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Glydentify.py

@@ -0,0 +1,501 @@
+import pandas as pd
+from IPython.display import clear_output
+import torch
+from transformers import EsmForSequenceClassification, AdamW, AutoTokenizer
+from torch.utils.data import DataLoader, TensorDataset, random_split
+from sklearn.preprocessing import LabelEncoder
+from tqdm import tqdm
+import numpy as np
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+import pickle
+import torch.nn.functional as F
+import gradio as gr
+import io
+from PIL import Image
+import Bio
+from Bio import SeqIO
+import zipfile
+import os
+
+# Load the model from the file
+with open('family_labels.pkl', 'rb') as filefam:
+    yfam = pickle.load(filefam)
+
+tokenizerfam = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") #facebook/esm2_t33_650M_UR50D
+label_encoderfam = LabelEncoder()
+encoded_labelsfam = label_encoderfam.fit_transform(yfam)
+labelsfam = torch.tensor(encoded_labelsfam)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+device
+
+modelfam = EsmForSequenceClassification.from_pretrained("facebook/esm2_t33_650M_UR50D", num_labels=len(set(labelsfam.tolist())))
+modelfam = modelfam.to('cpu')
+
+modelfam.load_state_dict(torch.load("/home/aarya/Documents/paper3/model_650M.pth"))
+modelfam.eval()
+
+x_testfam = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH
+LQRVSLPRMVYPQPKVLTPCRKDVLVVTPWLAPIVWEGTFNIDILNEQFRLQNTTIGLTV
+FAIKKYVAFLKLFLETAEKHFMVGHRVHYYVFTDQPAAVPRVTLGTGRQLSVLEVRAYKR
+WQDVSMRRMEMISDFCERRFLSEVDYLVCVDVDMEFRDHVGVEILTPLFGTLHPGFYGSS
+REAFTYERRPQSQAYIPKDEGDFYYLGGFFGGSVQEVQRLTRACHQAMMVDQANGIEAVW
+HDESHLNKYLLRHKPTKVLSPEYLWDQQLLGWPAVLRKLRFTAVPKNHQAVRNP
+"""]
+
+encoded_inputfam = tokenizerfam(x_testfam, padding=True, truncation=True, max_length=512, return_tensors="pt")
+input_idsfam = encoded_inputfam["input_ids"]
+attention_maskfam = encoded_inputfam["attention_mask"]
+
+with torch.no_grad():
+    outputfam = modelfam(input_idsfam, attention_mask=attention_maskfam)
+    logitsfam = outputfam.logits
+    probabilitiesfam = F.softmax(logitsfam, dim=1)
+    _, predicted_labelsfam = torch.max(logitsfam, dim=1)
+probabilitiesfam[0]
+
+decoded_labelsfam = label_encoderfam.inverse_transform(predicted_labelsfam.tolist())
+decoded_labelsfam
+
+
+
+#Load donor model from file
+tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D")
+
+with open('donorslabels.pkl', 'rb') as file:
+    label_encoder = pickle.load(file)
+
+# encoded_labels = label_encoder.fit(y)
+# labels = torch.tensor(encoded_labels)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+device
+
+model = EsmForSequenceClassification.from_pretrained("facebook/esm2_t12_35M_UR50D", num_labels=len(label_encoder.classes_))
+model = model.to('cpu')
+
+model.load_state_dict(torch.load("best_model_35M_t12_5v5.pth")) #model_best_35v2M.pth
+model.eval()
+
+x_test = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH
+LQRVSLPRMVYPQPKVLTPCRKDVLVVTPWLAPIVWEGTFNIDILNEQFRLQNTTIGLTV
+FAIKKYVAFLKLFLETAEKHFMVGHRVHYYVFTDQPAAVPRVTLGTGRQLSVLEVRAYKR
+WQDVSMRRMEMISDFCERRFLSEVDYLVCVDVDMEFRDHVGVEILTPLFGTLHPGFYGSS
+REAFTYERRPQSQAYIPKDEGDFYYLGGFFGGSVQEVQRLTRACHQAMMVDQANGIEAVW
+HDESHLNKYLLRHKPTKVLSPEYLWDQQLLGWPAVLRKLRFTAVPKNHQAVRNP
+"""]
+
+encoded_input = tokenizer(x_test, padding=True, truncation=True, max_length=512, return_tensors="pt")
+input_ids = encoded_input["input_ids"]
+attention_mask = encoded_input["attention_mask"]
+
+with torch.no_grad():
+    output = model(input_ids, attention_mask=attention_mask)
+    logits = output.logits
+    probabilities = F.softmax(logits, dim=1)
+    _, predicted_labels = torch.max(logits, dim=1)
+probabilities[0]
+
+decoded_labels = label_encoder.inverse_transform(predicted_labels.tolist())
+decoded_labels
+
+
+glycosyltransferase_db = {
+    "GT31-chsy"      : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8  ', 'More Info': 'http://www.cazy.org/GT31.html'},
+    "GT2-CesA2"      : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT43-arath"     : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'},
+    "GT8-Met1"       : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9  ', 'More Info': 'http://www.cazy.org/GT8.html' },
+    "GT32-higher"    : {'CAZy Name': 'GT32', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT32.html'},
+    "GT40"           : {'CAZy Name': 'GT40', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT40.html'},
+    "GT16"           : {'CAZy Name': 'GT16', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6  ', 'More Info': 'http://www.cazy.org/GT16.html'},
+    "GT27"           : {'CAZy Name': 'GT27', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '5  ', 'More Info': 'http://www.cazy.org/GT27.html'},
+    "GT55"           : {'CAZy Name': 'GT55', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '2  ', 'More Info': 'http://www.cazy.org/GT55.html'},
+    "GT8-Glycogenin" : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9  ', 'More Info': 'http://www.cazy.org/GT8.html' },
+    "GT8-1"          : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9  ', 'More Info': 'http://www.cazy.org/GT8.html' },
+    "GT25"           : {'CAZy Name': 'GT25', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6  ', 'More Info': 'http://www.cazy.org/GT25.html'},
+    "GT2-DPM_like"   : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '2  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT31-fringe"    : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8  ', 'More Info': 'http://www.cazy.org/GT31.html'},
+    "GT2-Bact_puta"  : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT84"           : {'CAZy Name': 'GT84', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '1  ', 'More Info': 'http://www.cazy.org/GT84.html'},
+    "GT13"           : {'CAZy Name': 'GT13', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6  ', 'More Info': 'http://www.cazy.org/GT13.html'},
+    "GT43-cele"      : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'},
+    "GT2-Bact_LPS1"  : {'CAZy Name': 'GT92', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT2-Bact_Oant"  : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '   ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT67"           : {'CAZy Name': 'GT67', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8  ', 'More Info': 'http://www.cazy.org/GT67.html'},
+    "GT2-HAS"        : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT82"           : {'CAZy Name': 'GT82', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '7  ', 'More Info': 'http://www.cazy.org/GT82.html'},
+    "GT24"           : {'CAZy Name': 'GT24', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9  ', 'More Info': 'http://www.cazy.org/GT24.html'},
+    "GT31-plant"     : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8  ', 'More Info': 'http://www.cazy.org/GT31.html'},
+    "GT81-Bact"      : {'CAZy Name': 'GT81', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '2  ', 'More Info': 'http://www.cazy.org/GT81.html'},
+    "GT2-Bact_gt25Me": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '   ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT2-B3GntL"     : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '4  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT49"           : {'CAZy Name': 'GT49', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT49.html'},
+    "GT34"           : {'CAZy Name': 'GT34', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT34.html'},
+    "GT45"           : {'CAZy Name': 'GT45', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT45.html'},
+    "GT32-lower"     : {'CAZy Name': 'GT32', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT32.html'},
+    "GT88"           : {'CAZy Name': 'GT88', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9  ', 'More Info': 'http://www.cazy.org/GT88.html'},
+    "GT21"           : {'CAZy Name': 'GT21', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1  ', 'More Info': 'http://www.cazy.org/GT21.html'},
+    "GT2-DPG_synt"   : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '2  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT43-b3gat2"    : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'},
+    "GT2-Chitin_synt": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '5  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT8-Bact"       : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT8.html' },
+    "GT8-Met2"       : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT8.html' },
+    "GT2-Bact_Chlor1": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '   ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT54"           : {'CAZy Name': 'GT54', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '6  ', 'More Info': 'http://www.cazy.org/GT54.html'},
+    "GT2-Cel_bre3"   : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT2-Bact_Rham"  : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT6"            : {'CAZy Name': 'GT6 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT6.html' },
+    "GT2-Bact_puta2" : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '   ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT7-1"          : {'CAZy Name': 'GT7 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '5  ', 'More Info': 'http://www.cazy.org/GT7.html' },
+    "GT2-Csl"        : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '4  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT2-ExoU"       : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '   ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT2-Csl2"       : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '4  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT64"           : {'CAZy Name': 'GT64', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT64.html'},
+    "GT2-Bact_Chlor2": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '   ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT78"           : {'CAZy Name': 'GT78', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '2  ', 'More Info': 'http://www.cazy.org/GT78.html'},
+    "GT12"           : {'CAZy Name': 'GT12', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT12.html'},
+    "GT31-gnt"       : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8  ', 'More Info': 'http://www.cazy.org/GT31.html'},
+    "GT2-Bact_CHS"   : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '5  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT62"           : {'CAZy Name': 'GT62', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '3  ', 'More Info': 'http://www.cazy.org/GT62.html'},
+    "GT8-Met_Pla"    : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT8.html' },
+    "GT15"           : {'CAZy Name': 'GT15', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '8  ', 'More Info': 'http://www.cazy.org/GT15.html'},
+    "GT43-b3gat1"    : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'},
+    "GT31-b3glt"     : {'CAZy Name': 'GT31', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '8  ', 'More Info': 'http://www.cazy.org/GT31.html'},
+    "GT2-CesA1"      : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '1  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT60"           : {'CAZy Name': 'GT60', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '5  ', 'More Info': 'http://www.cazy.org/GT60.html'},
+    "GT14"           : {'CAZy Name': 'GT14', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '7  ', 'More Info': 'http://www.cazy.org/GT14.html'},
+    "GT2-Bact_DPM_sy": {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '2  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT17"           : {'CAZy Name': 'GT17', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '7  ', 'More Info': 'http://www.cazy.org/GT17.html'},
+    "GT2-Bact_LPS2"  : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '3  ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT77"           : {'CAZy Name': 'GT77', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9  ', 'More Info': 'http://www.cazy.org/GT77.html'},
+    "GT2-Bact_EpsO"  : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': '   ', 'More Info': 'http://www.cazy.org/GT2.html' },
+    "GT43-b3gat3"    : {'CAZy Name': 'GT43', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT43.html'},
+    "GT8-Fun"        : {'CAZy Name': 'GT8 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Retaining', 'Clade': '9  ', 'More Info': 'http://www.cazy.org/GT8.html' },
+    "GT75"           : {'CAZy Name': 'GT75', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT75.html'},
+    "GT2-Bact_GlfT"  : {'CAZy Name': 'GT2 ', 'Alternative Name': '', 'Fold': 'A', 'Mechanism': 'Inverting', 'Clade': 'N/A', 'More Info': 'http://www.cazy.org/GT2.html' },
+
+}
+
+
+
+
+
+def get_family_info(family_name):
+    family_info = glycosyltransferase_db.get(family_name, {})
+    # convert information to markdown formatted string
+    markdown_text = ""
+    for key, value in family_info.items():
+        if key == "more_info":
+            markdown_text += "**{}:**".format(key.title().replace("_", " ")) + "\n"
+            for link in value:
+                markdown_text += "[{}]({})\n".format(link, link)
+        else:
+            markdown_text += "**{}:** {}\n".format(key.title().replace("_", " "), value)
+    return markdown_text
+
+def fig_to_img(fig):
+    """Converts a matplotlib figure to a PIL Image and returns it"""
+    buf = io.BytesIO()
+    fig.savefig(buf, format='png', bbox_inches='tight')
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+
+def process_family_sequence(protein_fasta):
+    lines = protein_fasta.split('\n')
+    
+    headers = [line for line in lines if line.startswith('>')]
+    if len(headers) > 1:
+        return None, "Multiple fasta sequences detected. Please upload a fasta file with multiple sequences, otherwise only include one fasta sequence.", None
+
+    protein_sequence = ''.join(line for line in lines if not line.startswith('>'))
+
+    # Check for invalid characters
+    valid_characters = set("ACDEFGHIKLMNPQRSTVWYacdefghiklmnpqrstvwy")  # the 20 standard amino acids
+    if not set(protein_sequence).issubset(valid_characters):
+        return None, "Invalid protein sequence. It contains characters that are not one of the 20 standard amino acids. Does your sequence contain gaps?", None
+
+    encoded_input = tokenizer([protein_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt")
+    input_idsfam = encoded_input["input_ids"]
+    attention_maskfam = encoded_input["attention_mask"]
+
+    with torch.no_grad():
+        outputfam = modelfam(input_idsfam, attention_mask=attention_maskfam)
+        logitsfam = outputfam.logits
+        probabilitiesfam = F.softmax(logitsfam, dim=1)
+        _, predicted_labelsfam = torch.max(logitsfam, dim=1)
+
+    decoded_labelsfam = label_encoderfam.inverse_transform(predicted_labelsfam.tolist())
+    family_info = get_family_info(decoded_labelsfam[0])
+
+    figfam = plt.figure(figsize=(10, 5))
+    labelsfam = label_encoderfam.classes_
+    probabilitiesfam = probabilitiesfam.tolist()
+
+    # Convert the nested list to a flat list of probabilities
+    probabilitiesfam_flat = probabilitiesfam[0] if probabilitiesfam else []
+
+    # Sort labels and probabilities by probability
+    labels_probsfam = list(zip(labelsfam, probabilitiesfam_flat))
+    labels_probsfam.sort(key=lambda x: x[1], reverse=True)
+
+    # Select the top 5 fams
+    labels_probs_top5fam = labels_probsfam[:5]
+    labels_top5, probabilities_top5 = zip(*labels_probs_top5fam)
+
+    y_posfam = np.arange(len(labels_top5))
+
+    plt.barh(y_posfam, [prob*100 for prob in probabilities_top5], align='center', alpha=0.5)
+    plt.yticks(y_posfam, labels_top5)
+    plt.xlabel('Probability (%)')
+    plt.title('Top 5 Family Class Probabilities')
+    plt.xlim(0, 100)
+    plt.close(figfam)
+
+    img = fig_to_img(figfam)
+
+    if len(protein_sequence) < 100:
+        return decoded_labelsfam[0], img, None, f"**Warning:** The sequence is relatively short. Fragmentary and partial sequences may result in incorrect predictions. \n\n {family_info}"
+
+
+    return decoded_labelsfam[0], img, None, family_info
+
+
+def process_single_sequence(protein_fasta): #, protein_file
+
+    lines = protein_fasta.split('\n')
+
+    headers = [line for line in lines if line.startswith('>')]
+    if len(headers) > 1:
+        return None, "Multiple fasta sequences detected. Please upload a fasta file with multiple sequences, otherwise only include one fasta sequence.", None
+
+    protein_sequence = ''.join(line for line in lines if not line.startswith('>'))
+
+    # Check for invalid characters
+    valid_characters = set("ACDEFGHIKLMNPQRSTVWYacdefghiklmnpqrstvwy")  # the 20 standard amino acids
+    if not set(protein_sequence).issubset(valid_characters):
+        return None, "Invalid protein sequence. It contains characters that are not one of the 20 standard amino acids. Does your sequence contain gaps?", None
+
+
+    encoded_input = tokenizer([protein_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt")
+    input_ids = encoded_input["input_ids"]
+    attention_mask = encoded_input["attention_mask"]
+
+    with torch.no_grad():
+        output = model(input_ids, attention_mask=attention_mask)
+        logits = output.logits
+        dprobabilities = F.softmax(logits, dim=1)[0]
+        _, predicted_labels = torch.max(logits, dim=1)
+
+    decoded_labels = label_encoder.inverse_transform(predicted_labels.tolist())
+    family_info = get_family_info(decoded_labels[0])
+
+    fig = plt.figure(figsize=(10, 5))
+    labels = label_encoder.classes_
+    dprobabilities = dprobabilities.tolist()
+
+    # Sort labels and probabilities by probability
+    labels_probs = list(zip(labels, dprobabilities))
+    labels_probs.sort(key=lambda x: x[1], reverse=True)
+
+    # Select the top 3 donors
+    labels_probs_top3 = labels_probs[:3]
+    labels_top3, probabilities_top3 = zip(*labels_probs_top3)
+    
+    y_pos = np.arange(len(labels_top3))
+    
+    plt.barh(y_pos, [prob*100 for prob in probabilities_top3], align='center', alpha=0.5)
+    plt.yticks(y_pos, labels_top3)
+    plt.xlabel('Probability (%)')
+    plt.title('Top 3 Donor Class Probabilities')
+    plt.xlim(0, 100) 
+    plt.close(fig)
+
+    img = fig_to_img(fig)
+
+    if len(protein_sequence) < 100:
+        return decoded_labels[0], img, None, f"**Warning:** The sequence is relatively short. Fragmentary and partial sequences may result in incorrect predictions. \n\n {family_info}"
+
+
+    return decoded_labels[0], img, None, None
+
+def process_sequence_file(protein_file):  # added progress parameter that is displayed in gradio #, progress=gr.Progress()
+    try:
+        records = list(SeqIO.parse(protein_file.name, "fasta"))
+    except Exception as e:
+        return str(e)
+
+    if not os.path.exists('results'):
+        os.makedirs('results')
+
+    total = len(records)  
+
+    for idx, record in enumerate(records):
+        protein_sequence = str(record.seq)
+
+        valid_characters = set("ACDEFGHIKLMNPQRSTVWYacdefghiklmnpqrstvwy")  
+        if not set(protein_sequence).issubset(valid_characters):
+            with open(f'results/result_{idx+1}.txt', 'w') as file:
+                file.write("Invalid protein sequence. It contains characters that are not one of the 20 standard amino acids. Does your sequence contain gaps?")
+            continue
+
+        label, img, _, info = process_single_sequence(protein_sequence)
+        img.save(f'results/result_{idx+1}.png')
+        with open(f'results/result_{idx+1}.txt', 'w') as file:
+            file.write(f'Predicted Donor: {label}\n\n{info}')
+
+        # progress(idx/total)  # Update the progress bar
+    
+    # Create a zip file w/ results -- To Do: Figure out how to improve compression for large files
+    with zipfile.ZipFile('predicted_results.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
+        for root, dirs, files in os.walk('results/'):
+            for file in files:
+                zipf.write(os.path.join(root, file))
+
+    return 'predicted_results.zip' #Provide indication of how to interpret downloaded zip file? f"**Warning:** The sequence is relatively short. Fragmentary and partial sequences may result in incorrect predictions.
+
+    # Function to mask a residue at a particular position
+def mask_residue(sequence, position):
+    return sequence[:position] + 'X' + sequence[position+1:]
+
+def generate_heatmap(protein_fasta):
+
+    lines = protein_fasta.split('\n')
+
+    headers = [line for line in lines if line.startswith('>')]
+    if len(headers) > 1:
+        return None, "Multiple fasta sequences detected. Please upload a fasta file with multiple sequences, otherwise only include one fasta sequence.", None
+
+    protein_sequence = ''.join(line for line in lines if not line.startswith('>'))
+
+    # Check for invalid characters
+    valid_characters = set("ACDEFGHIKLMNPQRSTVWYacdefghiklmnpqrstvwy")  # the 20 standard amino acids
+    if not set(protein_sequence).issubset(valid_characters):
+        return None, "Invalid protein sequence. It contains characters that are not one of the 20 standard amino acids. Does your sequence contain gaps?", None
+
+
+    # Tokenize and predict for original sequence
+    encoded_input = tokenizer([protein_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt")
+    with torch.no_grad():
+        original_output = model(encoded_input["input_ids"], attention_mask=encoded_input["attention_mask"])
+    original_probabilities = F.softmax(original_output.logits, dim=1).cpu().numpy()[0]
+
+    # Initialize an array to hold the importance scores
+    importance_scores = np.zeros((len(protein_sequence), len(original_probabilities)))
+
+    # Define the size of each group
+    group_size = 10  # You can change this
+
+    # Initialize an array to hold the importance scores
+    num_groups = len(original_sequence) // group_size + (len(original_sequence) % group_size > 0)
+    importance_scores = np.zeros((num_groups, len(original_probabilities)))
+
+    # Initialize tqdm progress bar
+    # with tqdm(total=num_groups, desc="Processing groups", position=0, leave=True) as pbar:
+    #     # Loop through each group of residues in the sequence
+    for i in range(0, len(protein_sequence), group_size):
+        # Mask the residues in the group at positions [i, i + group_size)
+        masked_sequence = protein_sequence[:i] + 'X' * min(group_size, len(protein_sequence) - i) + protein_sequence[i + group_size:]
+        
+        # Tokenize and predict for the masked sequence
+        encoded_input = tokenizer([masked_sequence], padding=True, truncation=True, max_length=512, return_tensors="pt")
+        with torch.no_grad():
+            masked_output = model(encoded_input["input_ids"], attention_mask=encoded_input["attention_mask"])
+        masked_probabilities = F.softmax(masked_output.logits, dim=1).cpu().numpy()[0]
+        
+        # Calculate the change in probabilities and store it as the importance score
+        group_index = i // group_size
+        importance_scores[group_index, :] = np.abs(original_probabilities - masked_probabilities)
+            
+        progress = (i // group_size + 1) / num_groups * 100
+        print(f"Progress: {progress:.2f}%")
+    
+    figmap, ax = plt.subplots(figsize=(20, 20))
+    sns.heatmap(importance_scores, annot=True, cmap="coolwarm", xticklabels=label_encoder.classes_, yticklabels=[f"{i}-{i+group_size-1}" for i in range(0, len(original_sequence), group_size)], ax=ax)
+    ax.set_xlabel("Predicted Labels")
+    ax.set_ylabel("Residue Position Groups")
+
+    img = fig_to_img(figmap)
+
+    return img
+
+
+def main_function_single(sequence):
+    # Process seq, and return outputs for both fam and don
+    family_label, family_img, _, family_info = process_family_sequence(sequence)
+    donor_label, donor_img, *_ = process_single_sequence(sequence)
+    figmap = generate_heatmap(sequence)
+    return family_label, family_img, family_info, donor_label, donor_img, figmap
+
+def main_function_upload(protein_file): #, progress=gr.Progress()
+    return process_sequence_file(protein_file) #, progress
+
+prediction_imagefam = gr.outputs.Image(type='pil', label="Family prediction graph")
+prediction_imagedonor = gr.outputs.Image(type='pil', label="Donor prediction graph")
+prediction_explain = gr.outputs.Image(type='pil', label="Donor prediction explaination")
+
+
+with gr.Blocks() as app:
+    gr.Markdown("# Glydentify")
+
+    with gr.Tab("Single Sequence Prediction"):
+        with gr.Row().style(equal_height=True):
+            with gr.Column():
+                sequence = gr.inputs.Textbox(lines=15, placeholder='Enter Protein Sequence Here...', label="Protein Sequence")
+            with gr.Column():
+                with gr.Accordion("Example:"):
+                    gr.Markdown("""
+                                \>Q9LTZ9|GALS2_ARATH Galactan beta-1,4-galactosyltransferase GALS2
+                                MAKERDQNTKDKNLLICFLWNFSAELKLALMALLVLCTLATLLPFLPSSFSISASELRFC
+                                ISRIAVNSTSVNFTTVVEKPVLDNAVKLTEKPVLDNGVTKQPLTEEKVLNNGVIKRTFTG
+                                YGWAAYNFVLMNAYRGGVNTFAVIGLSSKPLHVYSHPTYRCEWIPLNQSDNRILTDGTKI
+                                LTDWGYGRVYTTVVVNCTFPSNTVINPKNTGGTLLLHATTGDTDRNITDSIPVLTETPNT
+                                VDFALYESNLRRREKYDYLYCGSSLYGNLSPQRIREWIAYHVRFFGERSHFVLHDAGGIT
+                                EEVFEVLKPWIELGRVTVHDIREQERFDGYYHNQFMVVNDCLHRYRFMAKWMFFFDVDEF
+                                IYVPAKSSISSVMVSLEEYSQFTIEQMPMSSQLCYDGDGPARTYRKWGFEKLAYRDVKKV
+                                PRRDRKYAVQPRNVFATGVHMSQHLQGKTYHRAEGKIRYFHYHGSISQRREPCRHLYNGT
+                                RIVHENN
+                                """)
+                family_prediction = gr.outputs.Textbox(label="Predicted family")
+                donor_prediction = gr.outputs.Textbox(label="Predicted donor")
+                info_markdown = gr.Markdown()
+
+        # Predict and Clear buttons
+        with gr.Row().style(equal_height=True):
+            with gr.Column():
+                predict_button = gr.Button("Predict")
+                predict_button.click(main_function_single, inputs=sequence,
+                                     outputs=[family_prediction, prediction_imagefam, info_markdown,
+                                              donor_prediction, prediction_imagedonor, prediction_explain])
+
+        # Family & Donor Section
+        with gr.Row().style(equal_height=True):
+            with gr.Column():
+                with gr.Accordion("Prediction Bar Graphs:"):
+                    prediction_imagefam.render() # = gr.outputs.Image(type='pil', label="Family prediction graph")
+                    prediction_imagedonor.render() # = gr.outputs.Image(type='pil', label="Donor prediction graph")
+
+            # Explain Section
+            with gr.Column():
+                with gr.Accordion("Donor explanation"):
+                    prediction_explain.render() # = gr.outputs.Image(type='pil', label="Donor prediction explaination")
+    
+    with gr.Tab("Multiple Sequence Prediction"):
+        with gr.Row().style(equal_height=True):
+            with gr.Column():
+                protein_file = gr.inputs.File(label="Upload FASTA file")
+            with gr.Column():
+                result_file = gr.outputs.File(label="Download predictions of uploaded sequences")
+                with gr.Row().style(equal_height=True):
+                    with gr.Column():
+                        process_button = gr.Button("Process")
+                        process_button.click(main_function_upload, inputs=protein_file, outputs=[result_file])                        
+                    with gr.Column():
+                        clear = gr.Button("Clear")
+                        clear.click(lambda: None) 
+            # clear.click()
+
+app.launch(show_error=True)
+
+

requirements.txt

@@ -0,0 +1,329 @@
+name: glydentify
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - aiofiles=22.1.0=pyhd8ed1ab_0
+  - aiohttp=3.7.4.post0=py39h3811e60_1
+  - aiosqlite=0.18.0=pyhd8ed1ab_0
+  - altair=4.2.2=pyhd8ed1ab_0
+  - anyio=3.6.2=pyhd8ed1ab_0
+  - aom=3.5.0=h27087fc_0
+  - argon2-cffi=21.3.0=pyhd8ed1ab_0
+  - argon2-cffi-bindings=21.2.0=py39hb9d737c_3
+  - asttokens=2.2.1=pyhd8ed1ab_0
+  - async-timeout=3.0.1=py_1000
+  - attrs=22.2.0=pyh71513ae_0
+  - aws-c-auth=0.7.0=hf8751d9_2
+  - aws-c-cal=0.6.0=h93469e0_0
+  - aws-c-common=0.8.23=hd590300_0
+  - aws-c-compression=0.2.17=h862ab75_1
+  - aws-c-event-stream=0.3.1=h9599702_1
+  - aws-c-http=0.7.11=hbe98c3e_0
+  - aws-c-io=0.13.28=h3870b5a_0
+  - aws-c-mqtt=0.8.14=h2e270ba_2
+  - aws-c-s3=0.3.13=heb0bb06_2
+  - aws-c-sdkutils=0.1.11=h862ab75_1
+  - aws-checksums=0.1.16=h862ab75_1
+  - aws-crt-cpp=0.20.3=he9c0e7f_4
+  - aws-sdk-cpp=1.10.57=hbc2ea52_17
+  - babel=2.12.1=pyhd8ed1ab_1
+  - backcall=0.2.0=pyh9f0ad1d_0
+  - backports=1.0=pyhd8ed1ab_3
+  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
+  - beautifulsoup4=4.12.2=pyha770c72_0
+  - biopython=1.81=py39h72bdee0_0
+  - bleach=6.0.0=pyhd8ed1ab_0
+  - brotli=1.0.9=h166bdaf_8
+  - brotli-bin=1.0.9=h166bdaf_8
+  - brotlipy=0.7.0=py39hb9d737c_1005
+  - bzip2=1.0.8=h7f98852_4
+  - c-ares=1.19.1=hd590300_0
+  - ca-certificates=2023.5.7=hbcca054_0
+  - certifi=2023.5.7=pyhd8ed1ab_0
+  - cffi=1.15.1=py39he91dace_3
+  - chardet=4.0.0=py39hf3d152e_3
+  - charset-normalizer=3.1.0=pyhd8ed1ab_0
+  - click=8.1.3=unix_pyhd8ed1ab_2
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - comm=0.1.3=pyhd8ed1ab_0
+  - contourpy=1.0.7=py39h4b4f3f3_0
+  - cryptography=40.0.2=py39h079d5ae_0
+  - cycler=0.11.0=pyhd8ed1ab_0
+  - dataclasses=0.8=pyhc8e2a94_3
+  - datasets=2.13.1=pyhd8ed1ab_0
+  - debugpy=1.6.7=py39h227be39_0
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - defusedxml=0.7.1=pyhd8ed1ab_0
+  - dill=0.3.6=pyhd8ed1ab_1
+  - entrypoints=0.4=pyhd8ed1ab_0
+  - executing=1.2.0=pyhd8ed1ab_0
+  - expat=2.5.0=hcb278e6_1
+  - fastapi=0.95.1=pyhd8ed1ab_0
+  - ffmpeg=5.1.2=gpl_h8dda1f0_106
+  - ffmpy=0.3.0=pyhb6f538c_0
+  - filelock=3.12.0=pyhd8ed1ab_0
+  - flit-core=3.8.0=pyhd8ed1ab_0
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=hab24e00_0
+  - fontconfig=2.14.2=h14ed4e7_0
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - fonttools=4.39.3=py39h72bdee0_0
+  - freetype=2.12.1=hca18f0e_1
+  - fsspec=2023.4.0=pyh1a96a4e_0
+  - gflags=2.2.2=he1b5a44_1004
+  - glog=0.6.0=h6f12383_0
+  - gmp=6.2.1=h58526e2_0
+  - gnutls=3.7.8=hf3e180e_0
+  - gradio=3.23.0=pyhd8ed1ab_0
+  - h11=0.14.0=pyhd8ed1ab_0
+  - h2=4.1.0=pyhd8ed1ab_0
+  - hpack=4.0.0=pyh9f0ad1d_0
+  - httpcore=0.17.0=pyhd8ed1ab_0
+  - httpx=0.24.0=pyhd8ed1ab_1
+  - huggingface_hub=0.16.4=pyhd8ed1ab_0
+  - hyperframe=6.0.1=pyhd8ed1ab_0
+  - icu=72.1=hcb278e6_0
+  - idna=3.4=pyhd8ed1ab_0
+  - importlib-metadata=6.5.0=pyha770c72_0
+  - importlib-resources=5.12.0=pyhd8ed1ab_0
+  - importlib_metadata=6.5.0=hd8ed1ab_0
+  - importlib_resources=5.12.0=pyhd8ed1ab_0
+  - ipykernel=6.22.0=pyh210e3f2_0
+  - ipython=8.12.0=pyh41d4057_0
+  - ipython_genutils=0.2.0=py_1
+  - jedi=0.18.2=pyhd8ed1ab_0
+  - jinja2=3.1.2=pyhd8ed1ab_1
+  - json5=0.9.5=pyh9f0ad1d_0
+  - jsonschema=4.17.3=pyhd8ed1ab_0
+  - jupyter_client=8.2.0=pyhd8ed1ab_0
+  - jupyter_core=5.3.0=py39hf3d152e_0
+  - jupyter_events=0.6.3=pyhd8ed1ab_0
+  - jupyter_server=2.5.0=pyhd8ed1ab_0
+  - jupyter_server_fileid=0.9.0=pyhd8ed1ab_0
+  - jupyter_server_terminals=0.4.4=pyhd8ed1ab_1
+  - jupyter_server_ydoc=0.8.0=pyhd8ed1ab_0
+  - jupyter_ydoc=0.2.3=pyhd8ed1ab_0
+  - jupyterlab=3.6.3=pyhd8ed1ab_0
+  - jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
+  - jupyterlab_server=2.22.1=pyhd8ed1ab_0
+  - keyutils=1.6.1=h166bdaf_0
+  - kiwisolver=1.4.4=py39hf939315_1
+  - krb5=1.21.1=h659d440_0
+  - lame=3.100=h166bdaf_1003
+  - lcms2=2.15=haa2dc70_1
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=4.0.0=h27087fc_0
+  - libabseil=20230125.3=cxx17_h59595ed_0
+  - libarrow=12.0.1=h657c46f_5_cpu
+  - libblas=3.9.0=16_linux64_openblas
+  - libbrotlicommon=1.0.9=h166bdaf_8
+  - libbrotlidec=1.0.9=h166bdaf_8
+  - libbrotlienc=1.0.9=h166bdaf_8
+  - libcblas=3.9.0=16_linux64_openblas
+  - libcrc32c=1.1.2=h9c3ff4c_0
+  - libcurl=8.2.0=hca28451_0
+  - libdeflate=1.18=h0b41bf4_0
+  - libdrm=2.4.114=h166bdaf_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=h516909a_1
+  - libevent=2.1.12=hf998b51_1
+  - libexpat=2.5.0=hcb278e6_1
+  - libffi=3.4.2=h7f98852_5
+  - libgcc-ng=12.2.0=h65d4601_19
+  - libgfortran-ng=12.2.0=h69a702a_19
+  - libgfortran5=12.2.0=h337968e_19
+  - libgomp=12.2.0=h65d4601_19
+  - libgoogle-cloud=2.12.0=h840a212_1
+  - libgrpc=1.56.2=h3905398_0
+  - libiconv=1.17=h166bdaf_0
+  - libidn2=2.1.0=h470a237_0
+  - libjpeg-turbo=2.1.5.1=h0b41bf4_0
+  - liblapack=3.9.0=16_linux64_openblas
+  - libnghttp2=1.52.0=h61bc06f_0
+  - libnsl=2.0.0=h7f98852_0
+  - libnuma=2.0.16=h0b41bf4_1
+  - libopenblas=0.3.21=pthreads_h78a6416_3
+  - libopus=1.3.1=h7f98852_1
+  - libpciaccess=0.17=h166bdaf_0
+  - libpng=1.6.39=h753d276_0
+  - libprotobuf=4.23.3=hd1fb520_0
+  - libsodium=1.0.18=h36c2ea0_1
+  - libsqlite=3.40.0=h753d276_0
+  - libssh2=1.11.0=h0841786_0
+  - libstdcxx-ng=12.2.0=h46fd767_19
+  - libtasn1=4.19.0=h166bdaf_0
+  - libthrift=0.18.1=h8fd135c_2
+  - libtiff=4.5.0=ha587672_6
+  - libutf8proc=2.8.0=h166bdaf_0
+  - libuuid=2.38.1=h0b41bf4_0
+  - libva=2.18.0=h0b41bf4_0
+  - libvpx=1.11.0=h9c3ff4c_3
+  - libwebp-base=1.3.0=h0b41bf4_0
+  - libxcb=1.13=h7f98852_1004
+  - libxml2=2.10.4=hfdac1af_0
+  - libzlib=1.2.13=h166bdaf_4
+  - linkify-it-py=2.0.0=pyhd8ed1ab_0
+  - lz4-c=1.9.4=hcb278e6_0
+  - markdown-it-py=2.2.0=pyhd8ed1ab_0
+  - markupsafe=2.1.2=py39h72bdee0_0
+  - matplotlib-base=3.7.1=py39he190548_0
+  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
+  - mdit-py-plugins=0.3.3=pyhd8ed1ab_0
+  - mdurl=0.1.0=pyhd8ed1ab_0
+  - mistune=2.0.5=pyhd8ed1ab_0
+  - multidict=6.0.4=py39h72bdee0_0
+  - multiprocess=0.70.14=py39hb9d737c_3
+  - munkres=1.1.4=pyh9f0ad1d_0
+  - nbclassic=0.5.5=pyhb4ecaf3_1
+  - nbclient=0.7.3=pyhd8ed1ab_0
+  - nbconvert=7.3.1=pyhd8ed1ab_0
+  - nbconvert-core=7.3.1=pyhd8ed1ab_0
+  - nbconvert-pandoc=7.3.1=pyhd8ed1ab_0
+  - nbformat=5.8.0=pyhd8ed1ab_0
+  - ncurses=6.3=h27087fc_1
+  - nest-asyncio=1.5.6=pyhd8ed1ab_0
+  - nettle=3.8.1=hc379101_1
+  - notebook=6.5.4=pyha770c72_0
+  - notebook-shim=0.2.2=pyhd8ed1ab_0
+  - numpy=1.24.2=py39h7360e5f_0
+  - openh264=2.3.1=hcb278e6_2
+  - openjpeg=2.5.0=hfec8fc6_2
+  - openssl=3.1.1=hd590300_1
+  - orc=1.9.0=h385abfd_1
+  - orjson=3.8.10=py39hd8b4457_0
+  - p11-kit=0.24.1=hc5aa10d_0
+  - packaging=23.1=pyhd8ed1ab_0
+  - pandas=2.0.0=py39h2ad29b5_0
+  - pandoc=2.19.2=h32600fe_2
+  - pandocfilters=1.5.0=pyhd8ed1ab_0
+  - parso=0.8.3=pyhd8ed1ab_0
+  - patsy=0.5.3=pyhd8ed1ab_0
+  - pexpect=4.8.0=pyh1a96a4e_2
+  - pickleshare=0.7.5=py_1003
+  - pillow=9.5.0=py39h7207d5c_0
+  - pip=23.1=pyhd8ed1ab_0
+  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0
+  - platformdirs=3.2.0=pyhd8ed1ab_0
+  - pooch=1.7.0=pyha770c72_3
+  - prometheus_client=0.16.0=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.38=pyha770c72_0
+  - prompt_toolkit=3.0.38=hd8ed1ab_0
+  - psutil=5.9.5=py39h72bdee0_0
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - pyarrow=12.0.1=py39hfbd5978_5_cpu
+  - pycparser=2.21=pyhd8ed1ab_0
+  - pydantic=1.10.7=py39h72bdee0_0
+  - pydub=0.25.1=pyhd8ed1ab_0
+  - pygments=2.15.1=pyhd8ed1ab_0
+  - pyopenssl=23.1.1=pyhd8ed1ab_0
+  - pyparsing=3.0.9=pyhd8ed1ab_0
+  - pyrsistent=0.19.3=py39h72bdee0_0
+  - pysocks=1.7.1=pyha2e5f31_6
+  - python=3.9.16=h2782a2a_0_cpython
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-fastjsonschema=2.16.3=pyhd8ed1ab_0
+  - python-json-logger=2.0.7=pyhd8ed1ab_0
+  - python-multipart=0.0.6=pyhd8ed1ab_0
+  - python-tzdata=2023.3=pyhd8ed1ab_0
+  - python-xxhash=3.2.0=py39h72bdee0_0
+  - python_abi=3.9=3_cp39
+  - pytz=2023.3=pyhd8ed1ab_0
+  - pyyaml=6.0=py39hb9d737c_5
+  - pyzmq=25.0.2=py39h0be026e_0
+  - rdma-core=28.9=h59595ed_1
+  - re2=2023.03.02=h8c504da_0
+  - readline=8.2=h8228510_1
+  - regex=2023.6.3=py39hd1e30aa_0
+  - requests=2.28.2=pyhd8ed1ab_1
+  - responses=0.18.0=pyhd8ed1ab_0
+  - rfc3339-validator=0.1.4=pyhd8ed1ab_0
+  - rfc3986-validator=0.1.1=pyh9f0ad1d_0
+  - s2n=1.3.46=h06160fa_0
+  - sacremoses=0.0.53=pyhd8ed1ab_0
+  - safetensors=0.3.1=py39h9fdd4d6_0
+  - scipy=1.10.1=py39he83f1e1_0
+  - seaborn=0.12.2=hd8ed1ab_0
+  - seaborn-base=0.12.2=pyhd8ed1ab_0
+  - semantic_version=2.10.0=pyhd8ed1ab_0
+  - send2trash=1.8.0=pyhd8ed1ab_0
+  - setuptools=67.6.1=pyhd8ed1ab_0
+  - six=1.16.0=pyh6c4a22f_0
+  - snappy=1.1.10=h9fff704_0
+  - sniffio=1.3.0=pyhd8ed1ab_0
+  - soupsieve=2.3.2.post1=pyhd8ed1ab_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - starlette=0.26.1=pyhd8ed1ab_0
+  - statsmodels=0.13.5=py39h2ae25f5_2
+  - svt-av1=1.4.1=hcb278e6_0
+  - terminado=0.17.1=pyh41d4057_0
+  - tinycss2=1.2.1=pyhd8ed1ab_0
+  - tk=8.6.12=h27826a3_0
+  - tokenizers=0.13.3=py39h585fa2d_0
+  - tomli=2.0.1=pyhd8ed1ab_0
+  - toolz=0.12.0=pyhd8ed1ab_0
+  - tornado=6.3=py39h72bdee0_0
+  - tqdm=4.65.0=pyhd8ed1ab_1
+  - traitlets=5.9.0=pyhd8ed1ab_0
+  - transformers=4.31.0
+  - typing-extensions=4.5.0=hd8ed1ab_0
+  - typing_extensions=4.5.0=pyha770c72_0
+  - tzdata=2023c=h71feb2d_0
+  - uc-micro-py=1.0.1=pyhd8ed1ab_0
+  - ucx=1.14.1=hf587318_2
+  - unicodedata2=15.0.0=py39hb9d737c_0
+  - urllib3=1.26.15=pyhd8ed1ab_0
+  - uvicorn=0.21.1=py39hf3d152e_0
+  - wcwidth=0.2.6=pyhd8ed1ab_0
+  - webencodings=0.5.1=py_1
+  - websocket-client=1.5.1=pyhd8ed1ab_0
+  - websockets=11.0.2=py39h72bdee0_0
+  - wheel=0.40.0=pyhd8ed1ab_0
+  - wordcloud=1.8.2.2=py39hb9d737c_1
+  - x264=1!164.3095=h166bdaf_2
+  - x265=3.5=h924138e_3
+  - xorg-fixesproto=5.0=h7f98852_1002
+  - xorg-kbproto=1.0.7=h7f98852_1002
+  - xorg-libx11=1.8.4=h0b41bf4_0
+  - xorg-libxau=1.0.9=h7f98852_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xorg-libxext=1.3.4=h0b41bf4_2
+  - xorg-libxfixes=5.0.3=h7f98852_1004
+  - xorg-xextproto=7.3.0=h0b41bf4_1003
+  - xorg-xproto=7.0.31=h7f98852_1007
+  - xxhash=0.8.1=h0b41bf4_0
+  - xz=5.2.6=h166bdaf_0
+  - y-py=0.5.9=py39h50f1755_0
+  - yaml=0.2.5=h7f98852_2
+  - yarl=1.8.2=py39hb9d737c_0
+  - ypy-websocket=0.8.2=pyhd8ed1ab_0
+  - zeromq=4.3.4=h9c3ff4c_1
+  - zipp=3.15.0=pyhd8ed1ab_0
+  - zstd=1.5.2=h3eb15da_6
+  - pip:
+      - cmake==3.25.0
+      - cssselect2==0.7.0
+      - glypy==1.0.8
+      - hjson==3.1.0
+      - joblib==1.3.1
+      - lit==15.0.7
+      - lxml==4.9.2
+      - mpmath==1.2.1
+      - networkx==3.0
+      - pmw-py3==2.1
+      - preppy==4.2.1
+      - scikit-learn==1.3.0
+      - svglib==1.4.1
+      - sympy==1.11.1
+      - threadpoolctl==3.2.0
+      - torch==2.0.1
+      - torchaudio==2.0.2
+      - torchvision==0.15.2
+      - triton==2.0.0