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app.py

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+import streamlit as st
+import tempfile
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader
+from langchain.docstore.document import Document
+from langchain.chains.summarize import load_summarize_chain
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.llms import LlamaCpp
+from langchain.prompts import PromptTemplate
+import os
+import pandas as pd
+
+
+prompt_template_questions = """
+You are an expert in creating practice questions based on study material.
+Your goal is to prepare a student for their exam. You do this by asking questions about the text below:
+
+------------
+{text}
+------------
+
+Create questions that will prepare the student for their exam. Make sure not to lose any important information.
+
+QUESTIONS:
+"""
+
+PROMPT_QUESTIONS = PromptTemplate(template=prompt_template_questions, input_variables=["text"])
+
+refine_template_questions = """
+You are an expert in creating practice questions based on study material.
+Your goal is to help a student prepare for an exam.
+We have received some practice questions to a certain extent: {existing_answer}.
+We have the option to refine the existing questions or add new ones.
+(only if necessary) with some more context below.
+------------
+{text}
+------------
+
+Given the new context, refine the original questions in English.
+If the context is not helpful, please provide the original questions.
+
+QUESTIONS:
+"""
+
+REFINE_PROMPT_QUESTIONS = PromptTemplate(
+    input_variables=["existing_answer", "text"],
+    template=refine_template_questions,
+)
+
+# Initialize Streamlit app
+st.title('Question-Answer Pair Generator with Zephyr-7B')
+st.markdown('<style>h1{color: orange; text-align: center;}</style>', unsafe_allow_html=True)
+
+# File upload widget
+uploaded_file = st.sidebar.file_uploader("Upload a PDF file", type=["pdf"])
+
+# Set file path
+file_path = None
+
+# Check if a file is uploaded
+if uploaded_file:
+    # Save the uploaded file to a temporary location
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+        temp_file.write(uploaded_file.read())
+        file_path = temp_file.name
+
+# Check if file_path is set
+if file_path:
+    # Load data from the uploaded PDF
+    loader = PyPDFLoader(file_path)
+    data = loader.load()
+
+    # Combine text from Document into one string for question generation
+    text_question_gen = ''
+    for page in data:
+        text_question_gen += page.page_content
+    
+     # Initialize Text Splitter for question generation
+    text_splitter_question_gen = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=50)
+
+    # Split text into chunks for question generation
+    text_chunks_question_gen = text_splitter_question_gen.split_text(text_question_gen)
+
+    # Convert chunks into Documents for question generation
+    docs_question_gen = [Document(page_content=t) for t in text_chunks_question_gen]
+    
+    # Initialize Large Language Model for question generation
+    llm_question_gen = LlamaCpp(
+    streaming = True,
+    model_path="zephyr-7b-alpha.Q4_K_M.gguf",
+    temperature=0.75,
+    top_p=1, 
+    verbose=True,
+    n_ctx=4096
+    )
+
+      # Initialize question generation chain
+    question_gen_chain = load_summarize_chain(llm=llm_question_gen, chain_type="refine", verbose=True,
+                                              question_prompt=PROMPT_QUESTIONS, refine_prompt=REFINE_PROMPT_QUESTIONS)
+    # Run question generation chain
+    questions = question_gen_chain.run(docs_question_gen)
+
+    # Initialize Large Language Model for answer generation
+    llm_answer_gen = LlamaCpp(
+    streaming = True,
+    model_path="zephyr-7b-alpha.Q4_K_M.gguf",
+    temperature=0.75,
+    top_p=1, 
+    verbose=True,
+    n_ctx=4096)
+
+     # Create vector database for answer generation
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
+
+    # Initialize vector store for answer generation
+    vector_store = Chroma.from_documents(docs_question_gen, embeddings)
+
+    # Initialize retrieval chain for answer generation
+    answer_gen_chain = RetrievalQA.from_chain_type(llm=llm_answer_gen, chain_type="stuff",
+                                                   retriever=vector_store.as_retriever(k=2))
+    
+     # Split generated questions into a list of questions
+    question_list = questions.split("\n")
+
+    # Answer each question and save to a file
+    question_answer_pairs = []
+
+    for question in question_list:
+        st.write("Question: ", question)
+        answer = answer_gen_chain.run(question)
+        question_answer_pairs.append([question, answer])
+        st.write("Answer: ", answer)
+        st.write("--------------------------------------------------\n\n")
+    
+    # Create a directory for storing answers
+    answers_dir = os.path.join(tempfile.gettempdir(), "answers")
+    os.makedirs(answers_dir, exist_ok=True)
+
+    # Create a DataFrame from the list of question-answer pairs
+    qa_df = pd.DataFrame(question_answer_pairs, columns=["Question", "Answer"])
+
+    # Save the DataFrame to a CSV file
+    csv_file_path = os.path.join(answers_dir, "questions_and_answers.csv")
+    qa_df.to_csv(csv_file_path, index=False)
+
+    # Create a download button for the questions and answers CSV file
+    st.markdown('### Download Questions and Answers in CSV')
+    st.download_button("Download Questions and Answers (CSV)", csv_file_path)
+
+# Cleanup temporary files
+if file_path:
+    os.remove(file_path)
+

requirements.txt

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+langchain
+streamlit
+huggingface_hub
+Chromadb
+pypdf
+sentence-transformers
+torch
+accelerate
+llama-cpp-python
+