Delete app_NoLangSmith.py

app_NoLangSmith.py

@@ -1,194 +0,0 @@
-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)