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app.py

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+import streamlit as st
+from function import GetLLMResponse
+
+# List of math topics and difficulty levels
+math_topics = {
+    "Elementary School Level": ["Basic Arithmetic", "Place Value", "Fraction", "Decimals", "Geomerty"],
+    "Middle School Level": ["Algebra", "Ratio and Proportion", "Percentages", "Geometry", "Integers and Rational Numbers"],
+    "High School Level": ["Algebra II", "Trigonometry", "Pre-Calculus", "Calculus", "Statistics and Probability"]
+}
+
+# Page configuration
+st.set_page_config(page_title="Generate Math Quizzes",
+                   page_icon="🧮",
+                   layout="centered",
+                   initial_sidebar_state="collapsed")
+
+# Header and description
+st.title("Generate Math Quizzes 🧮")
+st.text("Choose the difficulty level and topic for your math quizzes.")
+
+# User input for quiz generation
+## Layout in columns
+col1, col2, col3 = st.columns([1, 1, 1])
+
+with col1:
+    selected_topic_level = st.selectbox('Select Topic Level', list(math_topics.keys()))
+
+with col2:
+    selected_topic = st.selectbox('Select Topic', math_topics[selected_topic_level])
+
+with col3:
+    num_quizzes = st.slider('Number Quizzes', min_value=1, max_value= 5, value=1)
+
+submit = st.button('Generate Quizzes')
+
+
+# Final Response
+if submit:
+    with st.spinner("Generating Quizzes..."):
+        response = GetLLMResponse(selected_topic_level, selected_topic, num_quizzes)
+        st.success("Quizzes Generated!")
+        
+        # Display questions and answers in a table
+        if response:
+            st.subheader("Quiz Questions and Answers:")
+            # Prepare data for the table
+            col1, col2 = st.columns(2)
+            with col1:
+                st.subheader("Questions")
+                questions = response.get('questions')
+                st.write(questions)
+            
+            with col2:
+                st.subheader("Answers")
+                answers = response.get('answer')
+                st.write(answers)
+
+        else:
+            st.warning("No Quiz Questions and Answers")
+            
+else:
+    st.warning("Click the 'Generate Quizzes' button to create quizzes.")

function.py

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+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+from langchain.chains import LLMChain
+from langchain.chains import SequentialChain
+from langchain.llms import HuggingFaceHub
+from dotenv import load_dotenv
+
+# load_dotenv();
+config = {'max_new_tokens': 512, 'temperature': 0.6}
+
+# Create function for app
+def GetLLMResponse(selected_topic_level, 
+                    selected_topic,
+                    num_quizzes):
+    
+    # Calling llama model
+    # llm = CTransformers(model="D:\Code Workspace\DL Model\llama-2-7b-chat.ggmlv3.q8_0.bin",
+    #                     model_type = 'llama',
+    #                     config = config)
+
+    # llm = CTransformers(model='TheBloke/Llama-2-7B-Chat-GGML',
+    #                     model_file = 'llama-2-7b-chat.ggmlv3.q8_0.bin',
+    #                     model_type = 'llama',
+    #                     config = config)
+
+    llm = HuggingFaceHub(
+        repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        model_kwargs = config
+    )
+    
+    ## Create LLM Chaining
+    questions_template = "I want you to just generate question with this specification: Generate a {selected_topic_level} math quiz on the topic of {selected_topic}. Generate only {num_quizzes} questions not more and without providing answers. The Question should not in image format/link"
+    questions_prompt = PromptTemplate(input_variables=["selected_topic_level", "selected_topic", "num_quizzes"],
+                                      template=questions_template)
+    questions_chain = LLMChain(llm= llm,
+                               prompt = questions_prompt,
+                               output_key = "questions")
+
+
+    answer_template = "I want you to become a teacher answer this specific Question:\n {questions}\n\n. You should gave me a straightforward and consise explanation and answer to each one of them"
+    answer_prompt = PromptTemplate(input_variables = ["questions"],
+                                   template = answer_template)
+    answer_chain = LLMChain(llm = llm,
+                            prompt = answer_prompt,
+                            output_key = "answer")
+    
+    ## Create Sequential Chaining
+    seq_chain = SequentialChain(chains = [questions_chain, answer_chain],
+                                input_variables = ['selected_topic_level', 'selected_topic', 'num_quizzes'],
+                                output_variables = ['questions', 'answer'])
+    
+    response = seq_chain({'selected_topic_level': selected_topic_level, 
+                            'selected_topic': selected_topic, 
+                            'num_quizzes' : num_quizzes})
+    
+    ## Generate the response from the llama 2 model
+    print(response)
+    return response

requirements.txt

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+sentence-transformers
+uvicorn
+ctransformers
+langchain
+python-box
+streamlit
+python-dotenv
+huggingface_hub