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app.py

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+import streamlit as st
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from dotenv import load_dotenv
+import os
+import time
+from langchain_cohere import CohereEmbeddings
+from langchain_community.llms import Cohere
+import asyncio
+from utils import speak
+import nest_asyncio
+from PIL import Image
+from langchain_groq import ChatGroq
+from langchain import LLMChain, PromptTemplate
+import google.generativeai as genai
+from utils import takeCommand
+from youtube_transcript_api import YouTubeTranscriptApi
+from utils import class_9_subjects , class_10_subjects , class_11_subjects , class_12_subjects
+import re
+from datetime import datetime
+nest_asyncio.apply()
+try:
+    loop = asyncio.get_running_loop()
+except RuntimeError:
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+
+
+st.markdown(
+    """
+    <style>
+    /* Background settings */
+    body {
+        background-color: #f7f9fc;
+        font-family: 'Open Sans', sans-serif;
+    }
+
+    /* Center the title */
+    .title {
+        text-align: center;
+        font-size: 3rem;
+        font-weight: bold;
+        color: #2A9D8F;
+        margin-bottom: 30px;
+    }
+
+    /* Style for header sections */
+    .header {
+        color: #ffffff;
+        font-size: 1.5rem;
+        font-weight: bold;
+        margin-top: 20px;
+    }
+
+    /* Custom buttons */
+    .stButton button {
+        background-color: #2A9D8F;
+        color: white;
+        border-radius: 10px;
+        border: none;
+        padding: 10px 20px;
+        font-size: 1.2rem;
+        cursor: pointer;
+    }
+
+    .stButton button:hover {
+        background-color: #21867a;
+    }
+
+    /* Styling file uploader */
+    .stFileUploader button {
+        background-color: #e76f51;
+        color: white;
+        border-radius: 8px;
+        font-size: 1rem;
+        padding: 10px 15px;
+        cursor: pointer;
+    }
+
+
+    /* Footer styles */
+    .footer {
+        position: fixed;
+        bottom: 0;
+        left: 0;
+        width: 100%;
+        background-color: #264653;
+        color: white;
+        text-align: center;
+        padding: 10px;
+        font-size: 1rem;
+        display: flex;
+        justify-content: center; /* Centers horizontally */
+        align-items: center; /* Centers vertically */
+    }
+
+    /* Image styling */
+    .uploaded-image {
+        border-radius: 15px;
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    }
+
+    /* Transcript text area */
+    .transcript {
+        background-color: #ffffff;
+        border: 1px solid #2A9D8F;
+        border-radius: 10px;
+        padding: 15px;
+        margin-top: 15px;
+        font-size: 1rem;
+        color: #333;
+        line-height: 1.5;
+    }
+    </style>
+    """, unsafe_allow_html=True
+)
+
+st.sidebar.markdown(
+    "<h2 style='text-align: center; color: white; font-weight: bold;'>WELCOME TO EDUAI</h2>",
+    unsafe_allow_html=True
+)
+
+st.sidebar.image("logo.png", width=160, use_column_width=False, output_format="auto", caption="Let AI Educate You")
+
+option = st.sidebar.selectbox(
+        "Choose an option:",
+        ["Get Solution from Image", "Chat with Your Book","Transcript Youtube Video ","Genrate Practice MCQ","Self Assesment"]
+    )
+
+
+if option=="Chat with Your Book":
+    st.markdown('<h1 class="title">Chat with Your Book</h1>', unsafe_allow_html=True)
+    load_dotenv()
+
+# Ensure the Google API key is retrieved correctly
+    #Cohere_API_KEY = os.getenv("COHERE_API_KEY")
+    Groq_API_KEY = os.getenv("GROQ_API_KEY")
+    llm=ChatGroq(groq_api_key=Groq_API_KEY,model_name="Llama3-8b-8192")
+
+# Initialize LLM with synchronous method
+    #llm =Cohere(model="command", temperature=0, cohere_api_key=Cohere_API_KEY)
+    class_options = [9, 10, 11, 12]
+    selected_class = st.selectbox("Select Your Class",["Select Class"]+class_options)
+    if selected_class==11:
+        st.markdown('<h2 class="header">Chatting with Class 11</h2>', unsafe_allow_html=True)
+    # Function to create vector embeddings
+        def vector_embedding():
+            if "vectors" not in st.session_state:
+                index_file = "faiss_index_11"
+                if os.path.exists(index_file):
+                    st.session_state.vectors = FAISS.load_local(index_file, CohereEmbeddings(model="multilingual-22-12"),allow_dangerous_deserialization=True)
+    
+
+        # Define prompt template
+        prompt = ChatPromptTemplate.from_template("""
+        Answer the questions based on the provided context only.
+        Please provide the most accurate response based on the question.
+        Provide only the answer, without any additional text.
+        <context>
+        {context}
+        <context>
+        Questions: {input}
+        """)
+
+        # Initialize session state for storing chat history
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+        
+        Sucess=''
+        
+        if "button_clicked_c11" not in st.session_state:
+            st.session_state.button_clicked_c11 = False
+
+        # Conditionally display the button only if it hasn't been clicked
+        if not st.session_state.button_clicked_c11:
+            # Button updates the session state directly in the same run
+            if st.button("Read My Book"):
+                vector_embedding()
+                Sucess="Book Read Successfully"
+                st.success(Sucess)
+                st.session_state.button_clicked_c11 = True
+
+        if st.session_state.button_clicked_c11:
+            Sucess=''
+            st.write(Sucess)
+
+
+        # Button to embed documents
+        #if st.button("Read My Book "):
+            #vector_embedding()
+            #st.success("Read Your Book Successfully")
+
+        
+        # Move the input box to the bottom of the page
+        st.write("-----")  # Add a separator
+
+        col1, col2 = st.columns([4, 1])
+        with col1:
+            prompt1 = st.chat_input("What you want to know?")
+        with col2:
+            Speak=st.button("Speak 🎙️")
+        if Speak:
+            st.write("please speak....")
+            prompt1=takeCommand()
+            st.write(f"You said: {prompt1}")
+        st.markdown('<div class="fixed-bottom">', unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+      
+        # Handle user input and AI response
+        if prompt1:
+            if "vectors" in st.session_state:
+                document_chain = create_stuff_documents_chain(llm, prompt)
+                retriever = st.session_state.vectors.as_retriever()
+                retrieval_chain = create_retrieval_chain(retriever, document_chain)
+                start = time.process_time()
+                response = retrieval_chain.invoke({'input': prompt1})
+                response_time = time.process_time() - start
+                
+                st.chat_message("user").markdown(prompt1)
+    # Add user message to chat history
+                st.session_state.messages.append({"role": "user", "content": prompt1})
+                st.write("Response Time:", response_time)
+                answer=response['answer']
+                response = f"Assistant: {answer}"
+    # Display assistant response in chat message container
+                with st.chat_message("assistant"):
+                    st.markdown(response)
+                # Add assistant response to chat history
+                st.session_state.messages.append({"role": "assistant", "content": response})
+                st.audio(speak(answer), format="audio/wav")
+            else:
+                st.error("Please let me read the book by clicking  'Read My Book'.")
+                
+       
+
+
+    if selected_class==10:
+        st.markdown('<h2 class="header">Chatting with Class 10</h2>', unsafe_allow_html=True)
+    # Function to create vector embeddings
+        def vector_embedding():
+            if "vectors" not in st.session_state:
+                index_file = "faiss_index_10"
+                if os.path.exists(index_file):
+                    st.session_state.vectors = FAISS.load_local(index_file, CohereEmbeddings(model="multilingual-22-12"),allow_dangerous_deserialization=True)
+    
+
+        # Define prompt template
+        prompt = ChatPromptTemplate.from_template("""
+        Answer the questions based on the provided context only.
+        Please provide the most accurate response based on the question.
+        Provide only the answer, without any additional text.
+        <context>
+        {context}
+        <context>
+        Questions: {input}
+        """)
+
+
+        # Initialize session state for storing chat history
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+        Sucess=''
+        if "button_clicked_c10" not in st.session_state:
+            st.session_state.button_clicked_c10 = False
+
+        # Conditionally display the button only if it hasn't been clicked
+        if not st.session_state.button_clicked_c10:
+            # Button updates the session state directly in the same run
+            if st.button("Read My Book"):
+                vector_embedding()
+                Sucess="Book Read Successfully"
+                st.success(Sucess)
+                st.session_state.button_clicked_c10 = True
+
+        if st.session_state.button_clicked_c10:
+            Sucess=''
+            st.write(Sucess)
+       
+
+ 
+        st.write("-----")  
+
+
+        col1, col2 = st.columns([4, 1])
+        with col1:
+            prompt1 = st.chat_input("What you want to know?")
+        with col2:
+            Speak=st.button("Speak 🎙️")
+        if Speak:
+            st.write("please speak....")
+            prompt1=takeCommand()
+            st.write(f"You said: {prompt1}")
+        st.markdown('<div class="fixed-bottom">', unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+      
+
+        if prompt1:
+            if "vectors" in st.session_state:
+                document_chain = create_stuff_documents_chain(llm, prompt)
+                retriever = st.session_state.vectors.as_retriever()
+                retrieval_chain = create_retrieval_chain(retriever, document_chain)
+                start = time.process_time()
+                response = retrieval_chain.invoke({'input': prompt1})
+                response_time = time.process_time() - start
+                
+                st.chat_message("user").markdown(prompt1)
+
+                st.session_state.messages.append({"role": "user", "content": prompt1})
+                st.write("Response Time:", response_time)
+                answer=response['answer']
+                response = f"Assistant: {answer}"
+
+                with st.chat_message("assistant"):
+                    st.markdown(response)
+                st.session_state.messages.append({"role": "assistant", "content": response})
+                st.audio(speak(answer), format="audio/wav")
+
+            else:
+                st.error("Please let me read the book by clicking  'Read My Book'.")
+
+    if selected_class==12:
+        st.markdown('<h2 class="header">Chatting with Class 12</h2>', unsafe_allow_html=True)
+
+        def vector_embedding():
+            if "vectors" not in st.session_state:
+                index_file = "faiss_index_12"
+                if os.path.exists(index_file):
+                    st.session_state.vectors = FAISS.load_local(index_file, CohereEmbeddings(model="multilingual-22-12"),allow_dangerous_deserialization=True)
+    
+
+  
+        prompt = ChatPromptTemplate.from_template("""
+        Answer the questions based on the provided context only.
+        Please provide the most accurate response based on the question.
+        Provide only the answer, without any additional text.
+        <context>
+        {context}
+        <context>
+        Questions: {input}
+        """)
+
+        
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        if "button_clicked_c12" not in st.session_state:
+            st.session_state.button_clicked_c12 = False
+
+       
+        Sucess=''
+        if not st.session_state.button_clicked_c12:
+         
+            if st.button("Read My Book"):
+                vector_embedding()
+                Sucess="Book Read Successfully"
+                st.success(Sucess)
+                st.session_state.button_clicked_c12 = True
+
+        if st.session_state.button_clicked_c12:
+            Sucess=''
+            st.write(Sucess)
+        st.write("-----") 
+
+        col1, col2 = st.columns([4, 1])
+        with col1:
+            prompt1 = st.chat_input("What you want to know?")
+        with col2:
+            Speak=st.button("Speak 🎙️")
+        if Speak:
+            st.write("please speak....")
+            prompt1=takeCommand()
+            st.write(f"You said: {prompt1}")
+        st.markdown('<div class="fixed-bottom">', unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+        if prompt1:
+            if "vectors" in st.session_state:
+                document_chain = create_stuff_documents_chain(llm, prompt)
+                retriever = st.session_state.vectors.as_retriever()
+                retrieval_chain = create_retrieval_chain(retriever, document_chain)
+                start = time.process_time()
+                response = retrieval_chain.invoke({'input': prompt1})
+                response_time = time.process_time() - start
+                
+                st.chat_message("user").markdown(prompt1)
+    # Add user message to chat history
+                st.session_state.messages.append({"role": "user", "content": prompt1})
+                st.write("Response Time:", response_time)
+                answer=response['answer']
+                response = f"Assistant: {answer}"
+    # Display assistant response in chat message container
+                with st.chat_message("assistant"):
+                    st.markdown(response)
+                st.session_state.messages.append({"role": "assistant", "content": response})
+                st.audio(speak(answer), format="audio/wav")
+
+            else:
+                st.error("Please let me read the book by clicking  'Read My Book'.")
+
+    if selected_class==9:
+        st.markdown('<h2 class="header">Chatting with Class 9</h2>', unsafe_allow_html=True)
+    # Function to create vector embeddings
+        def vector_embedding():
+            if "vectors" not in st.session_state:
+                index_file = "faiss_index_9"
+                if os.path.exists(index_file):
+                    st.session_state.vectors = FAISS.load_local(index_file, CohereEmbeddings(model="multilingual-22-12"),allow_dangerous_deserialization=True)
+    
+
+        # Define prompt template
+        prompt = ChatPromptTemplate.from_template("""
+        Answer the questions based on the provided context only.
+        Please provide the most accurate response based on the question.
+        Provide only the answer, without any additional text.
+        <context>
+        {context}
+        <context>
+        Questions: {input}
+        """)
+
+        # Initialize session state for storing chat history
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+
+        
+        if "button_clicked_c9" not in st.session_state:
+            st.session_state.button_clicked_c9 = False
+
+        # Conditionally display the button only if it hasn't been clicked
+        sucess=''
+        if not st.session_state.button_clicked_c9:
+            # Button updates the session state directly in the same run
+            if st.button("Read My Book"):
+                vector_embedding()
+                Sucess="Book Read Successfully"
+                st.success(Sucess)
+                st.session_state.button_clicked_c9 = True
+
+        if st.session_state.button_clicked_c9:
+            Sucess=''
+            st.write(Sucess)
+
+
+        # Move the input box to the bottom of the page
+        st.write("-----")  # Add a separator
+
+        # Input box for user to type in at the bottom
+        col1, col2 = st.columns([4, 1])
+        with col1:
+            prompt1 = st.chat_input("What you want to know?")
+        with col2:
+            Speak=st.button("Speak 🎙️")
+        if Speak:
+            st.write("please speak....")
+            prompt1=takeCommand()
+            st.write(f"You said: {prompt1}")
+        st.markdown('<div class="fixed-bottom">', unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+      
+        # Handle user input and AI response
+        if prompt1:
+            if "vectors" in st.session_state:
+                st.chat_message("user").markdown(prompt1)
+                document_chain = create_stuff_documents_chain(llm, prompt)
+                retriever = st.session_state.vectors.as_retriever()
+                retrieval_chain = create_retrieval_chain(retriever, document_chain)
+                start = time.process_time()
+                response = retrieval_chain.invoke({'input': prompt1})
+                response_time = time.process_time() - start
+
+    # Add user message to chat history
+                st.session_state.messages.append({"role": "user", "content": prompt1})
+                st.write("Response Time:", response_time)
+                answer=response['answer']
+                response = f"Assistant: {answer}"
+    # Display assistant response in chat message container
+                with st.chat_message("assistant"):
+                    st.markdown(response)
+                st.session_state.messages.append({"role": "assistant", "content": response})
+                st.audio(speak(answer), format="audio/wav")
+
+            else:
+                st.error("Please let me read the book by clicking  'Read My Book'.")
+
+
+
+
+elif option=="Get Solution from Image":
+    st.markdown('<h1 class="title">Get Solution from Image</h1>', unsafe_allow_html=True)
+    load_dotenv()
+    os.getenv("GOOGLE_API_KEY")
+    genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+    ## Function to load OpenAI model and get respones
+
+    def get_gemini_response(input,image):
+        model = genai.GenerativeModel('gemini-1.5-flash')
+        if input!="":
+            response = model.generate_content([input,image])
+        else:
+            response = model.generate_content(image)
+        return response.text
+
+    ##initialize our streamlit app
+
+  
+  
+    input = st.text_input("What you want to know?")
+    uploaded_file=None
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    image=""   
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image.", use_column_width=True)
+
+
+    submit=st.button("Provide Me A Solution")
+
+    ## If ask button is clicked
+
+    if submit:
+        
+        response=get_gemini_response(input,image)
+        st.subheader("The Response is")
+        st.write(response)
+
+
+elif option=="Transcript Youtube Video ":
+    st.markdown('<h1 class="title">Transcript Youtube Video </h1>', unsafe_allow_html=True)
+
+    prompt="""You are a highly knowledgeable AI specialized in text summarization for educational content creation.
+        Generate a summary of exactly {number} words based on the following text: "{text}".
+        Ensure the summary captures the key points and is concise and informative.
+        """
+
+    prompt_template = PromptTemplate(input_variables=["number","text"],template=prompt)
+## getting the transcript data from yt videos
+    def extract_transcript_details(youtube_video_url):
+        try:
+            video_id=youtube_video_url.split("=")[1]
+            
+            transcript_text=YouTubeTranscriptApi.get_transcript(video_id)
+
+            transcript = ""
+            for i in transcript_text:
+                transcript += " " + i["text"]
+
+            return transcript
+
+        except Exception as e:
+            raise e
+
+    def generate_gemini_content(prompt_template,number,text):
+
+        #model=genai.GenerativeModel("gemini-pro")
+        Groq_API_KEY = os.getenv("GROQ_API_KEY")
+        llm=ChatGroq(groq_api_key=Groq_API_KEY,model_name="Llama3-8b-8192")
+        chain =LLMChain(llm=llm, prompt=prompt_template, verbose=True)
+        response = chain.run({"number":number,"text":text})
+        return response
+
+
+    youtube_link = st.text_input("Enter YouTube Video Link:")
+    number = st.slider("Select the number of lines for the summary", 50, 1000, 50)
+    if youtube_link:
+        video_id = youtube_link.split("=")[1]
+        print(video_id)
+        st.image(f"http://img.youtube.com/vi/{video_id}/0.jpg", use_column_width=True)
+
+    if st.button("Get Detailed Notes"):
+        transcript_text=extract_transcript_details(youtube_link)
+
+        if transcript_text:
+            summary=generate_gemini_content(prompt_template,number,transcript_text)
+            st.markdown("## Detailed Notes:")
+            st.write(summary)
+            st.audio(speak(summary), format="audio/wav")
+   
+
+
+elif option=="Genrate Practice MCQ":
+    st.markdown('<h1 class="title">Genrate Practice MCQ</h1>', unsafe_allow_html=True)
+    load_dotenv()
+    Groq_API_KEY = os.getenv("GROQ_API_KEY")
+    llm = ChatGroq(groq_api_key=Groq_API_KEY, model_name="Llama3-8b-8192")
+    PROMPT_TEMPLATE_STRING = """
+    You are a highly knowledgeable AI specialized in educational content creation.
+    Generate {number} multiple-choice question(s) on the topic: {topic} with the following details:
+    - Difficulty level: {difficulty}
+    - Each question should be challenging and provide four answer choices, with only one correct answer.
+
+    Format each question as follows:
+    Question: [The generated question text]
+
+    A) [Answer choice 1]
+    B) [Answer choice 2]
+    C) [Answer choice 3]
+    D) [Answer choice 4]
+
+    Correct Answer: [The letter corresponding to the correct answer]
+
+    Make sure that the correct answer is clearly indicated.
+    """
+
+
+
+
+    # Create a PromptTemplate instance with the string template
+    prompt_template = PromptTemplate(input_variables=["number","topic","difficulty"],template=PROMPT_TEMPLATE_STRING)
+
+    # Input for the topic of the MCQ
+    topic = st.text_input("Enter the topic for the MCQ:")
+
+    # Select difficulty level
+    difficulty = st.selectbox("Select difficulty level:", ["easy", "medium", "hard"])
+    
+    number = st.selectbox("Select number of question :", [5, 10, 15 , 20])
+    
+
+    if st.button("Generate MCQ"):
+        if topic:
+            with st.spinner("Generating MCQ..."):
+                # Prepare the formatted prompt
+
+                # Initialize LLMChain with the prompt and LLM
+                mcq_chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)
+                mcq = mcq_chain.run({"number":number,"topic":topic,"difficulty":difficulty})
+                if mcq:
+                    st.write(mcq)
+                else:
+                    st.error("Failed to generate MCQ. Please try again.")
+        else:
+            st.error("Please enter a topic.")
+
+
+elif option=="Self Assesment" :
+    st.markdown('<h1 class="title">Self Assesment</h1>', unsafe_allow_html=True)
+    class_options = ['Class 9', 'Class 10', 'Class 11', 'Class 12']
+    selected_class = st.selectbox("Select Your Class", ['Select Class'] + class_options)
+    if selected_class == "Class 9":
+            subject_option = st.selectbox("Select Subject", ["Select"] + list(class_9_subjects.keys()))
+            if subject_option != "Select":
+                chapters = class_9_subjects.get(subject_option, [])
+                chapter_option = st.selectbox("Select Chapter", ["Select"] + chapters)
+                if chapter_option != "Select":
+                    st.write(f"You have selected: **{chapter_option}**")
+
+                    # Prompts
+                    PROMPT_TEMPLATE_STRING = """
+                    Based on the CBSE Class 9, generate a question in the form of a complete sentence:
+                                            Create a question about the following chapter: {Chapter}
+                                            
+                                            Provide only the question as the output, with no additional text. 
+                    """
+                    prompt_template = PromptTemplate(input_variables=["Chapter"], template=PROMPT_TEMPLATE_STRING)
+
+                    PROMPT_TEMPLATE_STRING2 = """
+                    Evaluate the provided answer based strictly on CBSE Class 9 standards, assigning marks out of 1.
+                    Deduct marks for any inaccuracies, even minor ones. If the final score is 0.6 or higher, round it up to 1. Provide only the marks as the output.
+
+                        Question: {input}
+                        Answer: {answer}
+
+                        Output the marks out of 1.
+                    """
+                    prompt_template2 = PromptTemplate(input_variables=["input", "answer"], template=PROMPT_TEMPLATE_STRING2)
+
+                    PROMPT_TEMPLATE_STRING3 = """
+                    Provide a clear and accurate answer to the following question based on CBSE Class 9 standards.
+
+                    Question: {question}
+
+                    Output the answer only.
+                    """
+                    prompt_template3 = PromptTemplate(input_variables=["question"], template=PROMPT_TEMPLATE_STRING3)
+
+                    load_dotenv()
+
+                    Groq_API_KEY = os.getenv("GROQ_API_KEY")
+                    llm = ChatGroq(groq_api_key=Groq_API_KEY, model_name="Llama3-8b-8192")
+                    prompt1 = chapter_option 
+                    num_questions = st.number_input("Enter the number of questions you want", min_value=1, max_value=15, step=1)
+                    
+                    # Initialize session state variables
+                    if "marks" not in st.session_state:
+                        st.session_state.marks = []
+                    if "questions" not in st.session_state:
+                        st.session_state.questions = []
+                    if "answers" not in st.session_state:
+                        st.session_state.answers = {}
+                    if "generated_questions" not in st.session_state:
+                        st.session_state.generated_questions = set()
+                    if "suggestion" not in st.session_state:
+                        st.session_state.suggestion = []
+                    if "submitted" not in st.session_state:
+                        st.session_state.submitted = False  # Track if the submit button has been pressed
+                    if "deleted" not in st.session_state:
+                        st.session_state.deleted = False  # Track if the file has been deleted
+
+                    # Generate questions
+                    if prompt1:
+                        question_chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)
+                        question = question_chain.run({"Chapter": prompt1})
+                        while len(st.session_state.questions) < num_questions:
+                            if question not in st.session_state.generated_questions:
+                                st.session_state.questions.append(question)
+                                st.session_state.generated_questions.add(question)
+                            if len(st.session_state.questions) >= num_questions:
+                                break
+
+                    # Display questions and text areas for answers
+                    for i in range(num_questions):
+                        if i < len(st.session_state.questions):
+                            st.write(f"### Question {i + 1}: {st.session_state.questions[i]}")
+                            answer_key = f"answer_{i}" 
+                            answer = st.text_area(f"Enter your answer for Question {i + 1}", key=answer_key, 
+                                                value=st.session_state.answers.get(answer_key, ""))
+                            st.session_state.answers[answer_key] = answer
+
+                    # Single submit button
+                    if not st.session_state.submitted and st.button("Submit All Answers"):
+                        st.session_state.submitted = True  # Mark as submitted
+
+                        # Process answers, generate marks and suggestions
+                        for i in range(num_questions):
+                            if i < len(st.session_state.questions):
+                                answer = st.session_state.answers.get(f"answer_{i}", "")
+                                if answer:
+                                    # Evaluate answer
+                                    response_chain = LLMChain(llm=llm, prompt=prompt_template2, verbose=True)
+                                    response = response_chain.run({"input": st.session_state.questions[i], 'answer': answer})
+                                    suggested_chain = LLMChain(llm=llm, prompt=prompt_template3, verbose=True)
+                                    suggestion = suggested_chain.run({"question": st.session_state.questions[i]})
+                                    
+                                    # Parse marks
+                                    match = re.search(r'\d+(\.\d+)?', response)
+                                    marks = 1 if match and float(match.group()) >= 0.6 else 0
+                                    st.session_state.marks.append(marks)
+                                    st.session_state.suggestion.append(suggestion)
+
+                                    # Display suggestions and marks
+                                    st.write(f"Suggested Answer {i + 1}: {suggestion}")
+                                    st.write(f"Marks for Question {i + 1}: {marks}/1")
+
+                        # Calculate total marks
+                        if st.session_state.marks:
+                            total_marks = sum(st.session_state.marks)
+                            st.write(f"### Total Marks: {total_marks} out of {num_questions}")
+
+
+    elif selected_class == "Class 10":
+            subject_option = st.selectbox("Select Subject", ["Select"] + list(class_10_subjects.keys()))
+            if subject_option != "Select":
+                chapters = class_10_subjects.get(subject_option, [])
+                chapter_option = st.selectbox("Select Chapter", ["Select"] + chapters)
+                if chapter_option != "Select":
+                    st.write(f"You have selected: **{chapter_option}**")
+
+                    # Prompts
+                    PROMPT_TEMPLATE_STRING = """
+                    Based on the CBSE Class 10, generate a question in the form of a complete sentence:
+                                            Create a question about the following chapter: {Chapter}
+                                            
+                                            Provide only the question as the output, with no additional text. 
+                    """
+                    prompt_template = PromptTemplate(input_variables=["Chapter"], template=PROMPT_TEMPLATE_STRING)
+
+                    PROMPT_TEMPLATE_STRING2 = """
+                    Evaluate the provided answer based strictly on CBSE Class 10 standards, assigning marks out of 1.
+                    Deduct marks for any inaccuracies, even minor ones. If the final score is 0.6 or higher, round it up to 1. Provide only the marks as the output.
+
+                        Question: {input}
+                        Answer: {answer}
+
+                        Output the marks out of 1.
+                    """
+                    prompt_template2 = PromptTemplate(input_variables=["input", "answer"], template=PROMPT_TEMPLATE_STRING2)
+
+                    PROMPT_TEMPLATE_STRING3 = """
+                    Provide a clear and accurate answer to the following question based on CBSE Class 10 standards.
+
+                    Question: {question}
+
+                    Output the answer only.
+                    """
+                    prompt_template3 = PromptTemplate(input_variables=["question"], template=PROMPT_TEMPLATE_STRING3)
+
+                    load_dotenv()
+
+                    Groq_API_KEY = os.getenv("GROQ_API_KEY")
+                    llm = ChatGroq(groq_api_key=Groq_API_KEY, model_name="Llama3-8b-8192")
+                    prompt1 = chapter_option 
+                    num_questions = st.number_input("Enter the number of questions you want", min_value=1, max_value=15, step=1)
+                    
+                    # Initialize session state variables
+                    if "marks" not in st.session_state:
+                        st.session_state.marks = []
+                    if "questions" not in st.session_state:
+                        st.session_state.questions = []
+                    if "answers" not in st.session_state:
+                        st.session_state.answers = {}
+                    if "generated_questions" not in st.session_state:
+                        st.session_state.generated_questions = set()
+                    if "suggestion" not in st.session_state:
+                        st.session_state.suggestion = []
+                    if "submitted" not in st.session_state:
+                        st.session_state.submitted = False  # Track if the submit button has been pressed
+                    if "deleted" not in st.session_state:
+                        st.session_state.deleted = False  # Track if the file has been deleted
+
+                    # Generate questions
+                    if prompt1:
+                        question_chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)
+                        question = question_chain.run({"Chapter": prompt1})
+                        while len(st.session_state.questions) < num_questions:
+                            if question not in st.session_state.generated_questions:
+                                st.session_state.questions.append(question)
+                                st.session_state.generated_questions.add(question)
+                            if len(st.session_state.questions) >= num_questions:
+                                break
+
+                    # Display questions and text areas for answers
+                    for i in range(num_questions):
+                        if i < len(st.session_state.questions):
+                            st.write(f"### Question {i + 1}: {st.session_state.questions[i]}")
+                            answer_key = f"answer_{i}" 
+                            answer = st.text_area(f"Enter your answer for Question {i + 1}", key=answer_key, 
+                                                value=st.session_state.answers.get(answer_key, ""))
+                            st.session_state.answers[answer_key] = answer
+
+                    # Single submit button
+                    if not st.session_state.submitted and st.button("Submit All Answers"):
+                        st.session_state.submitted = True  # Mark as submitted
+
+                        # Process answers, generate marks and suggestions
+                        for i in range(num_questions):
+                            if i < len(st.session_state.questions):
+                                answer = st.session_state.answers.get(f"answer_{i}", "")
+                                if answer:
+                                    # Evaluate answer
+                                    response_chain = LLMChain(llm=llm, prompt=prompt_template2, verbose=True)
+                                    response = response_chain.run({"input": st.session_state.questions[i], 'answer': answer})
+                                    suggested_chain = LLMChain(llm=llm, prompt=prompt_template3, verbose=True)
+                                    suggestion = suggested_chain.run({"question": st.session_state.questions[i]})
+                                    
+                                    # Parse marks
+                                    match = re.search(r'\d+(\.\d+)?', response)
+                                    marks = 1 if match and float(match.group()) >= 0.6 else 0
+                                    st.session_state.marks.append(marks)
+                                    st.session_state.suggestion.append(suggestion)
+
+                                    # Display suggestions and marks
+                                    st.write(f"Suggested Answer {i + 1}: {suggestion}")
+                                    st.write(f"Marks for Question {i + 1}: {marks}/1")
+
+                        # Calculate total marks
+                        if st.session_state.marks:
+                            total_marks = sum(st.session_state.marks)
+                            st.write(f"### Total Marks: {total_marks} out of {num_questions}")
+
+
+    elif selected_class == "Class 11":
+            subject_option = st.selectbox("Select Subject", ["Select"] + list(class_11_subjects.keys()))
+            if subject_option != "Select":
+                chapters = class_11_subjects.get(subject_option, [])
+                chapter_option = st.selectbox("Select Chapter", ["Select"] + chapters)
+                if chapter_option != "Select":
+                    st.write(f"You have selected: **{chapter_option}**")
+
+                    # Prompts
+                    PROMPT_TEMPLATE_STRING = """
+                    Based on the CBSE Class 11, generate a question in the form of a complete sentence:
+                                            Create a question about the following chapter: {Chapter}
+                                            
+                                            Provide only the question as the output, with no additional text. 
+                    """
+                    prompt_template = PromptTemplate(input_variables=["Chapter"], template=PROMPT_TEMPLATE_STRING)
+
+                    PROMPT_TEMPLATE_STRING2 = """
+                    Evaluate the provided answer based strictly on CBSE Class 11 standards, assigning marks out of 1.
+                    Deduct marks for any inaccuracies, even minor ones. If the final score is 0.6 or higher, round it up to 1. Provide only the marks as the output.
+
+                        Question: {input}
+                        Answer: {answer}
+
+                        Output the marks out of 1.
+                    """
+                    prompt_template2 = PromptTemplate(input_variables=["input", "answer"], template=PROMPT_TEMPLATE_STRING2)
+
+                    PROMPT_TEMPLATE_STRING3 = """
+                    Provide a clear and accurate answer to the following question based on CBSE Class 11 standards.
+
+                    Question: {question}
+
+                    Output the answer only.
+                    """
+                    prompt_template3 = PromptTemplate(input_variables=["question"], template=PROMPT_TEMPLATE_STRING3)
+
+                    load_dotenv()
+
+                    Groq_API_KEY = os.getenv("GROQ_API_KEY")
+                    llm = ChatGroq(groq_api_key=Groq_API_KEY, model_name="Llama3-8b-8192")
+                    prompt1 = chapter_option 
+                    num_questions = st.number_input("Enter the number of questions you want", min_value=1, max_value=15, step=1)
+                    
+                    # Initialize session state variables
+                    if "marks" not in st.session_state:
+                        st.session_state.marks = []
+                    if "questions" not in st.session_state:
+                        st.session_state.questions = []
+                    if "answers" not in st.session_state:
+                        st.session_state.answers = {}
+                    if "generated_questions" not in st.session_state:
+                        st.session_state.generated_questions = set()
+                    if "suggestion" not in st.session_state:
+                        st.session_state.suggestion = []
+                    if "submitted" not in st.session_state:
+                        st.session_state.submitted = False  # Track if the submit button has been pressed
+                    if "deleted" not in st.session_state:
+                        st.session_state.deleted = False  # Track if the file has been deleted
+
+                    # Generate questions
+                    if prompt1:
+                        question_chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)
+                        question = question_chain.run({"Chapter": prompt1})
+                        while len(st.session_state.questions) < num_questions:
+                            if question not in st.session_state.generated_questions:
+                                st.session_state.questions.append(question)
+                                st.session_state.generated_questions.add(question)
+                            if len(st.session_state.questions) >= num_questions:
+                                break
+
+                    # Display questions and text areas for answers
+                    for i in range(num_questions):
+                        if i < len(st.session_state.questions):
+                            st.write(f"### Question {i + 1}: {st.session_state.questions[i]}")
+                            answer_key = f"answer_{i}" 
+                            answer = st.text_area(f"Enter your answer for Question {i + 1}", key=answer_key, 
+                                                value=st.session_state.answers.get(answer_key, ""))
+                            st.session_state.answers[answer_key] = answer
+
+                    # Single submit button
+                    if not st.session_state.submitted and st.button("Submit All Answers"):
+                        st.session_state.submitted = True  # Mark as submitted
+
+                        # Process answers, generate marks and suggestions
+                        for i in range(num_questions):
+                            if i < len(st.session_state.questions):
+                                answer = st.session_state.answers.get(f"answer_{i}", "")
+                                if answer:
+                                    # Evaluate answer
+                                    response_chain = LLMChain(llm=llm, prompt=prompt_template2, verbose=True)
+                                    response = response_chain.run({"input": st.session_state.questions[i], 'answer': answer})
+                                    suggested_chain = LLMChain(llm=llm, prompt=prompt_template3, verbose=True)
+                                    suggestion = suggested_chain.run({"question": st.session_state.questions[i]})
+                                    
+                                    # Parse marks
+                                    match = re.search(r'\d+(\.\d+)?', response)
+                                    marks = 1 if match and float(match.group()) >= 0.6 else 0
+                                    st.session_state.marks.append(marks)
+                                    st.session_state.suggestion.append(suggestion)
+
+                                    # Display suggestions and marks
+                                    st.write(f"Suggested Answer {i + 1}: {suggestion}")
+                                    st.write(f"Marks for Question {i + 1}: {marks}/1")
+
+                        # Calculate total marks
+                        if st.session_state.marks:
+                            total_marks = sum(st.session_state.marks)
+                            st.write(f"### Total Marks: {total_marks} out of {num_questions}")
+
+    elif selected_class == "Class 12":
+            subject_option = st.selectbox("Select Subject", ["Select"] + list(class_12_subjects.keys()))
+            if subject_option != "Select":
+                chapters = class_12_subjects.get(subject_option, [])
+                chapter_option = st.selectbox("Select Chapter", ["Select"] + chapters)
+                if chapter_option != "Select":
+                    st.write(f"You have selected: **{chapter_option}**")
+
+                    # Prompts
+                    PROMPT_TEMPLATE_STRING = """
+                    Based on the CBSE Class 12, generate a question in the form of a complete sentence:
+                                            Create a question about the following chapter: {Chapter}
+                                            
+                                            Provide only the question as the output, with no additional text. 
+                    """
+                    prompt_template = PromptTemplate(input_variables=["Chapter"], template=PROMPT_TEMPLATE_STRING)
+
+                    PROMPT_TEMPLATE_STRING2 = """
+                    Evaluate the provided answer based strictly on CBSE Class 12 standards, assigning marks out of 1.
+                    Deduct marks for any inaccuracies, even minor ones. If the final score is 0.6 or higher, round it up to 1. Provide only the marks as the output.
+
+                        Question: {input}
+                        Answer: {answer}
+
+                        Output the marks out of 1.
+                    """
+                    prompt_template2 = PromptTemplate(input_variables=["input", "answer"], template=PROMPT_TEMPLATE_STRING2)
+
+                    PROMPT_TEMPLATE_STRING3 = """
+                    Provide a clear and accurate answer to the following question based on CBSE Class 12 standards.
+
+                    Question: {question}
+
+                    Output the answer only.
+                    """
+                    prompt_template3 = PromptTemplate(input_variables=["question"], template=PROMPT_TEMPLATE_STRING3)
+
+                    load_dotenv()
+
+                    Groq_API_KEY = os.getenv("GROQ_API_KEY")
+                    llm = ChatGroq(groq_api_key=Groq_API_KEY, model_name="Llama3-8b-8192")
+                    prompt1 = chapter_option 
+                    num_questions = st.number_input("Enter the number of questions you want", min_value=1, max_value=15, step=1)
+                    
+                    # Initialize session state variables
+                    if "marks" not in st.session_state:
+                        st.session_state.marks = []
+                    if "questions" not in st.session_state:
+                        st.session_state.questions = []
+                    if "answers" not in st.session_state:
+                        st.session_state.answers = {}
+                    if "generated_questions" not in st.session_state:
+                        st.session_state.generated_questions = set()
+                    if "suggestion" not in st.session_state:
+                        st.session_state.suggestion = []
+                    if "submitted" not in st.session_state:
+                        st.session_state.submitted = False  # Track if the submit button has been pressed
+                    if "deleted" not in st.session_state:
+                        st.session_state.deleted = False  # Track if the file has been deleted
+
+                    # Generate questions
+                    if prompt1:
+                        question_chain = LLMChain(llm=llm, prompt=prompt_template, verbose=True)
+                        question = question_chain.run({"Chapter": prompt1})
+                        while len(st.session_state.questions) < num_questions:
+                            if question not in st.session_state.generated_questions:
+                                st.session_state.questions.append(question)
+                                st.session_state.generated_questions.add(question)
+                            if len(st.session_state.questions) >= num_questions:
+                                break
+
+                    # Display questions and text areas for answers
+                    for i in range(num_questions):
+                        if i < len(st.session_state.questions):
+                            st.write(f"### Question {i + 1}: {st.session_state.questions[i]}")
+                            answer_key = f"answer_{i}" 
+                            answer = st.text_area(f"Enter your answer for Question {i + 1}", key=answer_key, 
+                                                value=st.session_state.answers.get(answer_key, ""))
+                            st.session_state.answers[answer_key] = answer
+
+                    # Single submit button
+                    if not st.session_state.submitted and st.button("Submit All Answers"):
+                        st.session_state.submitted = True  # Mark as submitted
+
+                        # Process answers, generate marks and suggestions
+                        for i in range(num_questions):
+                            if i < len(st.session_state.questions):
+                                answer = st.session_state.answers.get(f"answer_{i}", "")
+                                if answer:
+                                    # Evaluate answer
+                                    response_chain = LLMChain(llm=llm, prompt=prompt_template2, verbose=True)
+                                    response = response_chain.run({"input": st.session_state.questions[i], 'answer': answer})
+                                    suggested_chain = LLMChain(llm=llm, prompt=prompt_template3, verbose=True)
+                                    suggestion = suggested_chain.run({"question": st.session_state.questions[i]})
+                                    
+                                    # Parse marks
+                                    match = re.search(r'\d+(\.\d+)?', response)
+                                    marks = 1 if match and float(match.group()) >= 0.6 else 0
+                                    st.session_state.marks.append(marks)
+                                    st.session_state.suggestion.append(suggestion)
+
+                                    # Display suggestions and marks
+                                    st.write(f"Suggested Answer {i + 1}: {suggestion}")
+                                    st.write(f"Marks for Question {i + 1}: {marks}/1")
+
+                        # Calculate total marks
+                        if st.session_state.marks:
+                            total_marks = sum(st.session_state.marks)
+                            st.write(f"### Total Marks: {total_marks} out of {num_questions}")
+
+                       

requirements.txt

@@ -0,0 +1,19 @@
+langchain
+langchain_core
+streamlit
+langchain_community
+pypdf
+faiss-cpu
+langchainhub
+sentence_transformers
+PyPDF2
+langchain-objectbox
+langchain_cohere
+langchain_google_genai
+python-dotenv
+langchain_groq
+SpeechRecognition 
+pyaudio
+youtube_transcript_api
+ipykernel
+pyttsx3

utils.py

@@ -0,0 +1,311 @@
+import speech_recognition as sr
+import time
+from gtts import gTTS  # new import
+from io import BytesIO 
+import sqlite3
+def speak(text):
+    audio_bytes = BytesIO()
+    tts = gTTS(text=text, lang="en")
+    tts.write_to_fp(audio_bytes)
+    audio_bytes.seek(0)
+    return audio_bytes.read()
+
+
+
+def takeCommand():
+    #It takes microphone input from the user and returns string output
+
+    r = sr.Recognizer()
+    with sr.Microphone() as source:
+        r.pause_threshold = 1
+        audio = r.listen(source)
+
+    try:
+        query = r.recognize_google(audio, language='en-in')
+
+    except Exception as e:
+
+        return "None"
+    return query
+
+
+class_9_subjects = {
+    "Science": [
+        "Chapter 1: Matter in Our Surroundings",
+        "Chapter 2: Is Matter Around Us Pure",
+        "Chapter 3: Atoms and Molecules",
+        "Chapter 4: Structure of the Atom",
+        "Chapter 5: The Fundamental Unit of Life",
+        "Chapter 6: Tissues",
+        "Chapter 7: Diversity in Living Organisms",
+        "Chapter 8: Motion",
+        "Chapter 9: Force and Laws of Motion",
+        "Chapter 10: Gravitation",
+        "Chapter 11: Work and Energy",
+        "Chapter 12: Sound",
+        "Chapter 13: Why Do We Fall Ill",
+        "Chapter 14: Natural Resources",
+        "Chapter 15: Improvement in Food Resources"
+    ],
+    "English": [
+        "Beehive - Chapter 1: The Fun They Had",
+        "Beehive - Chapter 2: The Sound of Music",
+        "Beehive - Chapter 3: The Little Girl",
+        "Beehive - Chapter 4: A Truly Beautiful Mind",
+        "Beehive - Chapter 5: The Snake and the Mirror",
+        "Beehive - Chapter 6: My Childhood",
+        "Beehive - Chapter 7: Packing",
+        "Beehive - Chapter 8: Reach for the Top",
+        "Beehive - Chapter 9: The Bond of Love",
+        "Beehive - Chapter 10: Kathmandu",
+        "Beehive - Chapter 11: If I Were You",
+        "Moments - Chapter 1: The Lost Child",
+        "Moments - Chapter 2: The Adventure of Toto",
+        "Moments - Chapter 3: Iswaran the Storyteller",
+        "Moments - Chapter 4: In the Kingdom of Fools",
+        "Moments - Chapter 5: The Happy Prince",
+        "Moments - Chapter 6: Weathering the Storm in Ersama",
+        "Moments - Chapter 7: The Last Leaf",
+        "Moments - Chapter 8: A House is Not a Home",
+        "Moments - Chapter 9: The Accidental Tourist",
+        "Moments - Chapter 10: The Beggar"
+    ],
+    "History": [
+        "Chapter 1: The French Revolution",
+        "Chapter 2: Socialism in Europe and the Russian Revolution",
+        "Chapter 3: Nazism and the Rise of Hitler",
+        "Chapter 4: Forest Society and Colonialism",
+        "Chapter 5: Pastoralists in the Modern World",
+        "Chapter 6: Peasants and Farmers",
+        "Chapter 7: History and Sport: The Story of Cricket",
+        "Chapter 8: Clothing: A Social History"
+    ],
+    "Geography": [
+        "Chapter 1: India - Size and Location",
+        "Chapter 2: Physical Features of India",
+        "Chapter 3: Drainage",
+        "Chapter 4: Climate",
+        "Chapter 5: Natural Vegetation and Wildlife",
+        "Chapter 6: Population"
+    ],
+    "Civics": [
+        "Chapter 1: What is Democracy? Why Democracy?",
+        "Chapter 2: Constitutional Design",
+        "Chapter 3: Electoral Politics",
+        "Chapter 4: Working of Institutions",
+        "Chapter 5: Democratic Rights"
+    ],
+    "Economics": [
+        "Chapter 1: The Story of Village Palampur",
+        "Chapter 2: People as Resource",
+        "Chapter 3: Poverty as a Challenge",
+        "Chapter 4: Food Security in India"
+    ]
+}
+
+class_10_subjects = {
+    "Science": [
+        "Chapter 1: Chemical Reactions and Equations",
+        "Chapter 2: Acids, Bases, and Salts",
+        "Chapter 3: Metals and Non-Metals",
+        "Chapter 4: Carbon and Its Compounds",
+        "Chapter 5: Periodic Classification of Elements",
+        "Chapter 6: Light",
+        "Chapter 7: Human Eye and the Colourful World",
+        "Chapter 8: Electricity",
+        "Chapter 9: Magnetic Effects of Current",
+        "Chapter 10: Sources of Energy",
+        "Chapter 11: Life Processes",
+        "Chapter 12: Control and Coordination",
+        "Chapter 13: How do Organisms Reproduce?",
+        "Chapter 14: Heredity and Evolution",
+        "Chapter 15: Our Environment",
+        "Chapter 16: Management of Natural Resources"
+    ],
+    "English": [
+        "First Flight - Chapter 1: A Letter to God",
+        "First Flight - Chapter 2: Nelson Mandela: Long Walk to Freedom",
+        "First Flight - Chapter 3: Two Stories about Flying",
+        "First Flight - Chapter 4: From the Diary of Anne Frank",
+        "First Flight - Chapter 5: The Hundred Dresses - I",
+        "First Flight - Chapter 6: The Hundred Dresses - II",
+        "First Flight - Chapter 7: Glimpses of India",
+        "First Flight - Chapter 8: Mijbil the Otter",
+        "First Flight - Chapter 9: Madam Rides the Bus",
+        "First Flight - Chapter 10: The Sermon at Benares",
+        "First Flight - Chapter 11: The Proposal",
+        "Footprints Without Feet - Chapter 1: A Triumph of Surgery",
+        "Footprints Without Feet - Chapter 2: The Thief’s Story",
+        "Footprints Without Feet - Chapter 3: The Midnight Visitor",
+        "Footprints Without Feet - Chapter 4: A Question of Trust",
+        "Footprints Without Feet - Chapter 5: The Book That Saved the Earth",
+        "Footprints Without Feet - Chapter 6: The Drop of Blood",
+        "Footprints Without Feet - Chapter 7: The Making of a Scientist",
+        "Footprints Without Feet - Chapter 8: The Beggar"
+    ],
+    "History": [
+        "Chapter 1: The Rise of Nationalism in Europe",
+        "Chapter 2: Nationalism in India",
+        "Chapter 3: The Making of a Global World",
+        "Chapter 4: The Age of Industrialization",
+        "Chapter 5: Print Culture and the Modern World",
+        "Chapter 6: Novels, Society, and History"
+    ],
+    "Geography": [
+        "Chapter 1: Resources and Development",
+        "Chapter 2: Forest and Wildlife Resources",
+        "Chapter 3: Water Resources",
+        "Chapter 4: Agriculture",
+        "Chapter 5: Minerals and Energy Resources",
+        "Chapter 6: Manufacturing Industries",
+        "Chapter 7: Life Lines of National Economy"
+    ],
+    "Civics": [
+        "Chapter 1: Power Sharing",
+        "Chapter 2: Federalism",
+        "Chapter 3: Political Parties",
+        "Chapter 4: Democratic Rights"
+    ],
+    "Economics": [
+        "Chapter 1: Development",
+        "Chapter 2: Sectors of the Indian Economy",
+        "Chapter 3: Money and Credit",
+        "Chapter 4: Globalization and the Indian Economy",
+        "Chapter 5: Consumer Rights"
+    ]
+}
+
+class_11_subjects = {
+    "Biology": [
+        "Chapter 1: Diversity in Living World",
+        "Chapter 2: Structural Organisation in Animals and Plants",
+        "Chapter 3: Cell Structure and Function",
+        "Chapter 4: Plant Physiology",
+        "Chapter 5: Human Physiology",
+        "Chapter 6: Reproduction",
+        "Chapter 7: Genetics and Evolution",
+        "Chapter 8: Biology and Human Welfare",
+        "Chapter 9: Biotechnology and its Applications",
+        "Chapter 10: Ecology and Environment"
+    ],
+    "English": [
+        "Hornbill - Chapter 1: The Portrait of a Lady",
+        "Hornbill - Chapter 2: We're Not Afraid to Die... if We Can All Be Together",
+        "Hornbill - Chapter 3: Discovering Tut: The Saga Continues",
+        "Hornbill - Chapter 4: Landscape of the Soul",
+        "Hornbill - Chapter 5: The Ailing Planet: The Green Movement's Role",
+        "Hornbill - Chapter 6: The Browning Version",
+        "Hornbill - Chapter 7: The Adventure",
+        "Hornbill - Chapter 8: Silk Road",
+        "Snapshots - Chapter 1: The Summer of the Beautiful White Horse",
+        "Snapshots - Chapter 2: The Address",
+        "Snapshots - Chapter 3: Ranga's Marriage",
+        "Snapshots - Chapter 4: Albert Einstein at School",
+        "Snapshots - Chapter 5: Mother’s Day",
+        "Snapshots - Chapter 6: The Ghat of the Only World",
+        "Snapshots - Chapter 7: A House is Not a Home",
+        "Snapshots - Chapter 8: The Book That Saved the Earth"
+    ],
+    "Physics": [
+        "Chapter 1: Physical World",
+        "Chapter 2: Units and Measurements",
+        "Chapter 3: Motion in a Straight Line",
+        "Chapter 4: Motion in a Plane",
+        "Chapter 5: Laws of Motion",
+        "Chapter 6: Work, Energy, and Power",
+        "Chapter 7: System of Particles and Rotational Motion",
+        "Chapter 8: Gravitation",
+        "Chapter 9: Properties of Bulk Matter",
+        "Chapter 10: Thermodynamics",
+        "Chapter 11: Behaviour of Perfect Gas and Kinetic Theory",
+        "Chapter 12: Oscillations and Waves"
+    ],
+    "Chemistry": [
+        "Chapter 1: Some Basic Concepts of Chemistry",
+        "Chapter 2: Structure of Atom",
+        "Chapter 3: Classification of Elements and Periodicity in Properties",
+        "Chapter 4: Chemical Bonding and Molecular Structure",
+        "Chapter 5: States of Matter: Gases and Liquids",
+        "Chapter 6: Thermodynamics",
+        "Chapter 7: Equilibrium",
+        "Chapter 8: Redox Reactions",
+        "Chapter 9: Hydrogen",
+        "Chapter 10: s-Block Element (Alkali and Alkaline earth metals)",
+        "Chapter 11: Some p-Block Elements",
+        "Chapter 12: Organic Chemistry - Some Basic Principles and Techniques",
+        "Chapter 13: Hydrocarbons",
+        "Chapter 14: Environmental Chemistry"
+    ]
+}
+
+class_12_subjects = {
+    "Biology": [
+        "Chapter 1: Reproduction",
+        "Chapter 2: Genetics and Evolution",
+        "Chapter 3: Biology and Human Welfare",
+        "Chapter 4: Biotechnology and Its Applications",
+        "Chapter 5: Ecology and Environment",
+        "Chapter 6: Biotechnology: Principles and Processes",
+        "Chapter 7: Human Health and Disease",
+        "Chapter 8: Strategies for Enhancement in Food Production",
+        "Chapter 9: Microbes in Human Welfare",
+        "Chapter 10: Biodiversity and Conservation",
+        "Chapter 11: Biotechnology and Its Applications",
+        "Chapter 12: Organisms and Populations",
+        "Chapter 13: Ecosystem",
+        "Chapter 14: Environmental Issues"
+    ],
+    "English": [
+        "Flamingo - Chapter 1: The Last Lesson",
+        "Flamingo - Chapter 2: Lost Spring: Stories of Stolen Childhood",
+        "Flamingo - Chapter 3: Deep Water",
+        "Flamingo - Chapter 4: The Rattrap",
+        "Flamingo - Chapter 5: Indigo",
+        "Flamingo - Chapter 6: Going Places",
+        "Vistas - Chapter 1: The Third Level",
+        "Vistas - Chapter 2: The Tiger King",
+        "Vistas - Chapter 3: Journey to the End of the Earth",
+        "Vistas - Chapter 4: The Enemy",
+        "Vistas - Chapter 5: Should Wizard Hit Mommy?",
+        "Vistas - Chapter 6: On the Face of It",
+        "Vistas - Chapter 7: Evans Tries an O-Level",
+        "Vistas - Chapter 8: Memories of Childhood"
+    ],
+    "Physics": [
+        "Chapter 1: Electric Charges and Fields",
+        "Chapter 2: Electrostatic Potential and Capacitance",
+        "Chapter 3: Current Electricity",
+        "Chapter 4: Moving Charges and Magnetism",
+        "Chapter 5: Magnetism and Matter",
+        "Chapter 6: Electromagnetic Induction",
+        "Chapter 7: Alternating Currents",
+        "Chapter 8: Electromagnetic Waves",
+        "Chapter 9: Optics",
+        "Chapter 10: Wave Optics",
+        "Chapter 11: Dual Nature of Radiation and Matter",
+        "Chapter 12: Atoms",
+        "Chapter 13: Nuclei",
+        "Chapter 14: Semiconductor Electronics",
+        "Chapter 15: Communication Systems"
+    ],
+    "Chemistry": [
+        "Chapter 1: The Solid State",
+        "Chapter 2: Solutions",
+        "Chapter 3: Electrochemistry",
+        "Chapter 4: Chemical Kinetics",
+        "Chapter 5: Surface Chemistry",
+        "Chapter 6: General Principles and Processes of Isolation of Elements",
+        "Chapter 7: p-Block Elements",
+        "Chapter 8: d and f Block Elements",
+        "Chapter 9: Coordination Compounds",
+        "Chapter 10: Haloalkanes and Haloarenes",
+        "Chapter 11: Alcohols, Phenols, and Ethers",
+        "Chapter 12: Aldehydes, Ketones, and Carboxylic Acids",
+        "Chapter 13: Organic Compounds Containing Nitrogen",
+        "Chapter 14: Biomolecules",
+        "Chapter 15: Polymers",
+        "Chapter 16: Chemistry in Everyday Life"
+    ]
+}
+
+