quiz_gen_new3.py

quiz_gen_new3.py

@@ -0,0 +1,124 @@
+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"))