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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() |