Update app.py

app.py

@@ -1,20 +1,29 @@
 import os
+import re
 import streamlit as st
 from tempfile import NamedTemporaryFile
+import anthropic
+
+# Import necessary modules from LangChain
 from langchain.chains import create_retrieval_chain
 from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_core.prompts import ChatPromptTemplate
-from langchain_openai import ChatOpenAI
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_community.document_loaders import TextLoader
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.document_loaders import PyPDFLoader, TextLoader
 from langchain_community.vectorstores import FAISS
-from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-import re
-import anthropic
 
 # Function to remove code block markers from the answer
 def remove_code_blocks(text):
+    """
+    Removes code block markers from the answer text.
+
+    Args:
+        text (str): The text from which code block markers should be removed.
+
+    Returns:
+        str: The text without code block markers.
+    """
     code_block_pattern = r"^```(?:\w+)?\n(.*?)\n```$"
     match = re.match(code_block_pattern, text, re.DOTALL)
     if match:
@@ -24,29 +33,48 @@ def remove_code_blocks(text):
 
 # Function to process PDF, run Q&A, and return results
 def process_pdf(api_key, uploaded_file, questions_path, prompt_path, display_placeholder):
+    """
+    Processes a PDF file, runs Q&A, and returns the results.
+
+    Args:
+        api_key (str): OpenAI API key.
+        uploaded_file: Uploaded PDF file.
+        questions_path (str): Path to the questions file.
+        prompt_path (str): Path to the system prompt file.
+        display_placeholder: Streamlit placeholder for displaying results.
+
+    Returns:
+        list: List of QA results.
+    """
+    # Set the OpenAI API key
     os.environ["OPENAI_API_KEY"] = api_key
 
+    # Save the uploaded PDF to a temporary file
     with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
         temp_pdf.write(uploaded_file.read())
         temp_pdf_path = temp_pdf.name
 
+    # Load and split the PDF into documents
     loader = PyPDFLoader(temp_pdf_path)
     docs = loader.load()
-
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=500)
     splits = text_splitter.split_documents(docs)
 
+    # Create a vector store from the documents
     vectorstore = FAISS.from_documents(
-        documents=splits, embedding=OpenAIEmbeddings(model="text-embedding-3-large")
+        documents=splits,
+        embedding=OpenAIEmbeddings(model="text-embedding-3-large")
     )
     retriever = vectorstore.as_retriever(search_kwargs={"k": 10})
 
+    # Load the system prompt
     if os.path.exists(prompt_path):
         with open(prompt_path, "r") as file:
             system_prompt = file.read()
     else:
         raise FileNotFoundError(f"The specified file was not found: {prompt_path}")
 
+    # Create the prompt template
     prompt = ChatPromptTemplate.from_messages(
         [
             ("system", system_prompt),
@@ -54,38 +82,60 @@ def process_pdf(api_key, uploaded_file, questions_path, prompt_path, display_pla
         ]
     )
 
+    # Initialize the language model
     llm = ChatOpenAI(model="gpt-4o")
-    question_answer_chain = create_stuff_documents_chain(llm, prompt, document_variable_name="context")
+
+    # Create the question-answering chain
+    question_answer_chain = create_stuff_documents_chain(
+        llm, prompt, document_variable_name="context"
+    )
     rag_chain = create_retrieval_chain(retriever, question_answer_chain)
 
+    # Load the questions
     if os.path.exists(questions_path):
         with open(questions_path, "r") as file:
             questions = [line.strip() for line in file.readlines() if line.strip()]
     else:
         raise FileNotFoundError(f"The specified file was not found: {questions_path}")
 
+    # Process each question
     qa_results = []
     for question in questions:
         result = rag_chain.invoke({"input": question})
         answer = result["answer"]
 
+        # Remove code block markers
         answer = remove_code_blocks(answer)
 
         qa_text = f"### Question: {question}\n**Answer:**\n{answer}\n"
         qa_results.append(qa_text)
         display_placeholder.markdown("\n".join(qa_results), unsafe_allow_html=True)
 
+    # Clean up temporary PDF file
     os.remove(temp_pdf_path)
 
     return qa_results
 
-# New function to process multi-plan QA using an existing vector store
+# Function to perform multi-plan QA using an existing vector store
 def process_multi_plan_qa(api_key, input_text, display_placeholder):
+    """
+    Performs multi-plan QA using an existing shared vector store.
+
+    Args:
+        api_key (str): OpenAI API key.
+        input_text (str): The question to ask.
+        display_placeholder: Streamlit placeholder for displaying results.
+    """
+    # Set the OpenAI API key
     os.environ["OPENAI_API_KEY"] = api_key
 
     # Load the existing vector store
     embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
-    vector_store = FAISS.load_local("Combined_Summary_Vectorstore", embeddings, allow_dangerous_deserialization=True)
+    vector_store = FAISS.load_local(
+        "Combined_Summary_Vectorstore",
+        embeddings,
+        allow_dangerous_deserialization=True
+    )
 
     # Convert the vector store to a retriever
     retriever = vector_store.as_retriever(search_kwargs={"k": 50})
@@ -108,7 +158,9 @@ def process_multi_plan_qa(api_key, input_text, display_placeholder):
 
     # Create the question-answering chain
     llm = ChatOpenAI(model="gpt-4o")
-    question_answer_chain = create_stuff_documents_chain(llm, prompt, document_variable_name="context")
+    question_answer_chain = create_stuff_documents_chain(
+        llm, prompt, document_variable_name="context"
+    )
     rag_chain = create_retrieval_chain(retriever, question_answer_chain)
 
     # Process the input text
@@ -118,14 +170,27 @@ def process_multi_plan_qa(api_key, input_text, display_placeholder):
     # Display the answer
     display_placeholder.markdown(f"**Answer:**\n{answer}")
 
-def multi_plan_qa_multi_vectorstore(api_key, input_text, display_placeholder):
+# Function to perform multi-plan QA using multiple individual vector stores
+def process_multi_plan_qa_multi_vectorstore(api_key, input_text, display_placeholder):
+    """
+    Performs multi-plan QA using multiple individual vector stores.
+
+    Args:
+        api_key (str): OpenAI API key.
+        input_text (str): The question to ask.
+        display_placeholder: Streamlit placeholder for displaying results.
+    """
+    # Set the OpenAI API key
     os.environ["OPENAI_API_KEY"] = api_key
 
     # Directory containing individual vector stores
     vectorstore_directory = "Individual_Summary_Vectorstores"
 
     # List all vector store directories
-    vectorstore_names = [d for d in os.listdir(vectorstore_directory) if os.path.isdir(os.path.join(vectorstore_directory, d))]
+    vectorstore_names = [
+        d for d in os.listdir(vectorstore_directory)
+        if os.path.isdir(os.path.join(vectorstore_directory, d))
+    ]
 
     # Initialize a list to collect all retrieved chunks
     all_retrieved_chunks = []
@@ -136,13 +201,17 @@ def multi_plan_qa_multi_vectorstore(api_key, input_text, display_placeholder):
 
         # Load the vector store
         embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
-        vector_store = FAISS.load_local(vectorstore_path, embeddings, allow_dangerous_deserialization=True)
+        vector_store = FAISS.load_local(
+            vectorstore_path,
+            embeddings,
+            allow_dangerous_deserialization=True
+        )
 
         # Convert the vector store to a retriever
         retriever = vector_store.as_retriever(search_kwargs={"k": 2})
 
         # Retrieve relevant chunks for the input text
-        retrieved_chunks = retriever.invoke("input_text")
+        retrieved_chunks = retriever.invoke(input_text)
         all_retrieved_chunks.extend(retrieved_chunks)
 
     # Read the system prompt for multi-document QA
@@ -163,16 +232,30 @@ def multi_plan_qa_multi_vectorstore(api_key, input_text, display_placeholder):
 
     # Create the question-answering chain
     llm = ChatOpenAI(model="gpt-4o")
-    question_answer_chain = create_stuff_documents_chain(llm, prompt, document_variable_name="context")
+    question_answer_chain = create_stuff_documents_chain(
+        llm, prompt, document_variable_name="context"
+    )
 
     # Process the combined context
-    result = question_answer_chain.invoke({"input": input_text, "context": all_retrieved_chunks})
+    result = question_answer_chain.invoke({
+        "input": input_text,
+        "context": all_retrieved_chunks
+    })
 
     # Display the answer
-    display_placeholder.markdown(f"**Answer:**\n{result}")
-
+    answer = result["answer"] if "answer" in result else result
+    display_placeholder.markdown(f"**Answer:**\n{answer}")
 
 def load_documents_from_pdf(file):
+    """
+    Loads documents from a PDF file.
+
+    Args:
+        file: Uploaded PDF file.
+
+    Returns:
+        list: List of documents.
+    """
     # Check if the file is a PDF
     if not file.name.endswith('.pdf'):
         raise ValueError("The uploaded file is not a PDF. Please upload a PDF file.")
@@ -187,51 +270,84 @@ def load_documents_from_pdf(file):
     return docs
 
 def load_vector_store_from_path(path):
-    embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
-    return FAISS.load_local(path, embeddings, allow_dangerous_deserialization=True)
+    """
+    Loads a vector store from a given path.
 
+    Args:
+        path (str): Path to the vector store.
+
+    Returns:
+        FAISS: Loaded vector store.
+    """
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
+    return FAISS.load_local(
+        path,
+        embeddings,
+        allow_dangerous_deserialization=True
+    )
 
-# Function to compare document via one-to-many query approach
+# Function to compare documents via one-to-many query approach
 def process_one_to_many_query(api_key, focus_input, comparison_inputs, input_text, display_placeholder):
+    """
+    Compares a focus document against multiple comparison documents using a one-to-many query approach.
+
+    Args:
+        api_key (str): OpenAI API key.
+        focus_input: Focus document (uploaded file or path to vector store).
+        comparison_inputs: List of comparison documents (uploaded files or paths to vector stores).
+        input_text (str): The comparison question to ask.
+        display_placeholder: Streamlit placeholder for displaying results.
+    """
+    # Set the OpenAI API key
     os.environ["OPENAI_API_KEY"] = api_key
     print(comparison_inputs)
     # Load focus documents or vector store
     if isinstance(focus_input, st.runtime.uploaded_file_manager.UploadedFile):
+        # If focus_input is an uploaded PDF file
         focus_docs = load_documents_from_pdf(focus_input)
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=500)
         focus_splits = text_splitter.split_documents(focus_docs)
-        focus_vector_store = FAISS.from_documents(focus_splits, OpenAIEmbeddings(model="text-embedding-3-large"))
+        focus_vector_store = FAISS.from_documents(
+            focus_splits,
+            OpenAIEmbeddings(model="text-embedding-3-large")
+        )
         focus_retriever = focus_vector_store.as_retriever(search_kwargs={"k": 5})
     elif isinstance(focus_input, str) and os.path.isdir(focus_input):
+        # If focus_input is a path to a vector store
         focus_vector_store = load_vector_store_from_path(focus_input)
         focus_retriever = focus_vector_store.as_retriever(search_kwargs={"k": 5})
     else:
         raise ValueError("Invalid focus input type. Must be a PDF file or a path to a vector store.")
 
+    # Retrieve relevant chunks from the focus document
     focus_docs = focus_retriever.invoke(input_text)
 
+    # Initialize list to collect comparison chunks
     comparison_chunks = []
     for comparison_input in comparison_inputs:
         if isinstance(comparison_input, st.runtime.uploaded_file_manager.UploadedFile):
+            # If comparison_input is an uploaded PDF file
             comparison_docs = load_documents_from_pdf(comparison_input)
             text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=500)
             comparison_splits = text_splitter.split_documents(comparison_docs)
-            comparison_vector_store = FAISS.from_documents(comparison_splits, OpenAIEmbeddings(model="text-embedding-3-large"))
+            comparison_vector_store = FAISS.from_documents(
+                comparison_splits,
+                OpenAIEmbeddings(model="text-embedding-3-large")
+            )
             comparison_retriever = comparison_vector_store.as_retriever(search_kwargs={"k": 5})
         elif isinstance(comparison_input, str) and os.path.isdir(comparison_input):
+            # If comparison_input is a path to a vector store
             comparison_vector_store = load_vector_store_from_path(comparison_input)
             comparison_retriever = comparison_vector_store.as_retriever(search_kwargs={"k": 5})
         else:
             raise ValueError("Invalid comparison input type. Must be a PDF file or a path to a vector store.")
 
+        # Retrieve relevant chunks from the comparison document
         comparison_docs = comparison_retriever.invoke(input_text)
         comparison_chunks.extend(comparison_docs)
 
     # Construct the combined context
-    combined_context = (
-        focus_docs +
-        comparison_chunks
-    )
+    combined_context = focus_docs + comparison_chunks
 
     # Read the system prompt
     prompt_path = "Prompts/comparison_prompt.md"
@@ -252,7 +368,7 @@ def process_one_to_many_query(api_key, focus_input, comparison_inputs, input_tex
     # Create the question-answering chain
     llm = ChatOpenAI(model="gpt-4o")
     question_answer_chain = create_stuff_documents_chain(
-        llm, 
+        llm,
         prompt,
         document_variable_name="context"
     )
@@ -264,35 +380,67 @@ def process_one_to_many_query(api_key, focus_input, comparison_inputs, input_tex
     })
 
     # Display the answer
-    display_placeholder.markdown(f"**Answer:**\n{result}")
+    answer = result["answer"] if "answer" in result else result
+    display_placeholder.markdown(f"**Answer:**\n{answer}")
 
 # Function to list vector store documents
 def list_vector_store_documents():
+    """
+    Lists available vector store documents.
+
+    Returns:
+        list: List of document names.
+    """
     # Assuming documents are stored in the "Individual_All_Vectorstores" directory
     directory_path = "Individual_All_Vectorstores"
     if not os.path.exists(directory_path):
-        raise FileNotFoundError(f"The directory '{directory_path}' does not exist. Run `create_and_save_individual_vector_stores()` to create it.")
+        raise FileNotFoundError(
+            f"The directory '{directory_path}' does not exist. "
+            "Run `create_and_save_individual_vector_stores()` to create it."
+        )
     # List all available vector stores by document name
-    documents = [f.replace("_vectorstore", "").replace("_", " ") for f in os.listdir(directory_path) if f.endswith("_vectorstore")]
+    documents = [
+        f.replace("_vectorstore", "").replace("_", " ")
+        for f in os.listdir(directory_path)
+        if f.endswith("_vectorstore")
+    ]
     return documents
 
+# Function to compare plans using a long context model
 def compare_with_long_context(api_key, anthropic_api_key, input_text, focus_plan_path, selected_summaries, display_placeholder):
+    """
+    Compares plans using a long context model.
+
+    Args:
+        api_key (str): OpenAI API key.
+        anthropic_api_key (str): Anthropic API key.
+        input_text (str): The comparison question to ask.
+        focus_plan_path: Path to the focus plan or uploaded file.
+        selected_summaries (list): List of selected summary documents.
+        display_placeholder: Streamlit placeholder for displaying results.
+    """
+    # Set the API keys
     os.environ["OPENAI_API_KEY"] = api_key
     os.environ["ANTHROPIC_API_KEY"] = anthropic_api_key
-    # Load the focus plan
 
-# Load focus documents or vector store
+    # Load focus documents
     if isinstance(focus_plan_path, st.runtime.uploaded_file_manager.UploadedFile):
+        # If focus_plan_path is an uploaded file
         focus_docs = load_documents_from_pdf(focus_plan_path)
     elif isinstance(focus_plan_path, str):
+        # If focus_plan_path is a file path
         focus_loader = PyPDFLoader(focus_plan_path)
         focus_docs = focus_loader.load()
+    else:
+        raise ValueError("Invalid focus plan input type. Must be an uploaded file or a file path.")
 
     # Concatenate selected summary documents
     summaries_directory = "CAPS_Summaries"
     summaries_content = ""
     for filename in selected_summaries:
-        with open(os.path.join(summaries_directory, f"{filename.replace(" Summary", "_Summary")}.md"), 'r') as file:
+        # Fix the filename by replacing ' Summary' with '_Summary'
+        summary_filename = f"{filename.replace(' Summary', '_Summary')}.md"
+        with open(os.path.join(summaries_directory, summary_filename), 'r') as file:
             summaries_content += file.read() + "\n\n"
 
     # Prepare the context
@@ -300,17 +448,15 @@ def compare_with_long_context(api_key, anthropic_api_key, input_text, focus_plan
 
     # Create the client and message
     client = anthropic.Anthropic(api_key=anthropic_api_key)
-    message = client.messages.create(
-        model="claude-3-5-sonnet-20241022",
-        max_tokens=1024,
-        messages=[
-            {"role": "user", "content": f"{input_text}\n\nFocus Document:\n{focus_context}\n\nSummaries:\n{summaries_content}"}
-        ]
+    response = client.completions.create(
+        model="claude-2",
+        max_tokens_to_sample=1024,
+        prompt=f"{input_text}\n\nFocus Document:\n{focus_context}\n\nSummaries:\n{summaries_content}"
     )
 
     # Display the answer
-    display_placeholder.markdown(f"**Answer:**\n{message.content}", unsafe_allow_html=True)
-
+    answer = response.completion
+    display_placeholder.markdown(f"**Answer:**\n{answer}", unsafe_allow_html=True)
 
 # Streamlit app layout with tabs
 st.title("Climate Policy Analysis Tool")
@@ -319,11 +465,21 @@ st.title("Climate Policy Analysis Tool")
 api_key = st.text_input("Enter your OpenAI API key:", type="password", key="openai_key")
 
 # Create tabs
-tab1, tab2, tab3, tab4, tab5 = st.tabs(["Summary Generation", "Multi-Plan QA (Shared Vectorstore)", "Multi-Plan QA (Multi-Vectorstore)", "Plan Comparison Tool", "Plan Comparison with Long Context Model"])
+tab1, tab2, tab3, tab4, tab5 = st.tabs([
+    "Summary Generation",
+    "Multi-Plan QA (Shared Vectorstore)",
+    "Multi-Plan QA (Multi-Vectorstore)",
+    "Plan Comparison Tool",
+    "Plan Comparison with Long Context Model"
+])
 
 # First tab: Summary Generation
 with tab1:
-    uploaded_file = st.file_uploader("Upload a Climate Action Plan in PDF format", type="pdf", key="upload_file")
+    uploaded_file = st.file_uploader(
+        "Upload a Climate Action Plan in PDF format",
+        type="pdf",
+        key="upload_file"
+    )
 
     prompt_file_path = "Prompts/summary_tool_system_prompt.md"
     questions_file_path = "Prompts/summary_tool_questions.md"
@@ -337,14 +493,19 @@ with tab1:
             display_placeholder = st.empty()
             with st.spinner("Processing..."):
                 try:
-                    results = process_pdf(api_key, uploaded_file, questions_file_path, prompt_file_path, display_placeholder)
-                    
+                    results = process_pdf(
+                        api_key,
+                        uploaded_file,
+                        questions_file_path,
+                        prompt_file_path,
+                        display_placeholder
+                    )
                     markdown_text = "\n".join(results)
-                    
+
                     # Use the uploaded file's name for the download file
                     base_name = os.path.splitext(uploaded_file.name)[0]
                     download_file_name = f"{base_name}_Summary.md"
-                    
+
                     st.download_button(
                         label="Download Results as Markdown",
                         data=markdown_text,
@@ -355,7 +516,7 @@ with tab1:
                 except Exception as e:
                     st.error(f"An error occurred: {e}")
 
-# Second tab: Multi-Plan QA
+# Second tab: Multi-Plan QA (Shared Vectorstore)
 with tab2:
     input_text = st.text_input("Ask a question:", key="multi_plan_input")
     if st.button("Ask", key="multi_plan_qa_button"):
@@ -375,7 +536,7 @@ with tab2:
                 except Exception as e:
                     st.error(f"An error occurred: {e}")
 
-
+# Third tab: Multi-Plan QA (Multi-Vectorstore)
 with tab3:
     user_input = st.text_input("Ask a question:", key="multi_vectorstore_input")
     if st.button("Ask", key="multi_vectorstore_qa_button"):
@@ -387,7 +548,7 @@ with tab3:
             display_placeholder3 = st.empty()
             with st.spinner("Processing..."):
                 try:
-                    multi_plan_qa_multi_vectorstore(
+                    process_multi_plan_qa_multi_vectorstore(
                         api_key,
                         user_input,
                         display_placeholder3
@@ -403,10 +564,18 @@ with tab4:
     vectorstore_documents = list_vector_store_documents()
 
     # Option to upload a new plan or select from existing vector stores
-    focus_option = st.radio("Choose a focus plan:", ("Select from existing vector stores", "Upload a new plan"), key="focus_option")
+    focus_option = st.radio(
+        "Choose a focus plan:",
+        ("Select from existing vector stores", "Upload a new plan"),
+        key="focus_option"
+    )
 
     if focus_option == "Upload a new plan":
-        focus_uploaded_file = st.file_uploader("Upload a Climate Action Plan to compare", type="pdf", key="focus_upload")
+        focus_uploaded_file = st.file_uploader(
+            "Upload a Climate Action Plan to compare",
+            type="pdf",
+            key="focus_upload"
+        )
         if focus_uploaded_file is not None:
             # Directly use the uploaded file
             focus_input = focus_uploaded_file
@@ -414,21 +583,49 @@ with tab4:
             focus_input = None
     else:
         # Select a focus plan from existing vector stores
-        selected_focus_plan = st.selectbox("Select a focus plan:", vectorstore_documents, key="select_focus_plan")
-        focus_input = os.path.join("Individual_All_Vectorstores", f"{selected_focus_plan.replace(" Summary", "_Summary")}_vectorstore")
+        selected_focus_plan = st.selectbox(
+            "Select a focus plan:",
+            vectorstore_documents,
+            key="select_focus_plan"
+        )
+        focus_input = os.path.join(
+            "Individual_All_Vectorstores",
+            f"{selected_focus_plan.replace(' Summary', '_Summary')}_vectorstore"
+        )
 
     # Option to upload comparison documents or select from existing vector stores
-    comparison_option = st.radio("Choose comparison documents:", ("Select from existing vector stores", "Upload new documents"), key="comparison_option")
+    comparison_option = st.radio(
+        "Choose comparison documents:",
+        ("Select from existing vector stores", "Upload new documents"),
+        key="comparison_option"
+    )
 
     if comparison_option == "Upload new documents":
-        comparison_files = st.file_uploader("Upload comparison documents", type="pdf", accept_multiple_files=True, key="comparison_files")
+        comparison_files = st.file_uploader(
+            "Upload comparison documents",
+            type="pdf",
+            accept_multiple_files=True,
+            key="comparison_files"
+        )
         comparison_inputs = comparison_files
     else:
         # Select comparison documents from existing vector stores
-        selected_comparison_plans = st.multiselect("Select comparison documents:", vectorstore_documents, key="select_comparison_plans")
-        comparison_inputs = [os.path.join("Individual_All_Vectorstores", f"{doc.replace(" Summary", "_Summary")}_vectorstore") for doc in selected_comparison_plans]
+        selected_comparison_plans = st.multiselect(
+            "Select comparison documents:",
+            vectorstore_documents,
+            key="select_comparison_plans"
+        )
+        comparison_inputs = [
+            os.path.join(
+                "Individual_All_Vectorstores",
+                f"{doc.replace(' Summary', '_Summary')}_vectorstore"
+            ) for doc in selected_comparison_plans
+        ]
 
-    input_text = st.text_input("Ask a comparison question:", key="comparison_input")
+    input_text = st.text_input(
+        "Ask a comparison question:",
+        key="comparison_input"
+    )
 
     if st.button("Compare", key="compare_button"):
         if not api_key:
@@ -444,8 +641,13 @@ with tab4:
             with st.spinner("Processing..."):
                 try:
                     # Call the process_one_to_many_query function
-                    process_one_to_many_query(api_key, focus_input, comparison_inputs, input_text, display_placeholder4)
-                    
+                    process_one_to_many_query(
+                        api_key,
+                        focus_input,
+                        comparison_inputs,
+                        input_text,
+                        display_placeholder4
+                    )
                 except Exception as e:
                     st.error(f"An error occurred: {e}")
 
@@ -454,30 +656,56 @@ with tab5:
     st.header("Plan Comparison with Long Context Model")
 
     # Anthropics API Key Input
-    anthropic_api_key = st.text_input("Enter your Anthropic API key:", type="password", key="anthropic_key")
+    anthropic_api_key = st.text_input(
+        "Enter your Anthropic API key:",
+        type="password",
+        key="anthropic_key"
+    )
 
     # Option to upload a new plan or select from a list
-    focus_option = st.radio("Choose a focus plan:", ("Select from existing plans", "Upload a new plan"), key="focus_option_long_context")
+    focus_option = st.radio(
+        "Choose a focus plan:",
+        ("Select from existing plans", "Upload a new plan"),
+        key="focus_option_long_context"
+    )
 
     if focus_option == "Upload a new plan":
-        focus_uploaded_file = st.file_uploader("Upload a Climate Action Plan to compare", type="pdf", key="focus_upload_long_context")
+        focus_uploaded_file = st.file_uploader(
+            "Upload a Climate Action Plan to compare",
+            type="pdf",
+            key="focus_upload_long_context"
+        )
         if focus_uploaded_file is not None:
             # Directly use the uploaded file
             focus_plan_path = focus_uploaded_file
         else:
             focus_plan_path = None
     else:
-        # Select a focus plan from existing vector stores
+        # List of existing plans in CAPS
         plan_list = [f.replace(".pdf", "") for f in os.listdir("CAPS") if f.endswith('.pdf')]
-        selected_focus_plan = st.selectbox("Select a focus plan:", plan_list, key="select_focus_plan_long_context")
+        selected_focus_plan = st.selectbox(
+            "Select a focus plan:",
+            plan_list,
+            key="select_focus_plan_long_context"
+        )
         focus_plan_path = os.path.join("CAPS", f"{selected_focus_plan}.pdf")
 
     # List available summary documents for selection
     summaries_directory = "CAPS_Summaries"
-    summary_files = [f.replace(".md", "").replace("_", " ") for f in os.listdir(summaries_directory) if f.endswith('.md')]
-    selected_summaries = st.multiselect("Select summary documents for comparison:", summary_files, key="selected_summaries")
+    summary_files = [
+        f.replace(".md", "").replace("_", " ")
+        for f in os.listdir(summaries_directory) if f.endswith('.md')
+    ]
+    selected_summaries = st.multiselect(
+        "Select summary documents for comparison:",
+        summary_files,
+        key="selected_summaries"
+    )
 
-    input_text = st.text_input("Ask a comparison question:", key="comparison_input_long_context")
+    input_text = st.text_input(
+        "Ask a comparison question:",
+        key="comparison_input_long_context"
+    )
 
     if st.button("Compare with Long Context", key="compare_button_long_context"):
         if not api_key:
@@ -492,6 +720,13 @@ with tab5:
             display_placeholder = st.empty()
             with st.spinner("Processing..."):
                 try:
-                    compare_with_long_context(api_key, anthropic_api_key, input_text, focus_plan_path, selected_summaries, display_placeholder)
+                    compare_with_long_context(
+                        api_key,
+                        anthropic_api_key,
+                        input_text,
+                        focus_plan_path,
+                        selected_summaries,
+                        display_placeholder
+                    )
                 except Exception as e:
-                    st.error(f"An error occurred: {e}")
+                    st.error(f"An error occurred: {e}")