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| import streamlit as st | |
| from phi.assistant import Assistant | |
| from phi.document.reader.pdf import PDFReader | |
| from phi.utils.log import logger | |
| from assistant import get_groq_assistant | |
| import io | |
| import os | |
| # environment variables | |
| os.environ['GROQ_API_KEY'] = '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' | |
| st.set_page_config( | |
| page_title="Test Corrector Model" | |
| ) | |
| st.title("Test Corrector Model") | |
| st.markdown("##### Upload Model Answer and Student Answer PDFs to get the grades") | |
| def restart_assistant(): | |
| st.session_state["assistant"] = None | |
| st.session_state["assistant_run_id"] = None | |
| st.rerun() | |
| def main(): | |
| # Get LLM model | |
| llm_model = st.sidebar.selectbox("Select LLM", options=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768"]) | |
| embeddings_model = st.sidebar.selectbox("Select Embeddings", options=["nomic-embed-text", "text-embedding-3-small"]) | |
| if "llm_model" not in st.session_state: | |
| st.session_state["llm_model"] = llm_model | |
| elif st.session_state["llm_model"] != llm_model: | |
| st.session_state["llm_model"] = llm_model | |
| restart_assistant() | |
| if "embeddings_model" not in st.session_state: | |
| st.session_state["embeddings_model"] = embeddings_model | |
| elif st.session_state["embeddings_model"] != embeddings_model: | |
| st.session_state["embeddings_model"] = embeddings_model | |
| restart_assistant() | |
| #type annotation in Python. It indicates that the variable assistant is expected to be an instance of the Assistant class. | |
| assistant: Assistant | |
| if "assistant" not in st.session_state or st.session_state["assistant"] is None: | |
| logger.info(f"---*--- Creating {llm_model} Assistant ---*---") | |
| assistant = get_groq_assistant(llm_model=llm_model, embeddings_model=embeddings_model) | |
| st.session_state["assistant"] = assistant | |
| else: | |
| assistant = st.session_state["assistant"] | |
| try: | |
| st.session_state["assistant_run_id"] = assistant.create_run() | |
| except Exception: | |
| st.warning("Could not create assistant, is the database running?") | |
| return | |
| # Upload model answer PDF | |
| model_answer_pdf = st.file_uploader("Upload Model Answer PDF", type="pdf") | |
| model_answers = [] | |
| if model_answer_pdf: | |
| reader = PDFReader() | |
| model_documents = reader.read(io.BytesIO(model_answer_pdf.read())) | |
| model_answers = [doc.content for doc in model_documents] | |
| # Upload student answer PDF | |
| student_answer_pdf = st.file_uploader("Upload Student Answer PDF", type="pdf") | |
| student_answers = [] | |
| if student_answer_pdf: | |
| reader = PDFReader() | |
| student_documents = reader.read(io.BytesIO(student_answer_pdf.read())) | |
| student_answers = [doc.content for doc in student_documents] | |
| # Grade answers | |
| if st.button("Grade Answers"): | |
| if model_answers and student_answers: | |
| grades = [] | |
| # for model_answer, student_answer in zip(model_answers, student_answers): | |
| prompt = f"Grade the following student answer based on the model answer:\n\nModel Answer: {[doc.content for doc in model_documents]}\n\nStudent Answer: {[doc.content for doc in student_documents]}" | |
| response_generator = assistant.run(prompt) | |
| response = ''.join(list(response_generator)) | |
| grades.append(response) | |
| for i, grade in enumerate(grades, 1): | |
| st.write(f"{grade}") | |
| else: | |
| st.warning("Please upload both Model Answer PDF and Student Answer PDF") | |
| main() |