app.py

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 = "555c0e70-331d-4b43-aac7-5b3aac5078d6"
# pc = Pinecone(api_key=PINECONE_API_KEY)



import os
# from pathlib import Path
# Import specific functions from python-dotenv package (i.e., used to read key-value pairs from a .env file and add them to the environment variables.)
from dotenv import load_dotenv, find_dotenv
# Find and load .env file, override existing environment variables
load_dotenv(find_dotenv(), override=True)

# Retrieve OpenAI API key from environment variables
openai_api_key = os.getenv('OPENAI_API_KEY')
if not openai_api_key:
    raise ValueError("OPENAI_API_KEY not set in environment variables")
# Initialize the OpenAI service with API key
openai.api_key = openai_api_key

# Hardcode the OpenAI API key
# openai_api_key = "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" 

# AUTHENTICATE/INITIALIZE PINCONE SERVICE
from pinecone import Pinecone
pc = Pinecone()





# # 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)
    
    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)