import streamlit as st import openai import random # Fetch the OpenAI API key from Streamlit secrets openai_api_key = st.secrets["openai_api_key"] # Initialize the OpenAI service with API key openai.api_key = openai_api_key # Fetch Pinecone API key and environment from Streamlit secrets pinecone_api_key = st.secrets["pinecone_api_key"] # pinecone_api_key = '555c0e70-331d-4b43-aac7-5b3aac5078d6' pinecone_environment = st.secrets["pinecone_environment"] # AUTHENTICATE/INITIALIZE PINCONE SERVICE from pinecone import Pinecone # pc = Pinecone(api_key=pinecone_api_key) # pc = Pinecone (api_key= 'YOUR_API_KEY') Pinecone(api_key=pinecone_api_key, environment=pinecone_environment) # # Define the name of the Pinecone index index_name = 'mimtssinkqa' # Initialize the OpenAI embeddings object from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) # LOAD VECTOR STORE FROM EXISTING INDEX # from langchain_community.vectorstores import Pinecone from langchain_community.vectorstores import Pinecone as PineconeStore # vector_store = Pinecone.from_existing_index(index_name='mimtssinkqa', embedding=embeddings) vector_store = PineconeStore.from_existing_index(index_name=index_name, embedding=embeddings) def ask_with_memory(vector_store, query, chat_history=[]): from langchain_openai import ChatOpenAI from langchain.chains import ConversationalRetrievalChain from langchain.memory import ConversationBufferMemory from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.5) retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3}) memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) system_template = r''' Use the following pieces of context to answer the user's question. The title of the article is Intensifying literacy Instruction: Essential Practices. Do not mention the Header unless asked. ---------------- Context: ```{context}``` ''' user_template = ''' Question: ```{question}``` Chat History: ```{chat_history}``` ''' messages= [ SystemMessagePromptTemplate.from_template(system_template), HumanMessagePromptTemplate.from_template(user_template) ] qa_prompt = ChatPromptTemplate.from_messages (messages) chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory,chain_type='stuff', combine_docs_chain_kwargs={'prompt': qa_prompt}, verbose=False ) result = chain.invoke({'question': query, 'chat_history': st.session_state['history']}) # Append to chat history as a dictionary st.session_state['history'].append((query, result['answer'])) return (result['answer']) # Initialize chat history if 'history' not in st.session_state: st.session_state['history'] = [] # # STREAMLIT APPLICATION SETUP WITH PASSWORD # Define the correct password # correct_password = "MiBLSi" #Add the image with a specified width image_width = 300 # Set the desired width in pixels st.image('MTSS.ai_Logo.png', width=image_width) st.subheader('Ink QA™ | Dynamic PDFs') # Using Markdown for formatted text st.markdown(""" Resource: **Intensifying Literacy Instruction: Essential Practices** """, unsafe_allow_html=True) with st.sidebar: # Password input field # password = st.text_input("Enter Password:", type="password") st.image('mimtss.png', width=200) st.image('Literacy_Cover.png', width=200) st.link_button("View | Download", "https://mimtsstac.org/sites/default/files/session-documents/Intensifying%20Literacy%20Instruction%20-%20Essential%20Practices%20%28NATIONAL%29.pdf") Audio_Header_text = """ **Tune into Dr. St. Martin's introduction**""" st.markdown(Audio_Header_text) # Path or URL to the audio file audio_file_path = 'Audio_Introduction_Literacy.m4a' # Display the audio player widget st.audio(audio_file_path, format='audio/mp4', start_time=0) # Citation text with Markdown formatting citation_Content_text = """ **Citation** St. Martin, K., Vaughn, S., Troia, G., Fien, & H., Coyne, M. (2023). *Intensifying literacy instruction: Essential practices, Version 2.0*. Lansing, MI: MiMTSS Technical Assistance Center, Michigan Department of Education. **Table of Contents** * **Introduction**: pg. 1 * **Intensifying Literacy Instruction: Essential Practices**: pg. 4 * **Purpose**: pg. 4 * **Practice 1**: Knowledge and Use of a Learning Progression for Developing Skilled Readers and Writers: pg. 6 * **Practice 2**: Design and Use of an Intervention Platform as the Foundation for Effective Intervention: pg. 13 * **Practice 3**: On-going Data-Based Decision Making for Providing and Intensifying Interventions: pg. 16 * **Practice 4**: Adaptations to Increase the Instructional Intensity of the Intervention: pg. 20 * **Practice 5**: Infrastructures to Support Students with Significant and Persistent Literacy Needs: pg. 24 * **Motivation and Engagement**: pg. 28 * **Considerations for Understanding How Students' Learning and Behavior are Enhanced**: pg. 28 * **Summary**: pg. 29 * **Endnotes**: pg. 30 * **Acknowledgment**: pg. 39 """ st.markdown(citation_Content_text) # if password == correct_password: # Define a list of possible placeholder texts placeholders = [ 'Example: Summarize the article in 200 words or less', 'Example: What are the essential practices?', 'Example: I am a teacher, why is this resource important?', 'Example: How can this resource support my instruction in reading and writing?', 'Example: Does this resource align with the learning progression for developing skilled readers and writers?', 'Example: How does this resource address the needs of students scoring below the 20th percentile?', 'Example: Are there assessment tools included in this resource to monitor student progress?', 'Example: Does this resource provide guidance on data collection and analysis for monitoring student outcomes?', "Example: How can this resource be used to support students' social-emotional development?", "Example: How does this resource align with the district's literacy goals and objectives?", 'Example: What research and evidence support the effectiveness of this resource?', 'Example: Does this resource provide guidance on implementation fidelity' ] # Select a random placeholder from the list if 'placeholder' not in st.session_state: st.session_state.placeholder = random.choice(placeholders) q = st.text_input(label='Ask a question or make a request ', value='', placeholder=st.session_state.placeholder) # q = st.text_input(label='Ask a question or make a request ', value='') if q: with st.spinner('Thinking...'): answer = ask_with_memory(vector_store, q, st.session_state.history) # Display the response in a text area st.text_area('Response: ', value=answer, height=400, key="response_text_area") st.success('Powered by MTSS GPT. AI can make mistakes. Consider checking important information.') # Prepare chat history text for display # history_text = "\n\n".join(f"Q: {entry[0]}\nA: {entry[1]}" for entry in st.session_state.history) # Prepare chat history text for display in reverse order history_text = "\n\n".join(f"Q: {entry[0]}\nA: {entry[1]}" for entry in reversed(st.session_state.history)) # Display chat history st.text_area('Chat History', value=history_text, height=800) # import streamlit as st # import pinecone # from langchain.embeddings.openai import OpenAIEmbeddings # from langchain.vectorstores import Pinecone, Chroma # from langchain.chains import RetrievalQA # from langchain.chat_models import ChatOpenAI # import tiktoken # import random # # Fetch the OpenAI API key from Streamlit secrets # openai_api_key = st.secrets["openai_api_key"] # # Fetch Pinecone API key and environment from Streamlit secrets # pinecone_api_key = st.secrets["pinecone_api_key"] # pinecone_environment = st.secrets["pinecone_environment"] # # Initialize Pinecone # pinecone.init(api_key=pinecone_api_key, environment=pinecone_environment) # # Define the name of the Pinecone index # index_name = 'mi-resource-qa' # # Initialize the OpenAI embeddings object with the hardcoded API key # embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) # # Define functions # def insert_or_fetch_embeddings(index_name): # if index_name in pinecone.list_indexes(): # vector_store = Pinecone.from_existing_index(index_name, embeddings) # return vector_store # else: # raise ValueError(f"Index {index_name} does not exist. Please create it before fetching.") # # Initialize or fetch Pinecone vector store # vector_store = insert_or_fetch_embeddings(index_name) # # calculate embedding cost using tiktoken # def calculate_embedding_cost(text): # import tiktoken # enc = tiktoken.encoding_for_model('text-embedding-ada-002') # total_tokens = len(enc.encode(text)) # # print(f'Total Tokens: {total_tokens}') # # print(f'Embedding Cost in USD: {total_tokens / 1000 * 0.0004:.6f}') # return total_tokens, total_tokens / 1000 * 0.0004 # def ask_with_memory(vector_store, query, chat_history=[]): # from langchain.chains import ConversationalRetrievalChain # from langchain.chat_models import ChatOpenAI # llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=1, openai_api_key=openai_api_key) # # The retriever is created with metadata filter directly in search_kwargs # # retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3, 'filter': {'source': {'$eq': 'https://mimtsstac.org/sites/default/files/session-documents/Intensifying%20Literacy%20Instruction%20-%20Essential%20Practices%20%28NATIONAL%29.pdf'}}}) # retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3, 'filter': {'source':'https://mimtsstac.org/sites/default/files/session-documents/Intensifying%20Literacy%20Instruction%20-%20Essential%20Practices%20%28NATIONAL%29.pdf'}}) # chain= ConversationalRetrievalChain.from_llm(llm, retriever) # result = chain({'question': query, 'chat_history': st.session_state['history']}) # # Append to chat history as a dictionary # st.session_state['history'].append((query, result['answer'])) # return (result['answer']) # # Initialize chat history # if 'history' not in st.session_state: # st.session_state['history'] = [] # # # STREAMLIT APPLICATION SETUP WITH PASSWORD # # Define the correct password # # correct_password = "MiBLSi" # #Add the image with a specified width # image_width = 300 # Set the desired width in pixels # st.image('MTSS.ai_Logo.png', width=image_width) # st.subheader('Ink QA™ | Dynamic PDFs') # # Using Markdown for formatted text # st.markdown(""" # Resource: **Intensifying Literacy Instruction: Essential Practices** # """, unsafe_allow_html=True) # with st.sidebar: # # Password input field # # password = st.text_input("Enter Password:", type="password") # st.image('mimtss.png', width=200) # st.image('Literacy_Cover.png', width=200) # st.link_button("View | Download", "https://mimtsstac.org/sites/default/files/session-documents/Intensifying%20Literacy%20Instruction%20-%20Essential%20Practices%20%28NATIONAL%29.pdf") # Audio_Header_text = """ # **Tune into Dr. St. Martin's introduction**""" # st.markdown(Audio_Header_text) # # Path or URL to the audio file # audio_file_path = 'Audio_Introduction_Literacy.m4a' # # Display the audio player widget # st.audio(audio_file_path, format='audio/mp4', start_time=0) # # Citation text with Markdown formatting # citation_Content_text = """ # **Citation** # St. Martin, K., Vaughn, S., Troia, G., Fien, & H., Coyne, M. (2023). *Intensifying literacy instruction: Essential practices, Version 2.0*. Lansing, MI: MiMTSS Technical Assistance Center, Michigan Department of Education. # **Table of Contents** # * **Introduction**: pg. 1 # * **Intensifying Literacy Instruction: Essential Practices**: pg. 4 # * **Purpose**: pg. 4 # * **Practice 1**: Knowledge and Use of a Learning Progression for Developing Skilled Readers and Writers: pg. 6 # * **Practice 2**: Design and Use of an Intervention Platform as the Foundation for Effective Intervention: pg. 13 # * **Practice 3**: On-going Data-Based Decision Making for Providing and Intensifying Interventions: pg. 16 # * **Practice 4**: Adaptations to Increase the Instructional Intensity of the Intervention: pg. 20 # * **Practice 5**: Infrastructures to Support Students with Significant and Persistent Literacy Needs: pg. 24 # * **Motivation and Engagement**: pg. 28 # * **Considerations for Understanding How Students' Learning and Behavior are Enhanced**: pg. 28 # * **Summary**: pg. 29 # * **Endnotes**: pg. 30 # * **Acknowledgment**: pg. 39 # """ # st.markdown(citation_Content_text) # # if password == correct_password: # # Define a list of possible placeholder texts # placeholders = [ # 'Example: Summarize the article in 200 words or less', # 'Example: What are the essential practices?', # 'Example: I am a teacher, why is this resource important?', # 'Example: How can this resource support my instruction in reading and writing?', # 'Example: Does this resource align with the learning progression for developing skilled readers and writers?', # 'Example: How does this resource address the needs of students scoring below the 20th percentile?', # 'Example: Are there assessment tools included in this resource to monitor student progress?', # 'Example: Does this resource provide guidance on data collection and analysis for monitoring student outcomes?', # "Example: How can this resource be used to support students' social-emotional development?", # "Example: How does this resource align with the district's literacy goals and objectives?", # 'Example: What research and evidence support the effectiveness of this resource?', # 'Example: Does this resource provide guidance on implementation fidelity' # ] # # Select a random placeholder from the list # if 'placeholder' not in st.session_state: # st.session_state.placeholder = random.choice(placeholders) # q = st.text_input(label='Ask a question or make a request ', value='', placeholder=st.session_state.placeholder) # # q = st.text_input(label='Ask a question or make a request ', value='') # k = 3 # Set k to 3 # # # Initialize chat history if not present # # if 'history' not in st.session_state: # # st.session_state.history = [] # if q: # with st.spinner('Thinking...'): # answer = ask_with_memory(vector_store, q, st.session_state.history) # # Display the response in a text area # st.text_area('Response: ', value=answer, height=400, key="response_text_area") # st.success('Powered by MTSS GPT. AI can make mistakes. Consider checking important information.') # # # Prepare chat history text for display # # history_text = "\n\n".join(f"Q: {entry[0]}\nA: {entry[1]}" for entry in st.session_state.history) # # Prepare chat history text for display in reverse order # history_text = "\n\n".join(f"Q: {entry[0]}\nA: {entry[1]}" for entry in reversed(st.session_state.history)) # # Display chat history # st.text_area('Chat History', value=history_text, height=800)