app.py

from flask import Flask, render_template, request, jsonify
import os
os.environ['TRANSFORMERS_CACHE'] = '/code/cache/'
#os.environ['SENTENCE_TRANSFORMERS_HOME'] = './.cache'
#from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import T5Tokenizer, T5ForConditionalGeneration  

#import numpy as np
from transformers import AdamW
#import pandas as pd
import torch
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from torch.nn.utils.rnn import pad_sequence

MODEL_NAME='t5-base'
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
INPUT_MAX_LEN = 512 
OUTPUT_MAX_LEN = 512

#tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
#model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")

tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME, model_max_length=512)

app = Flask(__name__)
app.jinja_env.auto_reload = True
app.config['TEMPLATES_AUTO_RELOAD'] = True


@app.route("/")
def index():
    return render_template('chat.html')


@app.route("/get", methods=["GET", "POST"])
def chat():
    msg = request.form["msg"]
    input = msg
    return get_Chat_response(input)

class T5Model(pl.LightningModule):
    
    def __init__(self):
        super().__init__()
        self.model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict = True)

        
    def forward(self, input_ids, attention_mask, labels=None):
        
        output = self.model(
        input_ids=input_ids, 
        attention_mask=attention_mask, 
        labels=labels
        )
        return output.loss, output.logits
    
    def training_step(self, batch, batch_idx):

        input_ids = batch["input_ids"]
        attention_mask = batch["attention_mask"]
        labels= batch["target"]
        loss, logits = self(input_ids , attention_mask, labels)

        
        self.log("train_loss", loss, prog_bar=True, logger=True)

        return {'loss': loss}
    
    def validation_step(self, batch, batch_idx):
        input_ids = batch["input_ids"]
        attention_mask = batch["attention_mask"]
        labels= batch["target"]
        loss, logits = self(input_ids, attention_mask, labels)

        self.log("val_loss", loss, prog_bar=True, logger=True)
        
        return {'val_loss': loss}

    def configure_optimizers(self):
        return AdamW(self.parameters(), lr=0.0001)

train_model = T5Model.load_from_checkpoint('best-model-version.ckpt',map_location=DEVICE)
train_model.freeze()

def get_Chat_response(question):

    inputs_encoding =  tokenizer(
        question,
        add_special_tokens=True,
        max_length= INPUT_MAX_LEN,
        padding = 'max_length',
        truncation='only_first',
        return_attention_mask=True,
        return_tensors="pt"
        )

    
    generate_ids = train_model.model.generate(
        input_ids = inputs_encoding["input_ids"],
        attention_mask = inputs_encoding["attention_mask"],
        max_length = INPUT_MAX_LEN,
        num_beams = 4,
        num_return_sequences = 1,
        no_repeat_ngram_size=2,
        early_stopping=True,
        )

    preds = [
        tokenizer.decode(gen_id,
        skip_special_tokens=True, 
        clean_up_tokenization_spaces=True)
        for gen_id in generate_ids
    ]

    return "".join(preds)

#def get_Chat_response(text):
#
#    # Let's chat for 5 lines
#    for step in range(5):
#        # encode the new user input, add the eos_token and return a tensor in Pytorch
#        new_user_input_ids = tokenizer.encode(str(text) + tokenizer.eos_token, return_tensors='pt')
#
#        # append the new user input tokens to the chat history
#        bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
#
#        # generated a response while limiting the total chat history to 1000 tokens, 
#        chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
#
#        # pretty print last ouput tokens from bot
#        return tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)

if __name__ == '__main__':
    app.run(debug=True)