import streamlit as st import openai import random # Fetch the OpenAI API key from Streamlit secrets OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"] # Retrieve the OpenAI API Key from 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"] # # AUTHENTICATE/INITIALIZE PINCONE SERVICE from pinecone import Pinecone # PINECONE_API_KEY = "555c0e70-331d-4b43-aac7-5b3aac5078d6" pc = Pinecone(api_key=PINECONE_API_KEY) # # 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 vector_store = Pinecone.from_existing_index(index_name='mimtssinkqa', 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, openai_api_key=OPENAI_API_KEY) retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3}) memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) system_template = r''' Article Title: 'Intensifying Literacy Instruction: Essential Practices.' Article Focus: The main focus of the article is reading and the secondary focus is writing. Expertise: Assume the role of an expert literacy coach with in-depth knowledge of the Simple View of Reading, School-Wide Positive Behavioral Interventions and Supports (SWPBIS), and Social Emotional Learning (SEL). Audience: Tailor your response for teachers and administrators seeking to enhance literacy instruction within their educational settings. Response Requirements: Provide an answer utilizing the context provided. Unless specifically requested by the user, avoid mentioning the article's header. Cover all necessary details relevant to the question posed, drawing on your expertise in literacy instruction and the Simple View of Reading. Utilize paragraphs for detailed and descriptive explanations, and bullet points for highlighting key points or steps, ensuring the information is easily understood. Conclude with a recapitulation of main points, summarizing the essential takeaways from your response. ---------------- 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) # CLEAR THE TEXT BOX with st.form("Question",clear_on_submit=True): q = st.text_input(label='Ask a Question | Send a Prompt', placeholder=st.session_state.placeholder, value='', ) submitted = st.form_submit_button("Submit") st.divider() if submitted: with st.spinner('Thinking...'): answer = ask_with_memory(vector_store, q, st.session_state.history) # st.write(q) st.write(f"**{q}**") import time import random def stream_answer(): for word in answer.split(" "): yield word + " " # time.sleep(0.02) time.sleep(random.uniform(0.03, 0.08)) st.write(stream_answer) # Display the response in a text area # st.text_area('Response: ', value=answer, height=400, key="response_text_area") # OR to display as Markdown (interprets Markdown formatting) # st.markdown(answer) st.success('Powered by MTSS GPT. AI can make mistakes. Consider checking important information.') st.divider() # # Prepare chat history text for display 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)