKITCGYDLPAKYVIHTVGPIARGHINGSHKEDLANCYKSSLKLVKENNIRSVAFPCISTGIYGFPNEPAAVIALNTIKEWLAKNHHEVDRIIFCVFLEVDFKIYKKKMNEFFSVDDNNEEEEDVEMKEDSDENGPEEKQSVEEMEEQSQDADGVNTVTVPGPASEEAVEDCKDEDFAKDENITKGGEVTDHSVRDQDHPDGQENDSTKNEIKIETESQSSYMETEELSSNQEDAVIVEQPEVIPLTEDQEEKEGEKAPGEDTPRMPGKSEGSSDLENTPGPDAGAQDEAKEQRNGTK',
+ 'MAGQHLPVPRLEGVSREQFMQHLYPQRKPLVLEGIDLGPCTSKWTVDYLSQVGGKKEVKIHVAAVAQMDFISKNFVYRTLPFDQLVQRAAEEKHKEFFVSEDEKYYLRSLGEDPRKDVADIRKQFPLLKGDIKFPEFFKEEQFFSSVFRISSPGLQLWTHYDVMDNLLIQVTGKKRVVLFSPRDAQYLYLKGTKSEVLNIDNPDLAKYPLFSKARRYECSLEAGDVLFIPALWFHNVISEEFGVGVNIFWKHLPSECYDKTDTYGNKDPTAASRAAQILDRALKTLAELPEEYRDFYARRMVLHIQDKAYSKNSE',
+ 'MEAGPPGSARPAEPGPCLSGQRGADHTASASLQSVAGTEPGRHPQAVAAVLPAGGCGERMGVPTPKQFCPILERPLISYTLQALERVCWIKDIVVAVTGENMEVMKSIIQKYQHKRISLVEAGVTRHRSIFNGLKALAEDQINSKLSKPEVVIIHDAVRPFVEEGVLLKVVTAAKEHGAAGAIRPLVSTVVSPSADGCLDYSLERARHRASEMPQAFLFDVIYEAYQQCSDYDLEFGTECLQLALKYCCTKAKLVEGSPDLWKVTYKRDLYAAESIIKERISQEICVVMDTEEDNKHVGHLLEEVLKSELNHVKVTSEALGHAGRHLQQIILDQCYNFVCVNVTTSDFQETQKLLSMLEESSLCILYPVVVVSVHFLDFKLVPPSQKMENLMQIREFAKEVKERNILLYGLLISYPQDDQKLQESLRQGAIIIASLIKERNSGLIGQLLIA']
+
+def count_helix(helix):
+    final = []
+    temp = []
+    for x in range(1, len(helix)):
+        if helix[x] == helix[x-1] + 1:
+            temp.append(helix[x-1])
+            temp.append(helix[x])
+        elif len(temp) != 0: 
+            final.append((temp[0], temp[-1]))
+            temp = []
+    return final
+
+def count_strand(strand):
+    final = []
+    temp = []
+    for x in range(1, len(strand)):
+        if strand[x] == strand[x-1] + 1:
+            temp.append(strand[x-1])
+            temp.append(strand[x])
+        elif len(temp) != 0: 
+            final.append((temp[0], temp[-1]))
+            temp = []
+    return final
+
+def print_output1(helix):
+    helix_op = count_helix(helix)
+
+    if len(helix_op) != 0:
+        str1 = str(helix_op)[1:-1]
+        return str1
+    
+    else:
+        return str('No Helix found.')
+    
+def print_output2(strand):
+    strand_op = count_strand(strand)
+
+    if len(strand_op) != 0:
+        str1 = str(strand_op)[1:-1]
+        return str1 
+
+    else:
+        return str('No Beta strand found.')
+
+def predict(ProtienSequence):
+    input = tokenizer(ProtienSequence, return_tensors='pt')
+    with torch.inference_mode():
+        outputs = model(**input)
+    output = outputs.logits.argmax(axis=2)[0].numpy()
+
+    helix = []
+    strand = []
+
+    for i in range(len(output)):
+        if output[i] != 0:
+            if output[i] == 1:
+                helix.append(i+1)
+            else:
+                strand.append(i+1)
+    
+    return print_output1(helix), print_output2(strand)
+
+iface = gr.Interface(fn=predict,
+                    inputs='text',
+                    outputs=[gr.Text(label='Helix'),
+                             gr.Text(label='Beta Strand')],
+                             title=title,
+                             description=description,
+                             article=article,
+                             examples=example_list)
+iface.launch()