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import os
import json
import traceback
import pandas as pd
from dotenv import load_dotenv
from utils import read_file
from utils import get_table_data
import streamlit as st
from langchain.callbacks import get_openai_callback
from mcqgenrator import generate_evaluate_chain
from mcqgenrator.logger import logging
#loading json file
with open("mcq-generation/Response.json", 'r') as file:
RESPONSE_JSON = json.load(file)
#creating a title for the app
st.title("🎯MCQ's Generator Application with LangChain 🐦📝🔗")
with st.form("user input"):
uploaded_file=st.file_uploader("upload pdf or text")
mcq_count=st.number_input("no of mcq's", min_value=3, max_value=50)
subject=st.text_input("Insert Subject",max_chars=20)
tone=st.text_input("Complexity Level Of Questions", max_chars=20, placeholder="Simple")
button=st.form_submit_button("Create MCQs")
if button and uploaded_file is not None and mcq_count and subject and tone:
with st.spinner("loading..."):
try:
text=read_file(uploaded_file)
#Count tokens and the cost of API call
with get_openai_callback() as cb:
response=generate_evaluate_chain(
{
"text": text,
"number": mcq_count,
"subject":subject,
"tone": tone,
"RESPONSE_JSON": json.dumps(RESPONSE_JSON)
}
)
#st.write(response)
except Exception as e:
traceback.print_exception(type(e), e, e.__traceback__)
st.error("Error")
else:
print(f"Total Tokens:{cb.total_tokens}")
print(f"Prompt Tokens:{cb.prompt_tokens}")
print(f"Completion Tokens:{cb.completion_tokens}")
print(f"Total Cost:{cb.total_cost}")
if isinstance(response, dict):
#Extract the quiz data from the response
quiz=response.get("quiz", None)
if quiz is not None:
table_data=get_table_data(quiz)
if table_data is not None:
df=pd.DataFrame(table_data)
df.index=df.index+1
st.table(df)
#Display the review in atext box as well
st.text_area(label="Review", value=response["review"])
else:
st.error("Error in the table data")
else:
st.write(response) |