Create app.py

app.py

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+import tqdm
+from Bio import SeqIO
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import os
+import json
+from typing import Dict
+from collections import Counter
+import random
+import obonet
+from transformers import T5Tokenizer, T5EncoderModel
+import torch
+import re
+import gradio as gr
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+# Load the tokenizer
+tokenizer = T5Tokenizer.from_pretrained('Rostlab/prot_t5_xl_half_uniref50-enc', do_lower_case=False) #.to(device)
+
+# Load the model
+model = T5EncoderModel.from_pretrained("Rostlab/prot_t5_xl_half_uniref50-enc").to(device)
+
+def get_embeddings(seq):
+    sequence_examples = [" ".join(list(re.sub(r"[UZOB]", "X", seq)))]
+
+    ids = tokenizer.batch_encode_plus(sequence_examples, add_special_tokens=True, padding="longest")
+
+    input_ids = torch.tensor(ids['input_ids']).to(device)
+    attention_mask = torch.tensor(ids['attention_mask']).to(device)
+
+    # generate embeddings
+    with torch.no_grad():
+        embedding_repr = model(input_ids=input_ids,
+                               attention_mask=attention_mask)
+
+    # extract residue embeddings for the first ([0,:]) sequence in the batch and remove padded & special tokens ([0,:7])
+    emb_0 = embedding_repr.last_hidden_state[0]
+    emb_0_per_protein = emb_0.mean(dim=0)
+
+    return emb_0_per_protein
+
+def predict(filepath):
+    sequences = SeqIO.parse(filepath, "fasta")
+
+    ids = []
+    num_sequences=sum(1 for seq in sequences)
+    embeds = np.zeros((num_sequences, 1024))
+    i = 0
+    with open(filepath, "r") as fasta_file:
+      # Iterate over each sequence in the file
+      for sequence in SeqIO.parse(fasta_file, "fasta"):
+        # Access the sequence ID and sequence data
+        seq_id = sequence.id
+        seq_data = str(sequence.seq)
+        embeds[i] = get_embeddings(seq_data).detach().cpu().numpy()
+        print(embeds[i])
+        ids.append(seq_id)
+        i += 1
+        
+    INPUT_SHAPE=[1024]
+    num_of_labels=1500
+
+    model = tf.keras.Sequential([
+        tf.keras.layers.BatchNormalization(input_shape=INPUT_SHAPE),
+        tf.keras.layers.Dense(units=512, activation='relu'),
+        tf.keras.layers.Dropout(0.2),
+        tf.keras.layers.Dense(units=512, activation='relu'),
+        tf.keras.layers.Dropout(0.2),
+        tf.keras.layers.Dense(units=512, activation='relu'),
+        tf.keras.layers.Dropout(0.2),
+        tf.keras.layers.Dense(units=num_of_labels, activation='sigmoid')
+    ])
+
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+        loss='binary_crossentropy',
+        metrics=['binary_accuracy', tf.keras.metrics.AUC()]
+    )
+    
+    model.load_weights('./my_model.weights.h5') #load model here
+    labels_df=pd.read_csv('./labels.csv')
+    labels_df=labels_df.drop(columns='Unnamed: 0')
+
+    predictions = model.predict(embeds)
+    predictions_list1=[]
+    predictions_list2=[]
+
+    # 'predictions' will contain the model's output for the custom input tensor
+    # print(predictions)
+    for prediction in predictions:
+        tmp=[]
+        t2=[]
+        for i in prediction:
+            x=0 if i<0.4 else 1
+            tmp.append(x)
+            t2.append(i)
+        predictions_list1.append(tmp.copy())
+        predictions_list2.append(t2.copy())
+
+    label_columns = labels_df.columns
+
+    # Convert the predictions into a DataFrame
+    predictions_df = pd.DataFrame(predictions_list1, columns=label_columns)
+    p21=pd.DataFrame(predictions_list2, columns=label_columns)
+
+    # Save the DataFrame to a CSV file
+    predictions_df.to_csv("predictions.csv", index=False) #output csv
+    p21.to_csv("decimal.csv",index=False)
+    return "predictions.csv"
+
+gr.Interface(
+    predict,
+    title = 'Protein Function Prediction using fasta file,upload a fasta file',
+    inputs="file",
+    outputs="file"
+).launch()