StreamlitAPP.py

# We use streamlit to build the fastest application and we use langchain to generate the MCQs and evaluate the MCQs
# We don't want to create API by using jango and flask , so we use streamlit to build the application and we use langchain to generate the MCQs and evaluate the MCQs

import os
import json
import traceback
import pandas as pd
#importing necessary packages


from dotenv import load_dotenv                               # load_dotenv = to load .env file
from src.mcqgenerator.utils import read_file,get_table_data  # from utils = to import read_file,get_table_data
import streamlit as st

from langchain_community.callbacks.manager import get_openai_callback           # get_openai_callback = to import get_openai_callback

from src.mcqgenerator.MCQGenerator import generate_evaluate_chain    # from MCQGenerator = to import generate_evaluate_chain
from src.mcqgenerator.logger import logging             # from logger = to import logging

with open('/Users/rajkumarrawal/Documents/GitHub/MCQGEN/Response.json', 'r') as file:
    RESPONSE_JSON = json.load(file)

#creating a title for the application 
st.title("MCQs Creator Application with LangChain")  # st is the streamlit library   # st.title = to create a title for the application . 

# Creating a form using st.form
# Inside form we create the template
with st.form("user_inputs"):           # st.form = to create a form
    #File Upload
    uploaded_file=st.file_uploader("Upload a PDF or txt file")

    #Input Fields
    mcq_count=st.number_input("No. of MCQs", min_value=3, max_value=50)

    #Subject
    subject=st.text_input("Insert Subject",max_chars=20)

    # Quiz Tones
    tone=st.text_input("Complexity Level of Questions", max_chars=20, placeholder="Simple")

    #Add Button
    button=st.form_submit_button("Create MCQs")

    #Check if the button is clicked and all fields have input

    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:                # if the file is not pdf or txt then it will raise an exception
                traceback.print_exception(type(e), e, e.__traceback__)
                st.error("Error")
            
            else:
                print(f"Total Tokens:{cb.total_tokens}")         # print the total tokens
                print(f"Prompt Tokens:{cb.prompt_tokens}")         # print the prompt tokens
                print(f"Completion Tokens:{cb.completion_tokens}") # print the completion tokens
                print(f"Total Cost:{cb.total_cost}")                # print the total cost
                if isinstance(response, dict):
                    #Extract the quiz 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)