app

app.py

@@ -0,0 +1,551 @@
+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
+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, device, 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
+
+device = "cpu"
+
+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)
+
+
+
+
+
+