app.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import gradio as gr
import pandas as pd
import numpy as np
import torch
from torch import nn
from torch.nn import init, MarginRankingLoss
from torch.optim import Adam
from distutils.version import LooseVersion
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
import math
from transformers import AutoConfig, AutoModel, AutoTokenizer
import nltk
import re
import torch.optim as optim
from transformers import AutoModelForMaskedLM
import torch.nn.functional as F
import random


# In[2]:


# eng_dict = []
# with open('eng_dict.txt', 'r') as file:
#     # Read each line from the file and append it to the list
#     for line in file:
#         # Remove leading and trailing whitespace (e.g., newline characters)
#         cleaned_line = line.strip()
#         eng_dict.append(cleaned_line)


# In[14]:


def greet(X, ny):
    global eng_dict
    ny = int(ny)
    if ny == 0:
        rand_no = random.random()
        tok_map = {2: 0.4363429005892416,
                   1: 0.6672580202327398,
                   4: 0.7476060740459144,
                   3: 0.9618703668504087,
                   6: 0.9701028532809564,
                   7: 0.9729244545819342,
                   8: 0.9739508754144756,
                   5: 0.9994508859743607,
                   9: 0.9997507867114407,
                   10: 0.9999112969650892,
                   11: 0.9999788802297832,
                   0: 0.9999831041838266,
                   12: 0.9999873281378701,
                   22: 0.9999957760459568,
                   14: 1.0000000000000002}
        for key in tok_map.keys():
            if rand_no < tok_map[key]:
                num_sub_tokens_label = key
                break
    else:
        num_sub_tokens_label = ny
    tokenizer = AutoTokenizer.from_pretrained("microsoft/graphcodebert-base")
    model = AutoModelForMaskedLM.from_pretrained("microsoft/graphcodebert-base")
    model.load_state_dict(torch.load('model_26_2'))
    model.eval()
    X_init = X
    X_init = X_init.replace("[MASK]", " [MASK] ")
    X_init = X_init.replace("[MASK]", " ".join([tokenizer.mask_token] * num_sub_tokens_label))
    tokens = tokenizer.encode_plus(X_init, add_special_tokens=False,return_tensors='pt')
    input_id_chunki = tokens['input_ids'][0].split(510)
    input_id_chunks = []
    mask_chunks  = []
    mask_chunki = tokens['attention_mask'][0].split(510)
    for tensor in input_id_chunki:
        input_id_chunks.append(tensor)
    for tensor in mask_chunki:
        mask_chunks.append(tensor)
    xi = torch.full((1,), fill_value=101)
    yi = torch.full((1,), fill_value=1)
    zi = torch.full((1,), fill_value=102)
    for r in range(len(input_id_chunks)):
        input_id_chunks[r] = torch.cat([xi, input_id_chunks[r]],dim = -1)
        input_id_chunks[r] = torch.cat([input_id_chunks[r],zi],dim=-1)
        mask_chunks[r] = torch.cat([yi, mask_chunks[r]],dim=-1)
        mask_chunks[r] = torch.cat([mask_chunks[r],yi],dim=-1)
    di = torch.full((1,), fill_value=0)
    for i in range(len(input_id_chunks)):
        pad_len = 512 - input_id_chunks[i].shape[0]
        if pad_len > 0:
            for p in range(pad_len):
                input_id_chunks[i] = torch.cat([input_id_chunks[i],di],dim=-1)
                mask_chunks[i] = torch.cat([mask_chunks[i],di],dim=-1)
    vb = torch.ones_like(input_id_chunks[0])
    fg = torch.zeros_like(input_id_chunks[0])
    maski = []
    for l in range(len(input_id_chunks)):
        masked_pos = []
        for i in range(len(input_id_chunks[l])):
            if input_id_chunks[l][i] == tokenizer.mask_token_id: #103
                if i != 0 and input_id_chunks[l][i-1] == tokenizer.mask_token_id:
                    continue
                masked_pos.append(i)
        maski.append(masked_pos)
    input_ids = torch.stack(input_id_chunks)
    att_mask = torch.stack(mask_chunks)
    outputs = model(input_ids, attention_mask = att_mask)
    last_hidden_state = outputs[0].squeeze()
    l_o_l_sa = []
    sum_state = []
    for t in range(num_sub_tokens_label):
        c = []
        l_o_l_sa.append(c)
    if len(maski) == 1:
        masked_pos = maski[0]
        for k in masked_pos:
            for t in range(num_sub_tokens_label):
                l_o_l_sa[t].append(last_hidden_state[k+t])
    else:
        for p in range(len(maski)):
            masked_pos = maski[p]
            for k in masked_pos:
                for t in range(num_sub_tokens_label):
                    if (k+t) >= len(last_hidden_state[p]):
                        l_o_l_sa[t].append(last_hidden_state[p+1][k+t-len(last_hidden_state[p])])
                        continue
                    l_o_l_sa[t].append(last_hidden_state[p][k+t])
    for t in range(num_sub_tokens_label):
        sum_state.append(l_o_l_sa[t][0])
    for i in range(len(l_o_l_sa[0])):
        if i == 0:
            continue
        for t in range(num_sub_tokens_label):
            sum_state[t] = sum_state[t] + l_o_l_sa[t][i]
    yip = len(l_o_l_sa[0])
#     qw = []
    er = ""
    val = 0.0
    for t in range(num_sub_tokens_label):
        sum_state[t] /= yip
        idx = torch.topk(sum_state[t], k=5, dim=0)[1]
        probs = F.softmax(sum_state[t], dim=0)
        wor = [tokenizer.decode(i.item()).strip() for i in idx]
        cnt = 0
        for kl in wor:
            if all(char.isalpha() for char in kl):
#                 qw.append(kl.lower())
                er+=kl
                break 
            cnt+=1
        val = val - torch.log(probs[idx[cnt]])
    val = val/num_sub_tokens_label
    vali = round(val.item(), 2)
        
#     print(er)
#     astr = ""
#     for j in range(len(qw)):
#         mock = ""
#         mock+= qw[j]
#         if (j+2) < len(qw) and ((mock+qw[j+1]+qw[j+2]) in eng_dict):
#             mock +=qw[j+1]
#             mock +=qw[j+2]
#             j = j+2
#         elif (j+1) < len(qw) and ((mock+qw[j+1]) in eng_dict):
#             mock +=qw[j+1]
#             j = j+1
#         if len(astr) == 0:
#             astr+=mock
#         else:
#             astr+=mock.capitalize()     
    er = er+" (with PLL value of: "+str(vali)+")"
    return er
title = "Rename a variable in a Java class"
description = """This model is a fine-tuned GraphCodeBERT model fine-tuned to output higher-quality variable names for Java classes. Long classes are handled by the 
model. Replace any variable name with a "[MASK]" to get an identifier renaming.
"""
ex = [["""import java.io.*;
public class x {
public static void main(String[] args) {
String f = "file.txt";
BufferedReader [MASK] = null;
String l;
try {
[MASK] = new BufferedReader(new FileReader(f));
while ((l = [MASK].readLine()) != null) {
System.out.println(l);
            }
        } catch (IOException e) {
e.printStackTrace();
        } finally {
try {
if ([MASK] != null) [MASK].close();
            } catch (IOException ex) {
ex.printStackTrace();
            }
        }
    }
}""", "0"], ["""import java.net.*;
import java.io.*;

public class s {
    public static void main(String[] args) throws IOException {
        ServerSocket [MASK] = new ServerSocket(8000);
        try {
            Socket s = [MASK].accept();
            PrintWriter pw = new PrintWriter(s.getOutputStream(), true);
            BufferedReader br = new BufferedReader(new InputStreamReader(s.getInputStream()));
            String i;
            while ((i = br.readLine()) != null) {
                pw.println(i);
            }
        } finally {
            if ([MASK] != null) [MASK].close();
        }
    }
}""", "2"], ["""import java.io.*;
import java.util.*;

public class y {
    public static void main(String[] args) {
        String [MASK] = "data.csv";
        String l = "";
        String cvsSplitBy = ",";
        try (BufferedReader br = new BufferedReader(new FileReader([MASK]))) {
            while ((l = br.readLine()) != null) {
                String[] z = l.split(cvsSplitBy);
                System.out.println("Values [field-1= " + z[0] + " , field-2=" + z[1] + "]");
            }
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}""", "2"]]
# We instantiate the Textbox class
textbox = gr.Textbox(label="Type Java code snippet:", placeholder="replace variable with [MASK]", lines=10)
textbox1 = gr.Textbox(label="Number of tokens in name:", placeholder="0 for randomly sampled number of tokens",lines=1)

gr.Interface(title = title, description = description, examples = ex, fn=greet, inputs=[
        textbox,textbox1
    ], outputs="text").launch()


# In[ ]: