app.py

# -*- coding: utf-8 -*-

import streamlit as st
import pandas as pd
import torch

from utils import (
    load_model,
    load_tokenizer,
    make_input_sentence_from_strings,
    generate_description,
)

st.set_page_config(
    page_title="Table-to-text generation",
    page_icon="📝",
    layout="wide",
    initial_sidebar_state="auto",
    menu_items={
        "Get Help": "https://huggingface.co/transformers/master/index.html",
        "Report a bug": "https://github.com",
    },  # hide the "Made with Streamlit" footer
)

st.title("Table-to-text generation with multilingual pre-trained models")
st.markdown(
    """
This is a demo of table-to-text generation with multilingual pre-trained models.
The models are trained on our custom dataset, which is sampling from Viettel Report Template and generated description by ChatGPT.
"""
)

st.sidebar.title("Settings")
model_name = st.sidebar.selectbox(
    "Model name",
    [
        "vinai/bartpho-syllable",
        "vinai/bartpho-syllable-base",
        "google/byt5-base",
        "google/byt5-small",
        "facebook/mbart-large-50",
    ],
)

if torch.cuda.is_available():
    device = "cuda" if st.sidebar.checkbox("Use GPU", False) else "cpu"
else:
    st.sidebar.checkbox("Use GPU", False, disabled=True)
    device = "cpu"
max_len = st.sidebar.slider("Max length", 32, 512, 256, 32)
beam_size = st.sidebar.slider("Beam size", 1, 10, 3, 1)

# create a text input box for each of the following item
# CHỈ TIÊU	ĐƠN VỊ	ĐIỀU KIỆN	KPI mục tiêu tháng	Tháng 9.2022	Đánh giá	T8.2022	So sánh T8.2022 Tăng giảm	T9.2021	So sánh T9.2021 Tăng giảm

objective_name = st.text_input("CHỈ TIÊU", "")
(unit_col, condition_col, kpi_target_col) = st.columns(3)
with unit_col:
    unit = st.text_input("ĐƠN VỊ", "")
with condition_col:
    condition = st.selectbox("ĐIỀU KIỆN", [">=", "<=", None])
with kpi_target_col:
    kpi_target = st.text_input("KPI mục tiêu tháng", "")

current_date_col, real_value_col, evaluation_col = st.columns(3)
with current_date_col:
    current_date = st.date_input(
        "Thời gian báo cáo", value=None, min_value=None, max_value=None, key=None
    )
    current_time = [int(x) for x in current_date.__str__().split("-")[:2]]
with real_value_col:
    real_value = st.text_input(f"T{current_time[1]}.{current_time[0]} thực tế", "")
with evaluation_col:
    evaluation_value = st.selectbox(
        "Đánh giá",
        ["Đạt", "Không đạt", "Theo dõi"],
        index=2 if (kpi_target == "" or condition is None) else 0,
    )
# current_time is in format [year, month, day]

previous_month = (
    [current_time[0], current_time[1] - 1]
    if current_time[1] > 1
    else [current_time[0] - 1, 12]
)

previous_year = [current_time[0] - 1, current_time[1]]

(
    previous_month_value_col,
    previous_month_compare_col,
    previous_year_value_col,
    previous_year_compare_col,
) = st.columns(4)
with previous_month_value_col:
    previous_month_value = st.text_input(
        f"T{previous_month[1]}.{previous_month[0]}", ""
    )
with previous_month_compare_col:
    previous_month_compare = st.text_input(
        f"So sánh T{previous_month[1]}.{previous_month[0]} Tăng giảm",
        float(real_value) - float(previous_month_value)
        if previous_month_value != ""
        else "",
        # disabled=True,
    )
with previous_year_value_col:
    previous_year_value = st.text_input(f"T{previous_year[1]}.{previous_year[0]}", "")
with previous_year_compare_col:
    previous_year_compare = st.text_input(
        f"So sánh T{previous_year[1]}.{previous_year[0]} Tăng giảm",
        float(real_value) - float(previous_year_value)
        if previous_year_value != ""
        else "",
        # disabled=True,
    )


data = {
    "CHỈ TIÊU": objective_name,
    "ĐƠN VỊ": unit,
    "ĐIỀU KIỆN": condition,
    "KPI mục tiêu tháng": kpi_target,
    "Đánh giá": evaluation_value,
    "Thời gian báo cáo": current_time,
    f"T{current_time[1]}.{current_time[0]} thực tế": real_value,
    "Previous month value key": f"T{previous_month[1]}.{previous_month[0]}",
    f"T{previous_month[1]}.{previous_month[0]}": previous_month_value,
    "Previous year value key": f"T{previous_year[1]}.{previous_year[0]}",
    f"T{previous_year[1]}.{previous_year[0]}": previous_year_value,
    "Previous month compare key": f"So sánh T{previous_month[1]}.{previous_month[0]} Tăng giảm",
    f"So sánh T{previous_month[1]}.{previous_month[0]} Tăng giảm": previous_month_compare,
    "Previous year compare key": f"So sánh T{previous_year[1]}.{previous_year[0]} Tăng giảm",
    f"So sánh T{previous_year[1]}.{previous_year[0]} Tăng giảm": previous_year_compare,
    "Previous month": previous_month,
    "Previous year": previous_year,
}

tokenizer = load_tokenizer(model_name)
model = load_model(model_name, device)

if st.button("Generate"):
    if objective_name == "":
        st.error("Please input objective name")
    elif unit == "":
        st.error("Please input unit")
    else:
        with st.spinner("Generating..."):
            input_string = make_input_sentence_from_strings(data)
            print(input_string)
            descriptions = generate_description(
                input_string, model, tokenizer, device, max_len, model_name, beam_size
            )

            st.success(descriptions)