metadata
license: apache-2.0
from flask import Flask, render_template, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer import torch
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-finetuned-shona") model = AutoModelForCausalLM.from_pretrained("Davlan/xlm-roberta-base-finetuned-shona")
app = Flask(name)
@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)
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()