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 = 'pszemraj/long-t5-tglobal-base-16384-book-summary'
+    question_mod_name = 'mrm8488/t5-base-finetuned-question-generation-ap'
     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")
@@ -112,7 +112,6 @@ if raw_text != None and raw_text != '':
             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,\
@@ -121,5 +120,4 @@ if raw_text != None and raw_text != '':
         "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"))
+    # st.dataframe(pd.read_csv(output_path,sep="\t").tail(5))