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()