Delete 03_Ink QA™ | Intensifying Literacy Instruction_Conversational_HF.py

03_Ink QA™ | Intensifying Literacy Instruction_Conversational_HF.py

@@ -1,166 +0,0 @@
-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
-
-# Hardcode the OpenAI API key
-openai_api_key = "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" 
-
-# Pinecone API key and environment
-api_key = "58e247f3-041d-48ed-8466-61b39efa56a9"
-environment = "gcp-starter"
-
-# Initialize Pinecone
-pinecone.init(api_key=api_key, environment=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)
-
-# Define the metadata for filtering
-# metadata = {'source': '/Users/cheynelevesseur/Desktop/Python_Code/Projects/LLM/Intensifying Literacy Instruction - Essential Practices (NATIONAL).pdf'}
-
-# 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)
-    retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3})
-
-    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('/Users/cheynelevesseur/Desktop/Python_Code/Projects/LLM/Streamlit_Document_Reader_Simple/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('/Users/cheynelevesseur/Desktop/Python_Code/Projects/LLM/Streamlit_Document_Reader_Simple/mimtss.png', width=200)
-    st.image('/Users/cheynelevesseur/Desktop/Python_Code/Projects/LLM/Streamlit_Document_Reader_Simple/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 = '/Users/cheynelevesseur/Desktop/Python_Code/Projects/LLM/Streamlit_Document_Reader_Simple/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)