Update app.py

app.py

@@ -28,8 +28,8 @@ def load_model():
     nltk.download('omw-1.4')
     ## summary_mod_name = os.environ["summary_mod_name"]
     ## question_mod_name = os.environ["question_mod_name"]
-    summary_mod_name = "t5-small"
-    question_mod_name= "t5-small"
+    summary_mod_name = "t5-large"
+    question_mod_name = "t5-large"
     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")
@@ -43,6 +43,13 @@ 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"""
@@ -52,18 +59,21 @@ def load_file():
             raw_text = str(uploaded_file.read(),"utf-8")
         return raw_text
 
+
 # Loading Model
-summary_model, summary_tokenizer, question_tokenizer, question_model = load_model()
+summary_model, summary_tokenizer, question_tokenizer, question_model =load_model()
 
 # App title and description
-st.title("P's Prototype")
-st.write("Upload text, get exam")
+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("load text", default_text, height=250, max_chars=1000000, )
+raw_text = st.text_area("Enter text here - press Ctrl + enter to submit", height=250, max_chars=1000000, )
 
 # raw_text = load_file()
 start_time = str(datetime.datetime.now())
@@ -101,4 +111,15 @@ if raw_text != None and raw_text != '':
             """
             st.markdown(html_str , unsafe_allow_html=True)
             st.markdown("-----")
-        questions.append(ques)
+        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"))