add webdemo app using streamlit

app.py

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+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")