Delete app.py

app.py

@@ -1,118 +0,0 @@
-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()