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import streamlit as st |
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from streamlit_chat import message |
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from streamlit_extras.colored_header import colored_header |
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from streamlit_extras.add_vertical_space import add_vertical_space |
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from langchain import PromptTemplate, HuggingFaceHub, LLMChain |
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from dotenv import load_dotenv |
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pip install streamlit-chat |
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load_dotenv() |
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st.set_page_config(page_title="OpenAssistant Powered Quiz app") |
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with st.sidebar: |
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st.title('🤗💬 HuggingQuiz App') |
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st.markdown(''' |
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## About |
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This app is an LLM-powered chatbot built using: |
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- [Streamlit](https://streamlit.io/) |
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- [LangChain](https://python.langchain.com/) |
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- [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) LLM model |
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''') |
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add_vertical_space(3) |
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st.write('Made with ❤️ by [ninTea](https://github.com/NandiniTandon21)') |
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def main(): |
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st.title("Quiz App") |
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st.write("This app generates a quiz based on a given context.") |
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def chain_setup(): |
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"""Create the prompt template for the quiz app.""" |
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template = """<|im_end|> |
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You are an expert quiz maker for technical fields. Let's think step by step and |
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create a quiz with {num_questions} {quiz_type} questions about the following concept/content: {quiz_context}. |
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The format of the quiz could be one of the following: |
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- Multiple-choice: |
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- Questions: |
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<Question1>: <a. Answer 1>, <b. Answer 2>, <c. Answer 3>, <d. Answer 4> |
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<Question2>: <a. Answer 1>, <b. Answer 2>, <c. Answer 3>, <d. Answer 4> |
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.... |
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- Answers: |
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<Answer1>: <a|b|c|d> |
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<Answer2>: <a|b|c|d> |
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.... |
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Example: |
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- Questions: |
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- 1. What is the time complexity of a binary search tree? |
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a. O(n) |
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b. O(log n) |
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c. O(n^2) |
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d. O(1) |
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- Answers: |
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1. b |
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- True-false: |
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- Questions: |
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<Question1>: <True|False> |
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<Question2>: <True|False> |
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..... |
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- Answers: |
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<Answer1>: <True|False> |
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<Answer2>: <True|False> |
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..... |
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Example: |
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- Questions: |
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- 1. What is a binary search tree? |
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- 2. How are binary search trees implemented? |
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- Answers: |
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- 1. True |
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- 2. False |
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- Open-ended: |
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- Questions: |
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<Question1>: |
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<Question2>: |
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- Answers: |
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<Answer1>: |
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<Answer2>: |
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Example: |
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Questions: |
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- 1. What is a binary search tree? |
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- 2. How are binary search trees implemented? |
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- Answers: |
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1. A binary search tree is a data structure that is used to store data in a sorted manner. |
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2. Binary search trees are implemented using linked lists. |
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<|im_end|>""" |
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prompt = PromptTemplate(template=template, input_variables=["question"]) |
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prompt.format(num_questions=3, quiz_type="multiple-choice", quiz_context="Data Structures in Python Programming") |
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return prompt |
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llm=HuggingFaceHub(repo_id="OpenAssistant/llama2-70b-oasst-sft-v10", model_kwargs={"max_new_tokens":1200}) |
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llm_chain=LLMChain( |
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llm=llm, |
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prompt=prompt |
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) |
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return llm_chain |
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def generate_response(question, llm_chain): |
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response = llm_chain.run(question) |
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return response |
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llm_chain = chain_setup() |
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def split_questions_answers(quiz_response): |
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"""Function that splits the questions and answers from the quiz response.""" |
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questions = quiz_response.split("Answers:")[0] |
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answers = quiz_response.split("Answers:")[1] |
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return questions, answers |
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context = st.text_area("Enter the concept/context for the quiz") |
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num_questions = st.number_input("Enter the number of questions",min_value=1,max_value=10,value=3) |
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quiz_type = st.selectbox("Select the quiz type",["multiple-choice","true-false", "open-ended"]) |
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if st.button("Generate Quiz"): |
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quiz_response = llm_chain.invoke({"quiz_type":quiz_type,"num_questions":num_questions,"quiz_context":context}) |
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st.write("Quiz Generated!") |
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questions,answers = split_questions_answers(quiz_response) |
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st.session_state.answers = answers |
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st.session_state.questions = questions |
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st.write(questions) |
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if st.button("Show Answers"): |
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st.markdown(st.session_state.questions) |
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st.write("----") |
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st.markdown(st.session_state.answers) |
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if __name__=="__main__": |
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main() |
