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b.py

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+import streamlit as st
+from transformers import pipeline
+from PyPDF2 import PdfReader
+import PyPDF2
+import fitz
+import os
+import nltk
+
+
+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
+
+def main():
+    st.title('Question Generator from PDFs')
+    pipe = pipeline(
+        task = 'text2text-generation',
+        model = 'ramsrigouthamg/t5_squad_v1'
+    )
+    file = st.file_uploader(label='Upload',accept_multiple_files=True)
+    pr = st.button(label='Start')
+    if pr:
+        st.write('Hi')
+        raw_text = get_pdf_text(file)
+        sentences = nltk.sent_tokenize(text=raw_text)
+        # st.write(sts)
+        # for i in sentences:
+        #     st.write(i)
+        questions = []
+        st.subheader("Generated Questions are: ")
+        s = pipe(sentences)
+        for i in s:
+            questions.append(i['generated_text'][10:])
+            st.write(i['generated_text'][10:])
+        if st.toggle(label='Show Pipeline Output'):
+            st.write(s)
+        if st.toggle(label='Show Questions list'):
+            st.write(questions)
+        # for i in sts:
+        #     x = pipe(i)
+        #     questions.append(x)
+        #     st.write(x)
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+langchain==0.0.184
+PyPDF2==3.0.1
+python-dotenv==1.0.0
+streamlit==1.18.1
+faiss-cpu==1.7.4
+altair==4
+tiktoken==0.4.0
+# uncomment to use huggingface llms
+huggingface-hub==0.14.1
+
+# uncomment to use instructor embeddings
+InstructorEmbedding==1.0.1
+sentence-transformers==2.2.2