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import os
import json
import pandas as pd
import traceback
import streamlit as st
from dotenv import load_dotenv
from src.mcqgenerator.utilis import read_file,get_table_data
from src.mcqgenerator.logger import logging
from src.mcqgenerator.mcqgenerator import generate_evaluate_chain
from langchain_community.callbacks import get_openai_callback
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.chains import SequentialChain
with open('Response.json', 'r') as file:
RESPONSE_JSON = json.load(file)
st.title("MCQs Creator Application with Langchain")
with st.form("user_inputs"):
uploader_file=st.file_uploader("Upload a PDF or Txt file")
mcq_count=st.text_input("No .of MCQS")
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 uploader_file is not None and mcq_count and subject and tone:
with st.spinner("Loading...."):
try:
text=read_file(uploader_file)
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)
}
)
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):
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)
st.text_area(label="Review",value=response["review"])
else:
st.error("Error in the table date")
else:
st.write(response)
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