app.py

from logging import PlaceHolder
from re import sub
import streamlit as st
import imp, time, random
import base64
import io
import nbformat
from PIL import Image
from datasets import load_from_disk, load_dataset
import os
from transformers import pipeline


st.set_page_config(layout="wide")

def set_submitted_true():
    st.session_state.submitted = True

st.markdown("""
<style>
input, .rtl {
  unicode-bidi:bidi-override;
  direction: RTL;
}
textarea, .rtl {
  unicode-bidi:bidi-override;
  direction: RTL;
}
h2, .rtl {
  unicode-bidi:bidi-override;
  direction: RTL;
}
div[role=tablist], .rtl {
  unicode-bidi:bidi-override;
  direction: RTL;
}
div[role=alert], .rtl {
  unicode-bidi:bidi-override;
  direction: RTL;
}
</style>
    """, unsafe_allow_html=True)

latest_iteration = st.empty()
bar = st.progress(0)


st.markdown("## سیستم پرسش و پاسخ فارسی")
st.markdown("")

tab1, tab2 = st.tabs(["دمو", "مستندات"])


datasets_names_addresses = {"small-persian-QA": "Hamid-reza/small-persian-QA",
                            "addsent-small-persian-QA": "Hamid-reza/Adv-small-persian-QA",
                            "addany-small-persian-QA": "mohammadhossein/addany-dataset",
                            "back-translation-small-persian-QA": "jalalnb/back_translation_hy_on_small_persian_QA",
                            "invisible-char-small-persian-QA": "jalalnb/invisible_char_on_small_persian_QA"}

@st.cache(allow_output_mutation=True)
def load_datasets(datasets_names_addresses):
    return {dataset_name: load_dataset(dataset_address)["validation"]
            for dataset_name, dataset_address in datasets_names_addresses.items()}

datasets_names_content = load_datasets(datasets_names_addresses)

selected_dataset_name = st.sidebar.radio(
    ':دیتاست مورد نظر خود را انتخاب نمایید',
     list(datasets_names_addresses.keys()))
selected_dataset = datasets_names_content[selected_dataset_name]


models_names_addresses = {"mbert": ("arashmarioriyad/mbert_v3", "arashmarioriyad/mbert_tokenizer_v3"),
                          "parsbert": ("arashmarioriyad/parsbert_v1", "arashmarioriyad/parsbert_tokenizer_v1"),
                          "addsent-mbert": ("arashmarioriyad/addsent_mbert_v1", "arashmarioriyad/addsent_mbert_tokenizer_v1"),
                          "addsent-parsbert": ("arashmarioriyad/addsent_parsbert_v1", "arashmarioriyad/addsent_parsbert_tokenizer_v1"),
                          "addany-mbert": ("arashmarioriyad/addany_mbert_v1", "arashmarioriyad/addany_mbert_tokenizer_v1"),
                          "addany-parsbert": ("arashmarioriyad/addany_parsbert_v1", "arashmarioriyad/addany_parsbert_tokenizer_v1"),
                          "back-translation-mbert": ("arashmarioriyad/bt_hy_mbert_v1", "arashmarioriyad/bt_hy_mbert_tokenizer_v1"),
                          "back-translation-parsbert": ("arashmarioriyad/bt_hy_parsbert_v1", "arashmarioriyad/bt_hy_parsbert_tokenizer_v1"),
                          "invisible-char-mbert": ("arashmarioriyad/ic_mbert_v1", "arashmarioriyad/ic_mbert_tokenizer_v1"),
                          "invisible-char-parsbert": ("arashmarioriyad/ic_parsbert_v1", "arashmarioriyad/ic_parsbert_tokenizer_v1")}

@st.cache(allow_output_mutation=True)
def load_models(models_names_addresses):
    return {model_name: pipeline("question-answering",
                                 model=models_names_addresses[model_name][0],
                                 tokenizer=models_names_addresses[model_name][1])
            for model_name, model_address in models_names_addresses.items()}

models_names_contents = load_models(models_names_addresses)

selected_model_name = st.sidebar.radio(
    ':مدل مورد نظر خود را انتخاب نمایید',
     list(models_names_addresses.keys()))
selected_model = models_names_contents[selected_model_name]


st.sidebar.info("تمامی دادگان، کد ها و نتایج ارزیابی مدل ها در [صفحه گیت هاب پروژه](https://github.com/NLP-Final-Projects/Adversarial-QA/) قابل دسترسی است", icon="ℹ️")



with tab1.form("my_form", clear_on_submit=False):

    col1, col2, col3 = st.columns(3)
    with col1:
        generate_random_data = st.form_submit_button("تولید داده‌ی تصادفی")
        if generate_random_data:
            sample_idx = random.randrange(len(selected_dataset))
            st.session_state.context = selected_dataset[sample_idx]["context"]
            st.session_state.question = selected_dataset[sample_idx]["question"]

    if 'context' in st.session_state and st.session_state.context is not None:
        context = st.text_area(label="Context", key="context", height=300, value=st.session_state.context)
        question = st.text_input(label="Question", key="question", value=st.session_state.question)
    else:
        context = st.text_area(label="Context", height=300, placeholder="متن مورد نظر را اینجا وارد کنید ...")
        question = st.text_input(label="Question", placeholder="سوال خود از متن را اینجا بپرسید  ...")

    submitted = st.form_submit_button("Get Answer")
    if submitted or ('submitted' in st.session_state and st.session_state.submitted):
        st.session_state.submitted = False
        selected_prediction = selected_model(question=question, context=context)["answer"]
        st.text_area(label=f"Answer ({selected_model_name}):", value=selected_prediction if selected_prediction!="" else "بدون پاسخ")