Update app.py

app.py

@@ -1,77 +1,78 @@
-import streamlit as st
-import streamlit as st
-from pprint import pprint
-import subprocess
-cmd = ["python", "-m", "spacy", "download", "en_core_web_sm"]
-subprocess.run(cmd)
-from spacy.cli import download
-from Questgen import main
-from PyPDF2 import PdfReader
-from transformers import pipeline
-from PyPDF2 import PdfReader
-import nltk
-import pandas as pd
-nltk.download('punkt')
-# st.title(body='7 - Question Generation')
-
-
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-
-########################################################
-# st.header(body='Proposition 1',divider='red')
-
-# if st.toggle(label='Show Proposition 1'):
-st.title('Generate Questions from PDFs')
-file = st.file_uploader(label='Upload',accept_multiple_files=True)
-pr = st.button(label='Process')
-if pr:
-    # pr = st.button(label='Process')
-    raw_text = get_pdf_text(file)
-    # questions = []
-    ge = main.QGen()
-    payload = {
-        'input_text' : raw_text,
-        # 'max_questions':2,
-    }
-    output = ge.predict_mcq(payload=payload)
-    st.header(body='*Generated Questions are:*', divider='orange')
-    for question in output['questions']:
-        st.subheader(body=f":orange[Q{question['id']}:] {question['question_statement']}", divider='blue')
-        st.markdown(f"A: {question['answer']}")
-        c = 0
-        for option in question['options']:
-            # st.markdown(f"{c}")
-            c+=1
-            if c==1:
-                st.markdown(f"B: {option}")
-            elif c==2:
-                st.markdown(f"C: {option}")
-            elif c==3:
-                st.markdown(f"D: {option}")
-
-
-    if output is not None:
-        # Convert the dictionary to a DataFrame
-        df = pd.DataFrame(output['questions'])
-
-        # Convert the options from lists to strings
-        # df['options'] = df['options'].apply(lambda x: ','.join(x))
-        df = df.drop(labels=['options_algorithm','extra_options','context','question_type'],axis=1)
-        # Convert the DataFrame to CSV
-        csv = df.to_csv(index=False).encode('utf-8')
-        st.download_button(
-            label='Download Data',
-            data=csv,
-            file_name='Generated MCQs.csv',
-            mime='text/csv'
-        )
-
-    if st.toggle(label='Show Raw Output'):
+import streamlit as st
+import streamlit as st
+from pprint import pprint
+import subprocess
+cmd = ["python", "-m", "spacy", "download", "en_core_web_sm"]
+subprocess.run(cmd)
+from spacy.cli import download
+from Questgen import main, main2
+from PyPDF2 import PdfReader
+from transformers import pipeline
+from PyPDF2 import PdfReader
+import nltk
+import pandas as pd
+nltk.download('punkt')
+# st.title(body='7 - Question Generation')
+
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+########################################################
+# st.header(body='Proposition 1',divider='red')
+
+# if st.toggle(label='Show Proposition 1'):
+st.title('Generate Questions from PDFs')
+file = st.file_uploader(label='Upload',accept_multiple_files=True)
+pr = st.button(label='Process')
+if pr:
+    # pr = st.button(label='Process')
+    raw_text = get_pdf_text(file)
+    # questions = []
+    # ge = main.QGen()
+    ge = main2.QGen()
+    payload = {
+        'input_text' : raw_text,
+        # 'max_questions':2,
+    }
+    output = ge.predict_mcq(payload=payload)
+    st.header(body='*Generated Questions are:*', divider='orange')
+    for question in output['questions']:
+        st.subheader(body=f":orange[Q{question['id']}:] {question['question_statement']}", divider='blue')
+        st.markdown(f"A: {question['answer']}")
+        c = 0
+        for option in question['options']:
+            # st.markdown(f"{c}")
+            c+=1
+            if c==1:
+                st.markdown(f"B: {option}")
+            elif c==2:
+                st.markdown(f"C: {option}")
+            elif c==3:
+                st.markdown(f"D: {option}")
+
+
+    if output is not None:
+        # Convert the dictionary to a DataFrame
+        df = pd.DataFrame(output['questions'])
+
+        # Convert the options from lists to strings
+        # df['options'] = df['options'].apply(lambda x: ','.join(x))
+        df = df.drop(labels=['options_algorithm','extra_options','context','question_type'],axis=1)
+        # Convert the DataFrame to CSV
+        csv = df.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label='Download Data',
+            data=csv,
+            file_name='Generated MCQs.csv',
+            mime='text/csv'
+        )
+
+    if st.toggle(label='Show Raw Output'):
         st.write(output)