app.py

import argparse
import re
import os
import streamlit as st
import random
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import tokenizers
#os.environ["TOKENIZERS_PARALLELISM"] = "false"
random.seed(None)
suggested_text_list = ['פעם אחת, לפני שנים רבות','שלום, קוראים לי דורון ואני','בוקר טוב לכולם','ואז הפרתי את כל כללי הטקס כש']
@st.cache(hash_funcs={tokenizers.Tokenizer: id, tokenizers.AddedToken: id})
def load_model(model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return model, tokenizer
def extend(input_text, num_return_sequences, max_size=20, top_k=50, top_p=0.95):
    if len(input_text) == 0:
        input_text = ""
    encoded_prompt = tokenizer.encode(
    input_text, add_special_tokens=False, return_tensors="pt")
    encoded_prompt = encoded_prompt.to(device)
    if encoded_prompt.size()[-1] == 0:
        input_ids = None
    else:
        input_ids = encoded_prompt
    
    output_sequences = model.generate(
    input_ids=input_ids,
    max_length=max_size + len(encoded_prompt[0]),
    top_k=top_k, 
    top_p=top_p, 
    do_sample=True,
    num_return_sequences=num_return_sequences)
    # Remove the batch dimension when returning multiple sequences
    if len(output_sequences.shape) > 2:
        output_sequences.squeeze_()
    generated_sequences = []
    print(output_sequences)
    for generated_sequence_idx, generated_sequence in enumerate(output_sequences):        
        generated_sequence = generated_sequence.tolist()
        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
        print(text)
        total_sequence = (
            input_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
        )
        #generated_sequences.append(total_sequence)
        generated_sequences.append(text)
    
    parsed_text = total_sequence.replace("<|startoftext|>", "").replace("\r","").replace("\n\n", "\n")
    if len(parsed_text) == 0:
        parsed_text = "שגיאה"
    return parsed_text
if __name__ == "__main__":
    st.title("Hebrew GPT Neo (Small)")
    pre_model_path = "BigSalmon/InformalToFormalLincoln14"
    model, tokenizer = load_model(pre_model_path)
    stop_token = "<|endoftext|>"
    new_lines = "\n\n\n"
    np.random.seed(None)
    random_seed = np.random.randint(10000,size=1)    
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
    torch.manual_seed(random_seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(random_seed)
    model.to(device)
    text_area = st.text_area("Enter the first few words (or leave blank), tap on \"Generate Text\" below. Tapping again will produce a different result.", 'האיש האחרון בעולם ישב לבד בחדרו כשלפתע נשמעה נקישה')
    st.sidebar.subheader("Configurable parameters")
    max_len = st.sidebar.slider("Max-Length", 0, 256, 5,help="The maximum length of the sequence to be generated.")
    num_return_sequences = st.sidebar.slider("Outputs", 1, 50, 5,help="The number of outputs.")
    top_k = st.sidebar.slider("Top-K", 0, 100, 40, help="The number of highest probability vocabulary tokens to keep for top-k-filtering.")
    top_p = st.sidebar.slider("Top-P", 0.0, 1.0, 0.92, help="If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.")
    if st.button("Generate Text"):
        with st.spinner(text="Generating results..."):
            st.subheader("Result")
            print(f"device:{device}, n_gpu:{n_gpu}, random_seed:{random_seed}, maxlen:{max_len}, top_k:{top_k}, top_p:{top_p}")
            if len(text_area.strip()) == 0:
                text_area = random.choice(suggested_text_list)
            result = extend(input_text=text_area,
                            num_return_sequences=int(num_return_sequences),                         
                            max_size=int(max_len),                   
                            top_k=int(top_k),
                            top_p=float(top_p))
            print("Done length: " + str(len(result)) + " bytes") 
            #<div class="rtl" dir="rtl" style="text-align:right;">
            st.markdown(f"{result}", unsafe_allow_html=True)
            st.write("\n\nResult length: " + str(len(result)) + " bytes")
            print(f"\"{result}\"")