import transformers
import streamlit as st

from transformers import AutoTokenizer, AutoModelForCausalLM

st.set_page_config(
    page_title="Romanian Text Generator",
    page_icon="🇷🇴",
    layout="wide"
)

st.write("Type your text here and press Ctrl+Enter to generate the next sequence:")

model_list = [
    "dumitrescustefan/gpt-neo-romanian-780m",
    "readerbench/RoGPT2-base",
    "readerbench/RoGPT2-medium",
    "readerbench/RoGPT2-large"
]

st.sidebar.header("Select model")
model_checkpoint = st.sidebar.radio("", model_list)

st.sidebar.header("Select generation parameters")
max_length = st.sidebar.slider("Max Length", value=20, min_value=10, max_value=200)
temperature = st.sidebar.slider("Temperature", value=1.0, min_value=0.0, max_value=1.0, step=0.05)
top_k = st.sidebar.slider("Top-k", min_value=0, max_value=15, step=1, value=0)
top_p = st.sidebar.slider("Top-p", min_value=0.0, max_value=1.0, step=0.05, value=0.9)

text_element = st.text_input('Text:', 'Acesta este un exemplu,')

@st.cache(allow_output_mutation=True)
def setModel(model_checkpoint):
    model = AutoModelForCausalLM.from_pretrained(model_checkpoint)
    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
    return model, tokenizer

def infer(model, tokenizer, text, max_length, temperature, top_k, top_p):
    encoded_prompt = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
    output_sequences = model.generate(
        input_ids=encoded_prompt.input_ids,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        do_sample=True,
        num_return_sequences=1
    )

    return output_sequences

model, tokenizer = setModel(model_checkpoint)
output_sequences = infer(model, tokenizer, text_element, max_length, temperature, top_k, top_p)

for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
    print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
    generated_sequences = generated_sequence.tolist()

    # Decode text
    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)

    # Remove all text after the stop token
    # text = text[: text.find(args.stop_token) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    total_sequence = (
            sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)):]
    )

    generated_sequences.append(total_sequence)
    print(total_sequence)

st.write(generated_sequences[-1], text_element)