Create appX.py

appX.py

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+#------------------------------------------------------------------------
+# Import Modules
+#------------------------------------------------------------------------
+
+import streamlit as st
+import openai
+import random
+import os
+from pinecone import Pinecone
+from langchain.chat_models import ChatOpenAI
+from langsmith import Client
+
+#------------------------------------------------------------------------
+# Load API Keys From the .env File & Load the OpenAI, Pinecone, and LangSmith Client
+#------------------------------------------------------------------------
+
+# 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)
+
+os.environ ["LANGCHAIN_API_KEY"] = str(os.getenv("LANGCHAIN_API_KEY"))
+os.environ ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+os.environ ["LANGCHAIN_PROJECT"] = "Inkqa"
+
+client = Client() #langsmith client
+
+#------------------------------------------------------------------------
+# Initialize 
+#------------------------------------------------------------------------
+
+# # 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)