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| 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) |