Update MCQ_Gen.py

MCQ_Gen.py

@@ -1,156 +1,156 @@
-from fpdf import FPDF
-class PDF(FPDF):
-    def header(self):
-        self.set_font('Arial', 'B', 12)
-        self.cell(0, 10, 'MCQ Quiz', 0, 1, 'C')
-
-    def chapter_title(self, num, label):
-        self.set_font('Arial', '', 12)
-        self.cell(0, 10, 'Question %d: %s' % (num, label), 0, 1, 'L')
-        self.ln(5)
-
-    def chapter_body(self, body):
-        self.set_font('Arial', '', 12)
-        self.multi_cell(0, 10, body)
-        self.ln()
-
-    def add_question(self, num, question, options):
-        self.chapter_title(num, question)
-        for key, option in options.items():
-            self.chapter_body(f"{key}. {option}")
-        self.ln()
-
-    def add_answers_section(self, answers):
-        self.add_page()
-        self.set_font('Arial', 'B', 12)
-        self.cell(0, 10, 'Answers', 0, 1, 'C')
-        self.ln(10)
-        self.set_font('Arial', '', 12)
-        for num, answer in answers.items():
-            self.cell(0, 10, f"Question {num}: {answer}", 0, 1, 'L')
-
-
-
-import streamlit as st
-from dotenv import load_dotenv
-load_dotenv()
-import os
-import json
-import base64
-from langchain_google_genai import ChatGoogleGenerativeAI
-os.getenv("GOOGLE_API_KEY")
-RESPONSE_JSON = {
-    "1": {
-        "mcq": "multiple choice question",
-        "options": {
-            "a": "choice here",
-            "b": "choice here",
-            "c": "choice here",
-            "d": "choice here",
-        },
-        "correct": "correct answer",
-    },
-    "2": {
-        "mcq": "multiple choice question",
-        "options": {
-            "a": "choice here",
-            "b": "choice here",
-            "c": "choice here",
-            "d": "choice here",
-        },
-        "correct": "correct answer",
-    },
-    "3": {
-        "mcq": "multiple choice question",
-        "options": {
-            "a": "choice here",
-            "b": "choice here",
-            "c": "choice here",
-            "d": "choice here",
-        },
-        "correct": "correct answer",
-    },
-}
-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}
-
-"""
-
-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:
-"""
-def show():    
-    st.header("MCQ_Generator")
-    TEXT=st.text_input("Input Prompt: ",key="input1")
-    NUMBER=st.text_input("Number of MCQs ",key="input2")
-    SUBJECT=st.text_input("Topic of MCQs ",key="input3")
-    TONE=st.text_input("Difficulty Level ",key="input4")
-
-    submit=st.button("Submit")
-
-    if submit and TEXT:
-        llm = ChatGoogleGenerativeAI(model="gemini-pro",temperature=0.9)
-        from langchain.prompts import PromptTemplate
-        from langchain.chains import LLMChain
-        from langchain.chains import SequentialChain
-        quiz_generation_prompt = PromptTemplate(
-            input_variables=["text", "number", "subject", "tone", "response_json"],
-            template=TEMPLATE
-        )
-        quiz_chain=LLMChain(llm=llm, prompt=quiz_generation_prompt, output_key="quiz", verbose=True)
-        quiz_evaluation_prompt=PromptTemplate(input_variables=["subject", "quiz"], template=TEMPLATE)
-        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,)
-        response=generate_evaluate_chain(
-            {
-                "text": TEXT,
-                "number": NUMBER,
-                "subject":SUBJECT,
-                "tone": TONE,
-                "response_json": json.dumps(RESPONSE_JSON)
-            }
-        )
-        quiz=response.get("quiz")
-        if '### RESPONSE_JSON\n' in quiz:
-            quiz = quiz.split('### RESPONSE_JSON\n')[1]
-            quiz = json.loads(quiz)
-        else:
-            quiz=json.loads(quiz)
-        pdf = PDF()
-        pdf.add_page()
-        pdf.set_title(SUBJECT+" Quiz")
-        answers = {}
-        for key, value in quiz.items():
-            question_num = int(key)
-            pdf.add_question(question_num, value["mcq"], value["options"])
-            answers[question_num] = value["correct"]
-        pdf.add_answers_section(answers)
-
-        pdf_file_path =SUBJECT+"_mcq.pdf"
-        pdf.output(pdf_file_path)
-        
-        with open(pdf_file_path, "rb") as pdf_file:
-            st.download_button(
-                label="Download "+SUBJECT+" Quiz PDF",
-                data=pdf_file,
-                file_name=SUBJECT+"_quiz.pdf",
-                mime="application/pdf",
-            )
-
-        pdf_display = f'<iframe src="data:application/pdf;base64,{base64.b64encode(open(pdf_file_path, "rb").read()).decode()}" width="700" height="1000" type="application/pdf"></iframe>'
+from fpdf import FPDF
+class PDF(FPDF):
+    def header(self):
+        self.set_font('Arial', 'B', 12)
+        self.cell(0, 10, 'MCQ Quiz', 0, 1, 'C')
+
+    def chapter_title(self, num, label):
+        self.set_font('Arial', '', 12)
+        self.cell(0, 10, 'Question %d: %s' % (num, label), 0, 1, 'L')
+        self.ln(5)
+
+    def chapter_body(self, body):
+        self.set_font('Arial', '', 12)
+        self.multi_cell(0, 10, body)
+        self.ln()
+
+    def add_question(self, num, question, options):
+        self.chapter_title(num, question)
+        for key, option in options.items():
+            self.chapter_body(f"{key}. {option}")
+        self.ln()
+
+    def add_answers_section(self, answers):
+        self.add_page()
+        self.set_font('Arial', 'B', 12)
+        self.cell(0, 10, 'Answers', 0, 1, 'C')
+        self.ln(10)
+        self.set_font('Arial', '', 12)
+        for num, answer in answers.items():
+            self.cell(0, 10, f"Question {num}: {answer}", 0, 1, 'L')
+
+
+
+import streamlit as st
+from dotenv import load_dotenv
+load_dotenv()
+import os
+import json
+import base64
+from langchain_google_genai import ChatGoogleGenerativeAI
+os.getenv("GOOGLE_API_KEY")
+RESPONSE_JSON = {
+    "1": {
+        "mcq": "multiple choice question",
+        "options": {
+            "a": "choice here",
+            "b": "choice here",
+            "c": "choice here",
+            "d": "choice here",
+        },
+        "correct": "correct answer",
+    },
+    "2": {
+        "mcq": "multiple choice question",
+        "options": {
+            "a": "choice here",
+            "b": "choice here",
+            "c": "choice here",
+            "d": "choice here",
+        },
+        "correct": "correct answer",
+    },
+    "3": {
+        "mcq": "multiple choice question",
+        "options": {
+            "a": "choice here",
+            "b": "choice here",
+            "c": "choice here",
+            "d": "choice here",
+        },
+        "correct": "correct answer",
+    },
+}
+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}
+
+"""
+
+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:
+"""
+def show():    
+    st.header("MCQ_Generator")
+    TEXT=st.text_input("Input Prompt: ",key="input1")
+    NUMBER=st.text_input("Number of MCQs ",key="input2")
+    SUBJECT=st.text_input("Topic of MCQs ",key="input3")
+    TONE=st.text_input("Difficulty Level ",key="input4")
+
+    submit=st.button("Submit")
+
+    if submit and TEXT:
+        llm = ChatGoogleGenerativeAI(model="gemini-pro",temperature=0.9)
+        from langchain.prompts import PromptTemplate
+        from langchain.chains import LLMChain
+        from langchain.chains import SequentialChain
+        quiz_generation_prompt = PromptTemplate(
+            input_variables=["text", "number", "subject", "tone", "response_json"],
+            template=TEMPLATE
+        )
+        quiz_chain=LLMChain(llm=llm, prompt=quiz_generation_prompt, output_key="quiz", verbose=True)
+        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,)
+        response=generate_evaluate_chain(
+            {
+                "text": TEXT,
+                "number": NUMBER,
+                "subject":SUBJECT,
+                "tone": TONE,
+                "response_json": json.dumps(RESPONSE_JSON)
+            }
+        )
+        quiz=response.get("quiz")
+        if '### RESPONSE_JSON\n' in quiz:
+            quiz = quiz.split('### RESPONSE_JSON\n')[1]
+            quiz = json.loads(quiz)
+        else:
+            quiz=json.loads(quiz)
+        pdf = PDF()
+        pdf.add_page()
+        pdf.set_title(SUBJECT+" Quiz")
+        answers = {}
+        for key, value in quiz.items():
+            question_num = int(key)
+            pdf.add_question(question_num, value["mcq"], value["options"])
+            answers[question_num] = value["correct"]
+        pdf.add_answers_section(answers)
+
+        pdf_file_path =SUBJECT+"_mcq.pdf"
+        pdf.output(pdf_file_path)
+        
+        with open(pdf_file_path, "rb") as pdf_file:
+            st.download_button(
+                label="Download "+SUBJECT+" Quiz PDF",
+                data=pdf_file,
+                file_name=SUBJECT+"_quiz.pdf",
+                mime="application/pdf",
+            )
+
+        pdf_display = f'<iframe src="data:application/pdf;base64,{base64.b64encode(open(pdf_file_path, "rb").read()).decode()}" width="700" height="1000" type="application/pdf"></iframe>'
         st.markdown(pdf_display, unsafe_allow_html=True)