Create mcqgenrator.py

mcqgenrator.py

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+from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.chains import SequentialChain
+from langchain.callbacks import get_openai_callback
+import os
+import json
+import pandas as pd
+import traceback
+from dotenv import load_dotenv
+import PyPDF2
+
+
+load_dotenv()
+
+key=os.getenv("openai_key")
+print(key)
+
+llm=ChatOpenAI(openai_api_key=key,model_name="gpt-3.5-turbo",temperature=0.7)
+
+with open(r"D:\NLP_Specialization\Gen_AI\LangChain_LLM\mcq_gen_project-main\mcq_gen_project-main\Response.json","r") as f:
+    RESPONSE_JSON=json.load(f)
+
+print(RESPONSE_JSON)
+
+TEMPLATE="""
+Text:{text}
+You are an expert MCQ maker. Given the above text, it is your job to \
+create a quiz  of {number} multiple choice questions for {subject} students in {tone} tone. 
+Make sure the questions are not repeated and check all the questions to be conforming the text as well.
+Make sure to format your response like  RESPONSE_JSON below  and use it as a guide. \
+Ensure to make {number} MCQs
+### RESPONSE_JSON
+{RESPONSE_JSON}
+
+"""
+
+quiz_generation_prompt = PromptTemplate(
+    input_variables=["text", "number", "grade", "tone", "RESPONSE_JSON"],
+    template=TEMPLATE)
+
+
+quiz_chain=LLMChain(llm=llm, prompt=quiz_generation_prompt, output_key="quiz", verbose=True)
+
+
+TEMPLATE2="""
+You are an expert english grammarian and writer. Given a Multiple Choice Quiz for {subject} students.\
+You need to evaluate the complexity of the question and give a complete analysis of the quiz. Only use at max 50 words for complexity analysis. 
+if the quiz is not at per with the cognitive and analytical abilities of the students,\
+update the quiz questions which needs to be changed and change the tone such that it perfectly fits the student abilities
+Quiz_MCQs:
+{quiz}
+
+Check from an expert English Writer of the above quiz:
+"""
+quiz_evaluation_prompt=PromptTemplate(input_variables=["subject", "quiz"], template=TEMPLATE2)
+
+
+
+review_chain=LLMChain(llm=llm, prompt=quiz_evaluation_prompt, output_key="review", verbose=True)
+
+
+
+generate_evaluate_chain=SequentialChain(chains=[quiz_chain,review_chain],
+                                        input_variables=["text", "number", "subject", "tone", "RESPONSE_JSON"],
+                                        output_variables=["quiz", "review"], verbose=True)