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# from transformers import AutoModelForCausalLM, AutoTokenizer
import time
import datetime
import streamlit as st
question = "Name the planets in the solar system? A: "
question = "Quais são os planetas do sistema solar?"
question = "Qual é o maior planeta do sistema solar?"
before = datetime.datetime.now()
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-1.5-6B-Chat")
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-1.5-6B-Chat")
st.write('tokenizando...')
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate
st.write('gerando a saida...')
generate_ids = model.generate(inputs.input_ids, max_length=30)
output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
st.write('saída gerada')
st.write(output)
# Use a pipeline as a high-level helper
# from transformers import pipeline
# messages = [
# {"role": "user", "content": question},
# ]
# print('gerando a saida...')
# st.write('gerando a saida...')
# pipe = pipeline("text-generation", model="01-ai/Yi-1.5-34B-Chat")
# st.write('pipeline...')
# output = pipe(messages)
# st.write('saída gerada...')
# st.write(output)
# print('tokenizando...')
# tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# print('tokenizado.')
# print('carregando o modelo...')
# # Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
# model = AutoModelForCausalLM.from_pretrained(
# model_path,
# device_map="auto",
# torch_dtype='auto'
# ).eval()
# print('modelo carreegado.')
# # Prompt content: "hi"
# messages = [
# {"role": "user", "content": question}
# ]
# print('tokenizando o prompt...')
# input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors='pt')
# print('prompt tokenizado.')
# print('gerando a saida...')
# output_ids = model.generate(input_ids, eos_token_id=tokenizer.eos_token_id,
# max_new_tokens=10) #10 # 45
# # max_new_tokens=22)
print('saida gerada.')
# print('Decodificando a saida...')
# response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# print('saida decodificada.')
# Model response: "Hello! How can I assist you today?"
# print(response)
# question = output['choices'][0]['text'].split('A:')[0]
# answer = output['choices'][0]['text'].split('A:')[1]
# answer = 'A: ' + answer
print('\n\n')
print(question)
print(response)
after = datetime.datetime.now()
current_time = (after - before) # .strftime("%H:%M:%S")
print("\nTime Elapsed: ", current_time)
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