Update app.py

app.py

@@ -1,62 +1,58 @@
-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.")
+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