Update app.py

app.py

@@ -1,61 +1,239 @@
+# 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)
+
+
+
+
+
 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 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_environment = st.secrets["pinecone_environment"]
 
-# Initialize Pinecone
-pinecone.init(api_key=pinecone_api_key, environment=pinecone_environment)
+# AUTHENTICATE/INITIALIZE PINCONE SERVICE
+from pinecone import Pinecone
+pc = Pinecone()
 
 # 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)
+index_name = 'mimtssinkqa'
 
-# 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
+# Initialize the OpenAI embeddings object 
+from langchain_openai import OpenAIEmbeddings
+embeddings = OpenAIEmbeddings()
 
+# 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.chat_models import ChatOpenAI
-    
-    llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=1, openai_api_key=openai_api_key)
+    from langchain.memory import ConversationBufferMemory
+
+    from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+
+    llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.5)
     
-    # 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'}})
+    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']})
+    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']))
     
@@ -142,12 +320,6 @@ if 'placeholder' not in st.session_state:
     
 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...'):