app

app.py

@@ -1,544 +0,0 @@
-import streamlit as st
-from io import StringIO
-from Bio import SeqIO
-
-st.title("IRES-LM prediction and mutation")
-
-# Input sequence
-st.subheader("Input sequence")
-
-seq = st.text_area("FASTA format only", value=">vir_CVB3_ires_00505.1\nTTAAAACAGCCTGTGGGTTGATCCCACCCACAGGCCCATTGGGCGCTAGCACTCTGGTATCACGGTACCTTTGTGCGCCTGTTTTATACCCCCTCCCCCAACTGTAACTTAGAAGTAACACACACCGATCAACAGTCAGCGTGGCACACCAGCCACGTTTTGATCAAGCACTTCTGTTACCCCGGACTGAGTATCAATAGACTGCTCACGCGGTTGAAGGAGAAAGCGTTCGTTATCCGGCCAACTACTTCGAAAAACCTAGTAACACCGTGGAAGTTGCAGAGTGTTTCGCTCAGCACTACCCCAGTGTAGATCAGGTCGATGAGTCACCGCATTCCCCACGGGCGACCGTGGCGGTGGCTGCGTTGGCGGCCTGCCCATGGGGAAACCCATGGGACGCTCTAATACAGACATGGTGCGAAGAGTCTATTGAGCTAGTTGGTAGTCCTCCGGCCCCTGAATGCGGCTAATCCTAACTGCGGAGCACACACCCTCAAGCCAGAGGGCAGTGTGTCGTAACGGGCAACTCTGCAGCGGAACCGACTACTTTGGGTGTCCGTGTTTCATTTTATTCCTATACTGGCTGCTTATGGTGACAATTGAGAGATCGTTACCATATAGCTATTGGATTGGCCATCCGGTGACTAATAGAGCTATTATATATCCCTTTGTTGGGTTTATACCACTTAGCTTGAAAGAGGTTAAAACATTACAATTCATTGTTAAGTTGAATACAGCAAA")
-st.subheader("Upload sequence file")
-uploaded = st.file_uploader("Sequence file in FASTA format")
-
-# augments
-global output_filename, start_nt_position, end_nt_position, mut_by_prob, transform_type, mlm_tok_num, n_mut, n_designs_ep, n_sampling_designs_ep, n_mlm_recovery_sampling, mutate2stronger
-output_filename = st.text_input("output a .csv file", value='IRES_LM_prediction_mutation')
-start_nt_position = st.number_input("The start position of the mutation of this sequence, the first position is defined as 0", value=0)
-end_nt_position = st.number_input("The last position of the mutation of this sequence, the last position is defined as length(sequence)-1 or -1", value=-1)
-mut_by_prob = st.checkbox("Mutated by predicted Probability or Transformed Probability of the sequence", value=True)
-transform_type = st.selectbox("Type of probability transformation", 
-                              ['', 'sigmoid', 'logit', 'power_law', 'tanh'],
-                              index=2)
-mlm_tok_num = st.number_input("Number of masked tokens for each sequence per epoch", value=1)
-n_mut = st.number_input("Maximum number of mutations for each sequence", value=3)
-n_designs_ep = st.number_input("Number of mutations per epoch", value=10)
-n_sampling_designs_ep = st.number_input("Number of sampling mutations from n_designs_ep per epoch", value=5)
-n_mlm_recovery_sampling = st.number_input("Number of MLM recovery samplings (with AGCT recovery)", value=1)
-mutate2stronger = st.checkbox("Mutate to stronger IRES variant, otherwise mutate to weaker IRES", value=True)
-
-if not mut_by_prob and transform_type != '':
-    st.write("--transform_type must be '' when --mut_by_prob is False")
-    transform_type = ''
-
-global model, device
-device = "cpu"
-state_dict = torch.load('model.pt', map_location=torch.device(device))
-new_state_dict = OrderedDict()
-
-for k, v in state_dict.items():
-    name = k.replace('module.','')
-    new_state_dict[name] = v
-
-model = CNN_linear().to(device)
-model.load_state_dict(new_state_dict, strict = False)
-
-# Import necessary libraries
-# import matplotlib
-# import matplotlib.pyplot as plt
-import numpy as np
-import os
-import pandas as pd
-# import pathlib
-import random
-# import scanpy as sc
-# import seaborn as sns
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-# from argparse import Namespace
-from collections import Counter, OrderedDict
-from copy import deepcopy
-from esm import Alphabet, FastaBatchedDataset, ProteinBertModel, pretrained, MSATransformer
-from esm.data import *
-from esm.model.esm2 import ESM2
-# from sklearn import preprocessing
-# from sklearn.metrics import (confusion_matrix, roc_auc_score, auc,
-#                              precision_recall_fscore_support,
-#                              precision_recall_curve, classification_report,
-#                              roc_auc_score, average_precision_score,
-#                              precision_score, recall_score, f1_score,
-#                              accuracy_score)
-# from sklearn.model_selection import StratifiedKFold
-# from sklearn.utils import class_weight
-# from scipy.stats import spearmanr, pearsonr
-from torch import nn
-from torch.nn import Linear
-from torch.nn.utils.rnn import pad_sequence
-from torch.utils.data import Dataset, DataLoader
-from tqdm import tqdm, trange
-
-# Set global variables
-# matplotlib.rcParams.update({'font.size': 7})
-seed = 19961231
-random.seed(seed)
-np.random.seed(seed)
-torch.manual_seed(seed)
-# torch.cuda.manual_seed(seed)
-# torch.backends.cudnn.deterministic = True
-# torch.backends.cudnn.benchmark = False
-
-
-global idx_to_tok, prefix, epochs, layers, heads, fc_node, dropout_prob, embed_dim, batch_toks, repr_layers, evaluation, include, truncate, return_contacts, return_representation, mask_toks_id, finetune
-
-epochs = 5
-layers = 6
-heads = 16
-embed_dim = 128
-batch_toks = 4096
-fc_node = 64
-dropout_prob = 0.5
-folds = 10
-repr_layers = [-1]
-include = ["mean"]
-truncate = True
-finetune = False
-return_contacts = False
-return_representation = False
-
-global tok_to_idx, idx_to_tok, mask_toks_id
-alphabet = Alphabet(mask_prob = 0.15, standard_toks = 'AGCT')
-assert alphabet.tok_to_idx == {'<pad>': 0, '<eos>': 1, '<unk>': 2, 'A': 3, 'G': 4, 'C': 5, 'T': 6, '<cls>': 7, '<mask>': 8, '<sep>': 9}
-
-# tok_to_idx = {'<pad>': 0, '<eos>': 1, '<unk>': 2, 'A': 3, 'G': 4, 'C': 5, 'T': 6, '<cls>': 7, '<mask>': 8, '<sep>': 9}
-tok_to_idx = {'-': 0, '&': 1, '?': 2, 'A': 3, 'G': 4, 'C': 5, 'T': 6, '!': 7, '*': 8, '|': 9}
-idx_to_tok = {idx: tok for tok, idx in tok_to_idx.items()}
-# st.write(tok_to_idx)
-mask_toks_id = 8
-
-global w1, w2, w3
-w1, w2, w3 = 1, 1, 100
-
-class CNN_linear(nn.Module):
-    def __init__(self):
-        super(CNN_linear, self).__init__()
-        
-        self.esm2 = ESM2(num_layers = layers,
-                         embed_dim = embed_dim,
-                         attention_heads = heads,
-                         alphabet = alphabet)
-        
-        self.dropout = nn.Dropout(dropout_prob)
-        self.relu = nn.ReLU()
-        self.flatten = nn.Flatten()
-        self.fc = nn.Linear(in_features = embed_dim, out_features = fc_node)
-        self.output = nn.Linear(in_features = fc_node, out_features = 2)        
-            
-    def predict(self, tokens):
-        
-        x = self.esm2(tokens, [layers], need_head_weights=False, return_contacts=False, return_representation = True)
-        x_cls = x["representations"][layers][:, 0]
-        
-        o = self.fc(x_cls)
-        o = self.relu(o)
-        o = self.dropout(o)
-        o = self.output(o)
-        
-        y_prob = torch.softmax(o, dim = 1)
-        y_pred = torch.argmax(y_prob, dim = 1)
-        
-        if transform_type:
-            y_prob_transformed = prob_transform(y_prob[:,1])
-            return y_prob[:,1], y_pred, x['logits'], y_prob_transformed
-        else:
-            return y_prob[:,1], y_pred, x['logits'], o[:,1]
-    
-    def forward(self, x1, x2):
-        logit_1, repr_1 = self.predict(x1)
-        logit_2, repr_2 = self.predict(x2)
-        return (logit_1, logit_2), (repr_1, repr_2)
-
-def prob_transform(prob, **kwargs): # Logits
-    """
-    Transforms probability values based on the specified method.
-
-    :param prob: torch.Tensor, the input probabilities to be transformed
-    :param transform_type: str, the type of transformation to be applied
-    :param kwargs: additional parameters for transformations
-    :return: torch.Tensor, transformed probabilities
-    """
-
-    if transform_type == 'sigmoid':
-        x0 = kwget('x0', 0.5)
-        k = kwget('k', 10.0)
-        prob_transformed = 1 / (1 + torch.exp(-k * (prob - x0)))
-    
-    elif transform_type == 'logit':
-        # Adding a small value to avoid log(0) and log(1)
-        prob_transformed = torch.log(prob + 1e-6) - torch.log(1 - prob + 1e-6)
-    
-    elif transform_type == 'power_law':
-        gamma = kwget('gamma', 2.0)
-        prob_transformed = torch.pow(prob, gamma)
-    
-    elif transform_type == 'tanh':
-        k = kwget('k', 2.0)
-        prob_transformed = torch.tanh(k * prob)
-    
-    return prob_transformed
-
-def random_replace(sequence, continuous_replace=False):
-    if end_nt_position == -1: end_nt_position = len(sequence)
-    if start_nt_position < 0 or end_nt_position > len(sequence) or start_nt_position > end_nt_position:
-#         raise ValueError("Invalid start/end positions")
-        st.write("Invalid start/end positions")
-        start_nt_position, end_nt_position = 0, -1
-    
-    # 将序列切片成三部分：替换区域前、替换区域、替换区域后
-    pre_segment = sequence[:start_nt_position]
-    target_segment = list(sequence[start_nt_position:end_nt_position + 1])  # +1因为Python的切片是右开区间
-    post_segment = sequence[end_nt_position + 1:]
-    
-    if not continuous_replace:
-        # 随机替换目标片段的mlm_tok_num个位置
-        indices = random.sample(range(len(target_segment)), mlm_tok_num)
-        for idx in indices:
-            target_segment[idx] = '*'
-    else:
-        # 在目标片段连续替换mlm_tok_num个位置
-        max_start_idx = len(target_segment) - mlm_tok_num  # 确保从i开始的n_mut个元素不会超出目标片段的长度
-        if max_start_idx < 1:  # 如果目标片段长度小于mlm_tok_num，返回原始序列
-            return target_segment
-        start_idx = random.randint(0, max_start_idx)
-        for idx in range(start_idx, start_idx + mlm_tok_num):
-            target_segment[idx] = '*'
-    
-    # 合并并返回最终的序列
-    return ''.join([pre_segment] + target_segment + [post_segment])
-
-
-def mlm_seq(seq):
-    seq_token, masked_sequence_token = [7],[7]
-    seq_token += [tok_to_idx[token] for token in seq]
-    
-    masked_seq = random_replace(seq, n_mut) # 随机替换n_mut个元素为'*'
-    masked_seq_token += [tok_to_idx[token] for token in masked_seq]
-    
-    return seq, masked_seq, torch.LongTensor(seq_token), torch.LongTensor(masked_seq_token)
-
-def batch_mlm_seq(seq_list, continuous_replace = False):
-    batch_seq = []
-    batch_masked_seq = []
-    batch_seq_token_list = []
-    batch_masked_seq_token_list = []
-
-    for i, seq in enumerate(seq_list):
-        seq_token, masked_seq_token = [7], [7]
-        seq_token += [tok_to_idx[token] for token in seq]
-
-        masked_seq = random_replace(seq, continuous_replace)  # 随机替换n_mut个元素为'*'
-        masked_seq_token += [tok_to_idx[token] for token in masked_seq]
-
-        batch_seq.append(seq)
-        batch_masked_seq.append(masked_seq)
-
-        batch_seq_token_list.append(seq_token)
-        batch_masked_seq_token_list.append(masked_seq_token)
-
-    return batch_seq, batch_masked_seq, torch.LongTensor(batch_seq_token_list), torch.LongTensor(batch_masked_seq_token_list)
-
-def recovered_mlm_tokens(masked_seqs, masked_toks, esm_logits, exclude_low_prob = False):
-    # Only remain the AGCT logits
-    esm_logits = esm_logits[:,:,3:7]
-    # Get the predicted tokens using argmax
-    predicted_toks = (esm_logits.argmax(dim=-1)+3).tolist()
-    
-    batch_size, seq_len, vocab_size = esm_logits.size()
-    if exclude_low_prob: min_prob = 1 / vocab_size
-    # Initialize an empty list to store the recovered sequences
-    recovered_sequences, recovered_toks = [], []
-    
-    for i in range(batch_size):
-        recovered_sequence_i, recovered_tok_i = [], []
-        for j in range(seq_len):
-            if masked_toks[i][j] == 8:
-                st.write(i,j)
-                ### Sample M recovery sequences using the logits
-                recovery_probs = torch.softmax(esm_logits[i, j], dim=-1)
-                recovery_probs[predicted_toks[i][j]-3] = 0  # Exclude the most probable token
-                if exclude_low_prob: recovery_probs[recovery_probs < min_prob] = 0  # Exclude tokens with low probs < min_prob
-                recovery_probs /= recovery_probs.sum()  # Normalize the probabilities
-                
-                ### 有放回抽样
-                max_retries = 5
-                retries = 0
-                success = False
-
-                while retries < max_retries and not success:
-                    try:
-                        recovery_indices = list(np.random.choice(vocab_size, size=n_mlm_recovery_sampling, p=recovery_probs.cpu().detach().numpy(), replace=False))
-                        success = True  # 设置成功标志
-                    except ValueError as e:
-                        retries += 1
-                        st.write(f"Attempt {retries} failed with error: {e}")
-                        if retries >= max_retries:
-                            st.write("Max retries reached. Skipping this iteration.")
-                
-                ### recovery to sequence
-                if retries < max_retries:
-                    for idx in [predicted_toks[i][j]] + [3+i for i in recovery_indices]:
-                        recovery_seq = deepcopy(list(masked_seqs[i]))
-                        recovery_tok = deepcopy(masked_toks[i])
-                        
-                        recovery_tok[j] = idx
-                        recovery_seq[j-1] = idx_to_tok[idx]
-                        
-                        recovered_tok_i.append(recovery_tok)
-                        recovered_sequence_i.append(''.join(recovery_seq))
-                        
-        recovered_sequences.extend(recovered_sequence_i)
-        recovered_toks.extend(recovered_tok_i)
-    return recovered_sequences, torch.LongTensor(torch.stack(recovered_toks))
-
-def recovered_mlm_multi_tokens(masked_seqs, masked_toks, esm_logits, exclude_low_prob = False):
-    # Only remain the AGCT logits
-    esm_logits = esm_logits[:,:,3:7]
-    # Get the predicted tokens using argmax
-    predicted_toks = (esm_logits.argmax(dim=-1)+3).tolist()
-    
-    batch_size, seq_len, vocab_size = esm_logits.size()
-    if exclude_low_prob: min_prob = 1 / vocab_size
-    # Initialize an empty list to store the recovered sequences
-    recovered_sequences, recovered_toks = [], []
-    
-    for i in range(batch_size):
-        recovered_sequence_i, recovered_tok_i = [], []
-        recovered_masked_num = 0
-        for j in range(seq_len):
-            if masked_toks[i][j] == 8:
-                ### Sample M recovery sequences using the logits
-                recovery_probs = torch.softmax(esm_logits[i, j], dim=-1)
-                recovery_probs[predicted_toks[i][j]-3] = 0  # Exclude the most probable token
-                if exclude_low_prob: recovery_probs[recovery_probs < min_prob] = 0  # Exclude tokens with low probs < min_prob
-                recovery_probs /= recovery_probs.sum()  # Normalize the probabilities
-                
-                ### 有放回抽样
-                max_retries = 5
-                retries = 0
-                success = False
-
-                while retries < max_retries and not success:
-                    try:
-                        recovery_indices = list(np.random.choice(vocab_size, size=n_mlm_recovery_sampling, p=recovery_probs.cpu().detach().numpy(), replace=False))
-                        success = True  # 设置成功标志
-                    except ValueError as e:
-                        retries += 1
-                        st.write(f"Attempt {retries} failed with error: {e}")
-                        if retries >= max_retries:
-                            st.write("Max retries reached. Skipping this iteration.")
-                
-                ### recovery to sequence 
-                        
-                if recovered_masked_num == 0:
-                    if retries < max_retries:
-                        for idx in [predicted_toks[i][j]] + [3+i for i in recovery_indices]:
-                            recovery_seq = deepcopy(list(masked_seqs[i]))
-                            recovery_tok = deepcopy(masked_toks[i])
-
-                            recovery_tok[j] = idx
-                            recovery_seq[j-1] = idx_to_tok[idx]
-
-                            recovered_tok_i.append(recovery_tok)
-                            recovered_sequence_i.append(''.join(recovery_seq))
-
-                elif recovered_masked_num > 0:
-                    if retries < max_retries:
-                        for idx in [predicted_toks[i][j]] + [3+i for i in recovery_indices]:
-                            for recovery_seq, recovery_tok in zip(list(recovered_sequence_i), list(recovered_tok_i)): # 要在循环开始之前获取列表的副本来进行迭代。这样，在循环中即使我们修改了原始的列表，也不会影响迭代的行为。
-
-                                recovery_seq_temp = list(recovery_seq)
-                                recovery_tok[j] = idx
-                                recovery_seq_temp[j-1] = idx_to_tok[idx]
-                                
-                                recovered_tok_i.append(recovery_tok)
-                                recovered_sequence_i.append(''.join(recovery_seq_temp))
-                
-                recovered_masked_num += 1                
-        recovered_indices = [i for i, s in enumerate(recovered_sequence_i) if '*' not in s]
-        recovered_tok_i = [recovered_tok_i[i] for i in recovered_indices]
-        recovered_sequence_i = [recovered_sequence_i[i] for i in recovered_indices]
-
-        recovered_sequences.extend(recovered_sequence_i)
-        recovered_toks.extend(recovered_tok_i)
-        
-        recovered_sequences, recovered_toks = remove_duplicates_double(recovered_sequences, recovered_toks)
-
-    return recovered_sequences, torch.LongTensor(torch.stack(recovered_toks))
-
-def mismatched_positions(s1, s2):
-    # 这个函数假定两个字符串的长度相同。
-    """Return the number of positions where two strings differ."""
-    
-    # The number of mismatches will be the sum of positions where characters are not the same
-    return sum(1 for c1, c2 in zip(s1, s2) if c1 != c2)
-
-def remove_duplicates_triple(filtered_mut_seqs, filtered_mut_probs, filtered_mut_logits):
-    seen = {}
-    unique_seqs = []
-    unique_probs = []
-    unique_logits = []
-    
-    for seq, prob, logit in zip(filtered_mut_seqs, filtered_mut_probs, filtered_mut_logits):
-        if seq not in seen:
-            unique_seqs.append(seq)
-            unique_probs.append(prob)
-            unique_logits.append(logit)
-            seen[seq] = True
-
-    return unique_seqs, unique_probs, unique_logits
-
-def remove_duplicates_double(filtered_mut_seqs, filtered_mut_probs):
-    seen = {}
-    unique_seqs = []
-    unique_probs = []
-    
-    for seq, prob in zip(filtered_mut_seqs, filtered_mut_probs):
-        if seq not in seen:
-            unique_seqs.append(seq)
-            unique_probs.append(prob)
-            seen[seq] = True
-
-    return unique_seqs, unique_probs
-
-def mutated_seq(wt_seq, wt_label):
-    wt_seq = '!'+ wt_seq
-    wt_tok = torch.LongTensor([[tok_to_idx[token] for token in wt_seq]]).to(device)
-    wt_prob, wt_pred, _, wt_logit = model.predict(wt_tok)
-    st.write('Wild Type: Length')
-    st.write(f'Wild Type: Length = {len(wt_seq)} \n{wt_seq}')
-    st.write(f'Wild Type: Label = {wt_label}, Y_pred = {wt_pred.item()}, Y_prob = {wt_prob.item():.2%}')
-    st.write('sdafdasWild Type: Length')
-#     st.write(n_mut, mlm_tok_num, n_designs_ep, n_sampling_designs_ep, n_mlm_recovery_sampling, mutate2stronger)
-    # pbar = tqdm(total=n_mut)
-    mutated_seqs = []
-    i = 1
-    # pbar = st.progress(i, text="mutated number of sequence")
-    while i <= n_mut:
-        if i == 1: seeds_ep = [wt_seq[1:]]
-        seeds_next_ep, seeds_probs_next_ep, seeds_logits_next_ep = [], [], []
-        for seed in seeds_ep:
-            seed_seq, masked_seed_seq, seed_seq_token, masked_seed_seq_token = batch_mlm_seq([seed] * n_designs_ep, continuous_replace = True) ### mask seed with 1 site to "*"
-
-            seed_prob, seed_pred, _, seed_logit = model.predict(seed_seq_token[0].unsqueeze_(0).to(device))
-            _, _, seed_esm_logit, _ = model.predict(masked_seed_seq_token.to(device))
-            mut_seqs, mut_toks = recovered_mlm_multi_tokens(masked_seed_seq, masked_seed_seq_token, seed_esm_logit)
-            mut_probs, mut_preds, mut_esm_logits, mut_logits = model.predict(mut_toks.to(device))
-
-            ### Filter mut_seqs that mut_prob < seed_prob and mut_prob < wild_prob
-            filtered_mut_seqs = []
-            filtered_mut_probs = []
-            filtered_mut_logits = []
-            if mut_by_prob:
-                for z in range(len(mut_seqs)):
-                    if mutate2stronger:
-                        if mut_probs[z] >= seed_prob and mut_probs[z] >= wt_prob:
-                            filtered_mut_seqs.append(mut_seqs[z])
-                            filtered_mut_probs.append(mut_probs[z].cpu().detach().numpy())
-                            filtered_mut_logits.append(mut_logits[z].cpu().detach().numpy())
-                    else:
-                        if mut_probs[z] < seed_prob and mut_probs[z] < wt_prob:
-                            filtered_mut_seqs.append(mut_seqs[z])
-                            filtered_mut_probs.append(mut_probs[z].cpu().detach().numpy())
-                            filtered_mut_logits.append(mut_logits[z].cpu().detach().numpy())
-            else:
-                for z in range(len(mut_seqs)):
-                    if mutate2stronger:
-                        if mut_logits[z] >= seed_logit and mut_logits[z] >= wt_logit:
-                            filtered_mut_seqs.append(mut_seqs[z])
-                            filtered_mut_probs.append(mut_probs[z].cpu().detach().numpy())
-                            filtered_mut_logits.append(mut_logits[z].cpu().detach().numpy())
-                    else:
-                        if mut_logits[z] < seed_logit and mut_logits[z] < wt_logit:
-                            filtered_mut_seqs.append(mut_seqs[z])
-                            filtered_mut_probs.append(mut_probs[z].cpu().detach().numpy())
-                            filtered_mut_logits.append(mut_logits[z].cpu().detach().numpy())
-                
-            
-
-            ### Save
-            seeds_next_ep.extend(filtered_mut_seqs)
-            seeds_probs_next_ep.extend(filtered_mut_probs)
-            seeds_logits_next_ep.extend(filtered_mut_logits)
-            seeds_next_ep, seeds_probs_next_ep, seeds_logits_next_ep = remove_duplicates_triple(seeds_next_ep, seeds_probs_next_ep, seeds_logits_next_ep)
-
-        ### Sampling based on prob
-        if len(seeds_next_ep) > n_sampling_designs_ep:
-            seeds_probs_next_ep_norm = seeds_probs_next_ep / sum(seeds_probs_next_ep)  # Normalize the probabilities
-            seeds_index_next_ep = np.random.choice(len(seeds_next_ep), n_sampling_designs_ep, p = seeds_probs_next_ep_norm, replace = False)
-            
-            seeds_next_ep = np.array(seeds_next_ep)[seeds_index_next_ep]
-            seeds_probs_next_ep = np.array(seeds_probs_next_ep)[seeds_index_next_ep]
-            seeds_logits_next_ep = np.array(seeds_logits_next_ep)[seeds_index_next_ep]
-        seeds_mutated_num_next_ep = [mismatched_positions(wt_seq[1:], s) for s in seeds_next_ep]
-        
-        mutated_seqs.extend(list(zip(seeds_next_ep, seeds_logits_next_ep, seeds_probs_next_ep, seeds_mutated_num_next_ep)))
-
-        seeds_ep = seeds_next_ep
-        i += 1
-        # pbar.update(1)
-        # pbar.progress(i/n_mut, text="Mutating")
-    # pbar.close()
-    # st.success('Done', icon="✅")
-    mutated_seqs.extend([(wt_seq[1:], wt_logit.item(), wt_prob.item(), 0)])
-    mutated_seqs = sorted(mutated_seqs, key=lambda x: x[2], reverse=True)
-    mutated_seqs = pd.DataFrame(mutated_seqs, columns = ['mutated_seq', 'predicted_logit', 'predicted_probability', 'mutated_num']).drop_duplicates('mutated_seq')
-    return mutated_seqs
-
-def read_raw(raw_input):
-    ids = []
-    sequences = []
-
-    file = StringIO(raw_input)
-    for record in SeqIO.parse(file, "fasta"):
-
-        # 检查序列是否只包含A, G, C, T
-        sequence = str(record.seq.back_transcribe()).upper()
-        if not set(sequence).issubset(set("AGCT")):
-            st.write(f"Record '{record.description}' was skipped for containing invalid characters. Only A, G, C, T(U) are allowed.")
-            continue
-
-        # 将符合条件的序列添加到列表中
-        ids.append(record.id)
-        sequences.append(sequence)
-    
-    return ids, sequences
-
-def predict_raw(raw_input):
-    model.eval()
-    # st.write(model)
-    # st.write('====Parse Input====')
-    ids, seqs = read_raw(raw_input)
-
-    # st.write('====Predict====')
-    res_pd = pd.DataFrame()
-    for wt_seq, wt_id in zip(seqs, ids):
-        # try:
-        st.write(wt_id, wt_seq)
-        res = mutated_seq(wt_seq, wt_id)
-        st.write(res)
-        res_pd.append(res)
-        # except:
-            # st.write('====Please Try Again this sequence: ', wt_id, wt_seq)
-    st.write(res_pd)
-    return res_pd
-
-# Run    
-if st.button("Predict and Mutate"):
-    if uploaded:
-        result = predict_raw(uploaded.getvalue().decode())
-    else:
-        result = predict_raw(seq)
-    
-    result_file = result.to_csv(index=False)
-    st.download_button("Download", result_file, file_name=output_filename+".csv")
-    st.dataframe(result)