app.py

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import spacy
import gensim
from gensim.summarization.keypoints import keywords
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
import torch
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import numpy as np
import pickle
def receive_file():
    # Code for receiving a file
    return file

def send_file(file):
    # Code for sending a file
    return None

# Define the functions for providing training through files
def training_function(file):
    # Code for providing training through a file
    return None

# Create the main function
def main():
    # Code for the main function
    return None

# Test the chatbot
def test_chatbot():
    # Code for testing the chatbot
    return None

# Convert the chatbot into a program in the Gradient Space
def gradient_space_program():
    # Code for converting the chatbot into a program in the Gradient Space
    return None

# Call the main function
if __name__ == "__main__":
    main()