Update README.md

README.md

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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+## NLP App Hugging Face's logo
+Hugging Face
+# Streamlit app with computer vision 💡
+Elbrus Bootcamp | Phase-2 | Team Project 
+
+## Team🧑🏻‍💻
+1. [Awlly](https://github.com/Awlly)
+2. [sakoser](https://github.com/sakoser)
+3. [whoisida]https://github.com/whoisida
+
+## Task 📌lassifi
+Create a service that classifies movie reviews into good, neutral and bad categories, a service that classifies user input as toxic or non-toxic, as well as a GPT 2 based text generation service that was trained to emulate a certain author’s writing.
+
+## Contents 📝
+1. Classifies movie reviewsusing LSTM,ruBert,BOW 💨 [Dataset](https://drive.google.com/file/d/1c92sz81bEfOw-rutglKpmKGm6rySmYbt/view?usp=sharing)
+2. classifies user input as toxic or non-toxi using ruBert-tiny-toxicity 📑 [Dataset](https://drive.google.com/file/d/1O7orH9CrNEhnbnA5KjXji8sgrn6iD5n-/view?usp=drive_link)
+3. GPT 2 based text generation service
+
+## Deployment 🎈
+The service is implemented on [Hugging Face](https://huggingface.co/spaces/Awlly/NLP_app)
+
+## Libraries 📖
+```python
+import os
+import unicodedata
+import nltk
+from dataclasses import dataclass
+import joblib
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+from torchvision.datasets import ImageFolder
+from torchvision import datasets
+from torchvision import transforms as T
+from torchvision.io import read_image
+from torch.utils.data import Dataset, random_split
+import torchutils as tu
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from typing import Tuple
+from tqdm import tqdm
+from transformers import AutoModel, AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+import pydensecrf.densecrf as dcrf
+import pydensecrf.utils as dcrf_utils
+from preprocessing import data_preprocessing
+import streamlit as st
+import string
+from sklearn.linear_model import LogisticRegression
+import re
+
+
+
+
+from preprocessing import preprocess_single_string
+```
+
+
+from preprocessing import data_preprocessing
+
+
+
+
+## Guide 📜 
+####  How to run locally?
+
+1. To create a Python virtual environment for running the code, enter:
+
+    ``python3 -m venv my-env``
+
+2. Activate the new environment:
+
+    * Windows: ```my-env\Scripts\activate.bat```
+    * macOS and Linux: ```source my-env/bin/activate```
+