Delete quiz_gen_new3.py

quiz_gen_new3.py

@@ -1,124 +0,0 @@
-import streamlit as st
-from textwrap3 import wrap
-from flashtext import KeywordProcessor
-import torch, random, nltk, string, traceback, sys, os, requests, datetime
-import numpy as np
-import pandas as pd
-from transformers import T5ForConditionalGeneration,T5Tokenizer
-import pke
-from helper import postprocesstext, summarizer, get_nouns_multipartite, get_keywords,\
-    get_question, get_related_word, get_final_option_list, load_raw_text
-
-
-def set_seed(seed: int):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-set_seed(42)
-
-@st.cache(allow_output_mutation = True)
-def load_model():
-    nltk.download('punkt')
-    nltk.download('brown')
-    nltk.download('wordnet')
-    nltk.download('stopwords')
-    nltk.download('wordnet')
-    nltk.download('omw-1.4')
-    summary_mod_name = os.environ["summary_mod_name"]
-    question_mod_name = os.environ["question_mod_name"]
-    summary_model = T5ForConditionalGeneration.from_pretrained(summary_mod_name)
-    summary_tokenizer = T5Tokenizer.from_pretrained(summary_mod_name)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    summary_model = summary_model.to(device)
-    question_model = T5ForConditionalGeneration.from_pretrained(question_mod_name)
-    question_tokenizer = T5Tokenizer.from_pretrained(question_mod_name)
-    question_model = question_model.to(device)
-    return summary_model, summary_tokenizer, question_tokenizer, question_model 
-
-from nltk.corpus import wordnet as wn
-from nltk.tokenize import sent_tokenize
-from nltk.corpus import stopwords
-
-def csv_downloader(df):
-    res = df.to_csv(index=False,sep="\t").encode('utf-8')
-    st.download_button(
-    label="Download logs data as CSV separated by tab",
-    data=res,
-    file_name='df_quiz_log_file_v1.csv',
-    mime='text/csv')
-
-def load_file():
-    """Load text from file"""
-    uploaded_file = st.file_uploader("Upload Files",type=['txt'])
-    if uploaded_file is not None:
-        if uploaded_file.type == "text/plain":
-            raw_text = str(uploaded_file.read(),"utf-8")
-        return raw_text
-
-st.markdown('![Visitor count](https://shields-io-visitor-counter.herokuapp.com/badge?page=https://share.streamlit.io/https://huggingface.co/spaces/aakashgoel12/getmcq&label=VisitorsCount&labelColor=000000&logo=GitHub&logoColor=FFFFFF&color=1D70B8&style=for-the-badge)')
-
-# Loading Model
-summary_model, summary_tokenizer, question_tokenizer, question_model =load_model()
-
-# App title and description
-st.title("Exam Assistant")
-st.write("Upload text, Get ready for answering autogenerated questions")
-
-# Load file
-st.text("Disclaimer: This app stores user's input for model improvement purposes !!")
-
-# Load file
-
-default_text = load_raw_text()
-raw_text = st.text_area("Enter text here", default_text, height=250, max_chars=1000000, )
-
-# raw_text = load_file()
-start_time = str(datetime.datetime.now())
-if raw_text != None and raw_text != '':
-    summary_text = summarizer(raw_text,summary_model,summary_tokenizer)
-    ans_list =  get_keywords(raw_text,summary_text)
-    #print("Ans list: {}".format(ans_list))
-    questions = []
-    option1=[]
-    option2=[]
-    option3=[]
-    option4=[]
-    for idx,ans in enumerate(ans_list):
-        #print("IDX: {}, ANS: {}".format(idx, ans))
-        ques = get_question(summary_text,ans,question_model,question_tokenizer)
-        other_options = get_related_word(ans)
-        final_options, ans_index = get_final_option_list(ans,other_options)
-        option1.append(final_options[0])
-        option2.append(final_options[1])
-        option3.append(final_options[2])
-        option4.append(final_options[3])                   
-        if ques not in questions:
-            html_str = f"""
-            <div>
-            <p>
-            {idx+1}: <b> {ques} </b>
-            </p>
-            </div>
-            """
-            html_str += f' <p style="color:Green;"><b> {final_options[0]} </b></p> ' if ans_index == 0 else f' <p><b> {final_options[0]} </b></p> '
-            html_str += f' <p style="color:Green;"><b> {final_options[1]} </b></p> ' if ans_index == 1 else f' <p><b> {final_options[1]} </b></p> '
-            html_str += f' <p style="color:Green;"><b> {final_options[2]} </b></p> ' if ans_index == 2 else f' <p><b> {final_options[2]} </b></p> '
-            html_str += f' <p style="color:Green;"><b> {final_options[3]} </b></p> ' if ans_index == 3 else f' <p><b> {final_options[3]} </b></p> '
-            html_str += f"""
-            """
-            st.markdown(html_str , unsafe_allow_html=True)
-            st.markdown("-----")
-        questions.append(ques)
-    output_path = "results/df_quiz_log_file_v1.csv"
-    res_df = pd.DataFrame({"TimeStamp":[start_time]*len(ans_list),\
-        "Input":[str(raw_text)]*len(ans_list),\
-        "Question":questions,"Option1":option1,\
-        "Option2":option2,\
-        "Option3":option3,\
-        "Option4":option4,\
-        "Correct Answer":ans_list})
-    res_df.to_csv(output_path, mode='a', index=False, sep="\t", header= not os.path.exists(output_path))
-    # st.dataframe(pd.read_csv(output_path,sep="\t").tail(5))
-    csv_downloader(pd.read_csv(output_path,sep="\t"))