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# -*- coding: utf-8 -*-
"""Flan-t5-xl_with_GPU.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/15P4GWaUNFBqJf5_I58DxSiusjPiHUeU6
"""
#import gradio as gr
#def greet(name)
# return "Hello" + name + "!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()
#theme = gr.themes.Soft().set(
# body_background_fill='*background_fill_secondary',
# body_text_color_subdued='*body_text_color',
# body_text_color_subdued_dark='*chatbot_code_background_color'
#)
#app = gr.Interface(
# fn=qa_result,
# btn=gr.UploadButton("📁", file_types=[".pdf", ".csv", ".doc"], ),
# inputs=['textbox', 'text', 'file'],
# outputs='textbox',
# title='Բարև՛, ինչպե՞ս ես։',
# theme=theme,
# description='Ի՞նչ հարցեր ունես։'
#)
import os
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512"
from IPython.display import HTML, display
def set_css():
display(HTML('''
<style>
pre {
white-space: pre-wrap;
}
</style>
'''))
#get_ipython().events.register('pre_run_cell', set_css)
import multiprocessing
import torch
torch.cuda.empty_cache()
from deep_translator import GoogleTranslator
# Use any translator you like, in this example GoogleTranslator
#translated = GoogleTranslator(source='hy', target='en').translate("Բարև, ո՞նց ես։") # output -> Hello, how are you?
#device = "cuda:0" if torch.cuda.is_available() else "cpu"
#device
import streamlit as st
# Set the query parameter to request GPU support
#st.experimental_set_query_parameter('gpu', 'true')
#x = st.slider('Select a value')
#st.write(x, 'squared is', x * x)
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map = "auto")
# Move the model to the GPU
model = model.to('cuda')
# We are running FP32!
#my_text = "Summarize: \
#Science can ignite new discoveries for society, \
#Society has the tendency to refer to old, familiar ways of doing. \
#Chaos is also a part of our society, although increasingly often so.\
#Innovative ways lead to new growthin businesses and under certain conditions all of society participates."
#my_text = "Write an essay with 100 words about Quantum Physics and it's problems regarding our understanding of laws of Physics."
#my_text = "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
# my_text = "A short explanation of machine learning for medical applications."
def process():
##translated = st.text_input("Գրեք ձեր հարցը: ")
##my_text = GoogleTranslator(source='hy', target='en').translate(translated)
##input_ids = tokenizer(my_text, return_tensors = "pt").input_ids.to("cuda")
#From Here
# User input
user_input = st.text_input("Գրեք ձեր հարցը...", "")
my_text = GoogleTranslator(source='hy', target='en').translate(user_input)
if my_text:
# Tokenize input and move to the GPU
input_ids = tokenizer.encode(my_text, return_tensors="pt").to('cuda')
# Generate text
with torch.no_grad():
outputs = model.generate(input_ids)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
#To Here
#outputs = model.generate(input_ids,
#min_length = 20,
#max_new_tokens = 600,
#length_penalty = 1.0, # Set to values < 1.0 in order to encourage the model to generate shorter answers.
#num_beams = 10,
#no_repeat_ngram_size = 3,
#temperature = 0,
#top_k = 150, # default 50
#top_p = 0.92,
#repetition_penalty = 2.1)
#generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write(GoogleTranslator(source='en', target='hy').translate(generated_text))
process()
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