Update app.py

app.py

@@ -1,465 +1,590 @@
-#############################################################################################################################
-# Filename   : app.py
-# Description: A Streamlit application to showcase how RAG works.
-# Author     : Georgios Ioannou
-#
-# Copyright © 2024 by Georgios Ioannou
-#############################################################################################################################
-# Import libraries.
+# #############################################################################################################################
+# # Filename   : app.py
+# # Description: A Streamlit application to showcase how RAG works.
+# # Author     : Georgios Ioannou
+# #
+# # Copyright © 2024 by Georgios Ioannou
+# #############################################################################################################################
+# app.py
 import os
+import json
 import streamlit as st
+from typing import List, Dict, Any
+from urllib.parse import quote_plus
+from pymongo import MongoClient
+from PyPDF2 import PdfReader
 
-from dotenv import load_dotenv, find_dotenv
-from huggingface_hub import InferenceClient
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain_community.vectorstores import MongoDBAtlasVectorSearch
 from langchain.prompts import PromptTemplate
 from langchain.schema import Document
-from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
-# from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-from langchain.embeddings import OpenAIEmbeddings
-from langchain_community.vectorstores import MongoDBAtlasVectorSearch
-from pymongo import MongoClient
-from pymongo.collection import Collection
-from typing import Dict, Any
-from langchain.chat_models import ChatOpenAI
-
-
-
-#############################################################################################################################
-
-
-class RAGQuestionAnswering:
-    def __init__(self):
-        """
-        Parameters
-        ----------
-        None
-
-        Output
-        ------
-        None
-
-        Purpose
-        -------
-        Initializes the RAG Question Answering system by setting up configuration
-        and loading environment variables.
-
-        Assumptions
-        -----------
-        - Expects .env file with MONGO_URI and HF_TOKEN
-        - Requires proper MongoDB setup with vector search index
-        - Needs connection to Hugging Face API
-
-        Notes
-        -----
-        This is the main class that handles all RAG operations
-        """
-        self.load_environment()
-        self.setup_mongodb()
-        self.setup_embedding_model()
-        self.setup_vector_search()
-        self.setup_rag_chain()
-
-    def load_environment(self) -> None:
-        """
-        Parameters
-        ----------
-        None
-
-        Output
-        ------
-        None
-
-        Purpose
-        -------
-        Loads environment variables from .env file and sets up configuration constants.
-
-        Assumptions
-        -----------
-        Expects a .env file with MONGO_URI and HF_TOKEN defined
-
-        Notes
-        -----
-        Will stop the application if required environment variables are missing
-        """
-
-        load_dotenv(find_dotenv())
-        self.MONGO_URI = os.getenv("MONGO_URI")
-        # self.HF_TOKEN = os.getenv("HF_TOKEN")
-        self.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-
-        if not self.MONGO_URI or not self.OPENAI_API_KEY:
-            st.error("Please ensure MONGO_URI and OPENAI_API_KEY are set in your .env file")
-            st.stop()
-
-        # MongoDB configuration.
-        self.DB_NAME = "txts"
-        self.COLLECTION_NAME = "txts_collection"
-        self.VECTOR_SEARCH_INDEX = "vector_index"
-
-    def setup_mongodb(self) -> None:
-        """
-        Parameters
-        ----------
-        None
-
-        Output
-        ------
-        None
-
-        Purpose
-        -------
-        Initializes the MongoDB connection and sets up the collection.
-
-        Assumptions
-        -----------
-        - Valid MongoDB URI is available
-        - Database and collection exist in MongoDB Atlas
-
-        Notes
-        -----
-        Uses st.cache_resource for efficient connection management
-        """
-
-        @st.cache_resource
-        def init_mongodb() -> Collection:
-            cluster = MongoClient(self.MONGO_URI)
-            return cluster[self.DB_NAME][self.COLLECTION_NAME]
-
-        self.mongodb_collection = init_mongodb()
-
-    def setup_embedding_model(self) -> None:
-        """
-        Parameters
-        ----------
-        None
-
-        Output
-        ------
-        None
-
-        Purpose
-        -------
-        Initializes the embedding model for vector search.
-
-        Assumptions
-        -----------
-        - Valid Hugging Face API token
-        - Internet connection to access the model
-
-        Notes
-        -----
-        Uses the all-mpnet-base-v2 model from sentence-transformers
-        """
-
-        # @st.cache_resource
-        # def init_embedding_model() -> HuggingFaceInferenceAPIEmbeddings:
-        #     return HuggingFaceInferenceAPIEmbeddings(
-        #         api_key=self.HF_TOKEN,
-        #         model_name="sentence-transformers/all-mpnet-base-v2",
-        #     )
-
-        @st.cache_resource
-        def init_embedding_model() -> OpenAIEmbeddings:
-            return OpenAIEmbeddings(model="text-embedding-3-small", openai_api_key=self.OPENAI_API_KEY)
-
-        self.embedding_model = init_embedding_model()
-
-    def setup_vector_search(self) -> None:
-        """
-        Parameters
-        ----------
-        None
-
-        Output
-        ------
-        None
-
-        Purpose
-        -------
-        Sets up the vector search functionality using MongoDB Atlas.
-
-        Assumptions
-        -----------
-        - MongoDB Atlas vector search index is properly configured
-        - Valid embedding model is initialized
-
-        Notes
-        -----
-        Creates a retriever with similarity search and score threshold
-        """
-
-        @st.cache_resource
-        def init_vector_search() -> MongoDBAtlasVectorSearch:
-            return MongoDBAtlasVectorSearch.from_connection_string(
-                connection_string=self.MONGO_URI,
-                namespace=f"{self.DB_NAME}.{self.COLLECTION_NAME}",
-                embedding=self.embedding_model,
-                index_name=self.VECTOR_SEARCH_INDEX,
-            )
-
-        self.vector_search = init_vector_search()
-        self.retriever = self.vector_search.as_retriever(
-            search_type="similarity", search_kwargs={"k": 10, "score_threshold": 0.85}
-        )
-
-    def format_docs(self, docs: list[Document]) -> str:
-        """
-        Parameters
-        ----------
-        **docs:** list[Document] - List of documents to be formatted
-
-        Output
-        ------
-        str: Formatted string containing concatenated document content
-
-        Purpose
-        -------
-        Formats the retrieved documents into a single string for processing
-
-        Assumptions
-        -----------
-        Documents have page_content attribute
-
-        Notes
-        -----
-        Joins documents with double newlines for better readability
-        """
-
-        return "\n\n".join(doc.page_content for doc in docs)
-
-    # def generate_response(self, input_dict: Dict[str, Any]) -> str:
-    #     """
-    #     Parameters
-    #     ----------
-    #     **input_dict:** Dict[str, Any] - Dictionary containing context and question
-
-    #     Output
-    #     ------
-    #     str: Generated response from the model
-
-    #     Purpose
-    #     -------
-    #     Generates a response using the Hugging Face model based on context and question
-
-    #     Assumptions
-    #     -----------
-    #     - Valid Hugging Face API token
-    #     - Input dictionary contains 'context' and 'question' keys
-
-    #     Notes
-    #     -----
-    #     Uses Qwen2.5-1.5B-Instruct model with controlled temperature
-    #     """
-    #     hf_client = InferenceClient(api_key=self.HF_TOKEN)
-    #     formatted_prompt = self.prompt.format(**input_dict)
-
-    #     response = hf_client.chat.completions.create(
-    #         model="Qwen/Qwen2.5-1.5B-Instruct",
-    #         messages=[
-    #             {"role": "system", "content": formatted_prompt},
-    #             {"role": "user", "content": input_dict["question"]},
-    #         ],
-    #         max_tokens=1000,
-    #         temperature=0.2,
-    #     )
-
-    #     return response.choices[0].message.content
-    from langchain.chat_models import ChatOpenAI
-    from langchain.schema.messages import SystemMessage, HumanMessage
-
-    def generate_response(self, input_dict: Dict[str, Any]) -> str:
-        llm = ChatOpenAI(
-        model="gpt-4",  # or "gpt-3.5-turbo"
-        temperature=0.2,
-        openai_api_key=self.OPENAI_API_KEY,
-    )
-    
-    messages = [
-        SystemMessage(content=self.prompt.format(**input_dict)),
-        HumanMessage(content=input_dict["question"]),
-    ]
-    
-    return llm(messages).content
+from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
+from huggingface_hub import InferenceClient
 
+# =================== Secure Env via Hugging Face Secrets ===================
+user = quote_plus(os.getenv("MONGO_USERNAME"))
+password = quote_plus(os.getenv("MONGO_PASSWORD"))
+cluster = os.getenv("MONGO_CLUSTER")
+db_name = os.getenv("MONGO_DB_NAME", "files")
+collection_name = os.getenv("MONGO_COLLECTION", "files_collection")
+index_name = os.getenv("MONGO_VECTOR_INDEX", "vector_index")
 
-    def setup_rag_chain(self) -> None:
-        """
-        Parameters
-        ----------
-        None
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-        Output
-        ------
-        None
+MONGO_URI = f"mongodb+srv://{user}:{password}@{cluster}/{db_name}?retryWrites=true&w=majority"
 
-        Purpose
-        -------
-        Sets up the RAG chain for processing questions and generating answers
+# =================== Prompt ===================
+grantbuddy_prompt = PromptTemplate.from_template(
+    """You are Grant Buddy, a specialized assistant helping nonprofits apply for grants. 
+Always align answers with the nonprofit’s mission to combat systemic poverty through education, technology, and social innovation.
 
-        Assumptions
-        -----------
-        Retriever and response generator are properly initialized
+Use the following context to answer the question. Be concise and mission-aligned.
 
-        Notes
-        -----
-        Creates a chain that combines retrieval and response generation
-        """
+CONTEXT:
+{context}
 
-        self.prompt = PromptTemplate.from_template(
-            """Use the following pieces of context to answer the question at the end.
+QUESTION:
+{question}
 
-            START OF CONTEXT:
-            {context}
-            END OF CONTEXT:
-            
-            START OF QUESTION:
-            {question}
-            END OF QUESTION:
-
-            If you do not know the answer, just say that you do not know.
-            NEVER assume things.
-            """
-        )
-
-        self.rag_chain = {
-            "context": self.retriever | RunnableLambda(self.format_docs),
-            "question": RunnablePassthrough(),
-        } | RunnableLambda(self.generate_response)
-
-    def process_question(self, question: str) -> str:
-        """
-        Parameters
-        ----------
-        **question:** str - The user's question to be answered
-
-        Output
-        ------
-        str: The generated answer to the question
-
-        Purpose
-        -------
-        Processes a user question through the RAG chain and returns an answer
-
-        Assumptions
-        -----------
-        - Question is a non-empty string
-        - RAG chain is properly initialized
-
-        Notes
-        -----
-        Main interface for question-answering functionality
-        """
-
-        return self.rag_chain.invoke(question)
-
-
-#############################################################################################################################
-def setup_streamlit_ui() -> None:
-    """
-    Parameters
-    ----------
-    None
-
-    Output
-    ------
-    None
-
-    Purpose
-    -------
-    Sets up the Streamlit user interface with proper styling and layout
-
-    Assumptions
-    -----------
-    - CSS file exists at ./static/styles/style.css
-    - Image file exists at ./static/images/ctp.png
-
-    Notes
-    -----
-    Handles all UI-related setup and styling
-    """
-
-    st.set_page_config(page_title="RAG Question Answering", page_icon="🤖")
-
-    # Load CSS.
-    with open("./static/styles/style.css") as f:
-        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
-
-    # Title and subtitles.
-    st.markdown(
-        '<h1 align="center" style="font-family: monospace; font-size: 2.1rem; margin-top: -4rem">RAG Question Answering</h1>',
-        unsafe_allow_html=True,
-    )
-    st.markdown(
-        '<h3 align="center" style="font-family: monospace; font-size: 1.5rem; margin-top: -2rem">Using Zoom Closed Captioning From The Lectures</h3>',
-        unsafe_allow_html=True,
+Respond truthfully. If the answer is not available, say "This information is not available in the current context."
+"""
+)
+
+# =================== Vector Search Setup ===================
+@st.cache_resource
+def init_vector_search() -> MongoDBAtlasVectorSearch:
+    embedding_model = HuggingFaceInferenceAPIEmbeddings(
+        api_key=HF_TOKEN,
+        model_name="sentence-transformers/all-mpnet-base-v2"
     )
-    st.markdown(
-        '<h2 align="center" style="font-family: monospace; font-size: 1.5rem; margin-top: 0rem">CUNY Tech Prep Tutorial 5</h2>',
-        unsafe_allow_html=True,
+    return MongoDBAtlasVectorSearch.from_connection_string(
+        connection_string=MONGO_URI,
+        namespace=f"{db_name}.{collection_name}",
+        embedding=embedding_model,
+        index_name=index_name,
     )
 
-    # Display logo.
-    left_co, cent_co, last_co = st.columns(3)
-    with cent_co:
-        st.image("./static/images/ctp.png")
-
-
-#############################################################################################################################
+# =================== Format Retrieved Chunks ===================
+def format_docs(docs: List[Document]) -> str:
+    return "\n\n".join(doc.page_content or doc.metadata.get("content", "") for doc in docs)
+
+# =================== Generate Response from Hugging Face Model ===================
+def generate_response(input_dict: Dict[str, Any]) -> str:
+    client = InferenceClient(api_key=HF_TOKEN)
+    prompt = grantbuddy_prompt.format(**input_dict)
+
+    response = client.chat.completions.create(
+        model="Qwen/Qwen2.5-1.5B-Instruct",
+        messages=[
+            {"role": "system", "content": prompt},
+            {"role": "user", "content": input_dict["question"]},
+        ],
+        max_tokens=1000,
+        temperature=0.2,
+    )
+    return response.choices[0].message.content
 
+# =================== RAG Chain ===================
+def get_rag_chain(retriever):
+    return {
+        "context": retriever | RunnableLambda(format_docs),
+        "question": RunnablePassthrough()
+    } | RunnableLambda(generate_response)
 
+# =================== Streamlit UI ===================
 def main():
-    """
-    Parameters
-    ----------
-    None
-
-    Output
-    ------
-    None
-
-    Purpose
-    -------
-    Main function that runs the Streamlit application
-
-    Assumptions
-    -----------
-    All required environment variables and files are present
-
-    Notes
-    -----
-    Entry point for the application
-    """
-
-    # Setup UI.
-    setup_streamlit_ui()
-
-    # Initialize RAG system.
-    rag_system = RAGQuestionAnswering()
-
-    # Create input elements.
-    query = st.text_input("Question:", key="question_input")
-
-    # Handle submission.
-    if st.button("Submit", type="primary"):
-        if query:
-            with st.spinner("Generating response..."):
-                response = rag_system.process_question(query)
-                st.text_area("Answer:", value=response, height=200, disabled=True)
-        else:
+    st.set_page_config(page_title="Grant Buddy RAG", page_icon="🤖")
+    st.title("🤖 Grant Buddy: Grant-Writing Assistant")
+
+    uploaded_file = st.file_uploader("Upload PDF or TXT for extra context (optional)", type=["pdf", "txt"])
+    uploaded_text = ""
+    if uploaded_file:
+        if uploaded_file.name.endswith(".pdf"):
+            reader = PdfReader(uploaded_file)
+            uploaded_text = "\n".join([page.extract_text() for page in reader.pages])
+        elif uploaded_file.name.endswith(".txt"):
+            uploaded_text = uploaded_file.read().decode("utf-8")
+
+    retriever = init_vector_search().as_retriever(search_kwargs={"k": 10, "score_threshold": 0.75})
+    rag_chain = get_rag_chain(retriever)
+
+    query = st.text_input("Ask a grant-related question")
+    if st.button("Submit"):
+        if not query:
             st.warning("Please enter a question.")
+            return
 
-    # Add GitHub link.
-    st.markdown(
-        """
-        <p align="center" style="font-family: monospace; color: #FAF9F6; font-size: 1rem;">
-        <b>Check out our <a href="https://github.com/GeorgiosIoannouCoder/" style="color: #FAF9F6;">GitHub repository</a></b>
-        </p>
-        """,
-        unsafe_allow_html=True,
-    )
+        full_query = f"{query}\n\nAdditional context:\n{uploaded_text}" if uploaded_text else query
+        with st.spinner("Thinking..."):
+            response = rag_chain.invoke(full_query)
+            st.text_area("Grant Buddy says:", value=response, height=250, disabled=True)
+
+        with st.expander("🔍 Retrieved Chunks"):
+            context_docs = retriever.get_relevant_documents(full_query)
+            for doc in context_docs:
+                st.markdown(f"**Chunk ID:** {doc.metadata.get('chunk_id', 'unknown')}")
+                st.markdown(doc.page_content[:700] + "...")
+                st.markdown("---")
 
 
-#############################################################################################################################
 if __name__ == "__main__":
     main()
+
+# # Import libraries.
+# import os
+# import streamlit as st
+
+# from dotenv import load_dotenv, find_dotenv
+# from huggingface_hub import InferenceClient
+# from langchain.prompts import PromptTemplate
+# from langchain.schema import Document
+# from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
+# # from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+# from langchain.embeddings import OpenAIEmbeddings
+# from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+# from pymongo import MongoClient
+# from pymongo.collection import Collection
+# from typing import Dict, Any
+# from langchain.chat_models import ChatOpenAI
+
+
+
+# #############################################################################################################################
+
+
+# class RAGQuestionAnswering:
+#     def __init__(self):
+#         """
+#         Parameters
+#         ----------
+#         None
+
+#         Output
+#         ------
+#         None
+
+#         Purpose
+#         -------
+#         Initializes the RAG Question Answering system by setting up configuration
+#         and loading environment variables.
+
+#         Assumptions
+#         -----------
+#         - Expects .env file with MONGO_URI and HF_TOKEN
+#         - Requires proper MongoDB setup with vector search index
+#         - Needs connection to Hugging Face API
+
+#         Notes
+#         -----
+#         This is the main class that handles all RAG operations
+#         """
+#         self.load_environment()
+#         self.setup_mongodb()
+#         self.setup_embedding_model()
+#         self.setup_vector_search()
+#         self.setup_rag_chain()
+
+#     def load_environment(self) -> None:
+#         """
+#         Parameters
+#         ----------
+#         None
+
+#         Output
+#         ------
+#         None
+
+#         Purpose
+#         -------
+#         Loads environment variables from .env file and sets up configuration constants.
+
+#         Assumptions
+#         -----------
+#         Expects a .env file with MONGO_URI and HF_TOKEN defined
+
+#         Notes
+#         -----
+#         Will stop the application if required environment variables are missing
+#         """
+
+#         load_dotenv(find_dotenv())
+#         self.MONGO_URI = os.getenv("MONGO_URI")
+#         # self.HF_TOKEN = os.getenv("HF_TOKEN")
+#         self.OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+
+#         if not self.MONGO_URI or not self.OPENAI_API_KEY:
+#             st.error("Please ensure MONGO_URI and OPENAI_API_KEY are set in your .env file")
+#             st.stop()
+
+#         # MongoDB configuration.
+#         self.DB_NAME = "txts"
+#         self.COLLECTION_NAME = "txts_collection"
+#         self.VECTOR_SEARCH_INDEX = "vector_index"
+
+#     def setup_mongodb(self) -> None:
+#         """
+#         Parameters
+#         ----------
+#         None
+
+#         Output
+#         ------
+#         None
+
+#         Purpose
+#         -------
+#         Initializes the MongoDB connection and sets up the collection.
+
+#         Assumptions
+#         -----------
+#         - Valid MongoDB URI is available
+#         - Database and collection exist in MongoDB Atlas
+
+#         Notes
+#         -----
+#         Uses st.cache_resource for efficient connection management
+#         """
+
+#         @st.cache_resource
+#         def init_mongodb() -> Collection:
+#             cluster = MongoClient(self.MONGO_URI)
+#             return cluster[self.DB_NAME][self.COLLECTION_NAME]
+
+#         self.mongodb_collection = init_mongodb()
+
+#     def setup_embedding_model(self) -> None:
+#         """
+#         Parameters
+#         ----------
+#         None
+
+#         Output
+#         ------
+#         None
+
+#         Purpose
+#         -------
+#         Initializes the embedding model for vector search.
+
+#         Assumptions
+#         -----------
+#         - Valid Hugging Face API token
+#         - Internet connection to access the model
+
+#         Notes
+#         -----
+#         Uses the all-mpnet-base-v2 model from sentence-transformers
+#         """
+
+#         # @st.cache_resource
+#         # def init_embedding_model() -> HuggingFaceInferenceAPIEmbeddings:
+#         #     return HuggingFaceInferenceAPIEmbeddings(
+#         #         api_key=self.HF_TOKEN,
+#         #         model_name="sentence-transformers/all-mpnet-base-v2",
+#         #     )
+
+#         @st.cache_resource
+#         def init_embedding_model() -> OpenAIEmbeddings:
+#             return OpenAIEmbeddings(model="text-embedding-3-small", openai_api_key=self.OPENAI_API_KEY)
+
+#         self.embedding_model = init_embedding_model()
+
+#     def setup_vector_search(self) -> None:
+#         """
+#         Parameters
+#         ----------
+#         None
+
+#         Output
+#         ------
+#         None
+
+#         Purpose
+#         -------
+#         Sets up the vector search functionality using MongoDB Atlas.
+
+#         Assumptions
+#         -----------
+#         - MongoDB Atlas vector search index is properly configured
+#         - Valid embedding model is initialized
+
+#         Notes
+#         -----
+#         Creates a retriever with similarity search and score threshold
+#         """
+
+#         @st.cache_resource
+#         def init_vector_search() -> MongoDBAtlasVectorSearch:
+#             return MongoDBAtlasVectorSearch.from_connection_string(
+#                 connection_string=self.MONGO_URI,
+#                 namespace=f"{self.DB_NAME}.{self.COLLECTION_NAME}",
+#                 embedding=self.embedding_model,
+#                 index_name=self.VECTOR_SEARCH_INDEX,
+#             )
+
+#         self.vector_search = init_vector_search()
+#         self.retriever = self.vector_search.as_retriever(
+#             search_type="similarity", search_kwargs={"k": 10, "score_threshold": 0.85}
+#         )
+
+#     def format_docs(self, docs: list[Document]) -> str:
+#         """
+#         Parameters
+#         ----------
+#         **docs:** list[Document] - List of documents to be formatted
+
+#         Output
+#         ------
+#         str: Formatted string containing concatenated document content
+
+#         Purpose
+#         -------
+#         Formats the retrieved documents into a single string for processing
+
+#         Assumptions
+#         -----------
+#         Documents have page_content attribute
+
+#         Notes
+#         -----
+#         Joins documents with double newlines for better readability
+#         """
+
+#         return "\n\n".join(doc.page_content for doc in docs)
+
+#     # def generate_response(self, input_dict: Dict[str, Any]) -> str:
+#     #     """
+#     #     Parameters
+#     #     ----------
+#     #     **input_dict:** Dict[str, Any] - Dictionary containing context and question
+
+#     #     Output
+#     #     ------
+#     #     str: Generated response from the model
+
+#     #     Purpose
+#     #     -------
+#     #     Generates a response using the Hugging Face model based on context and question
+
+#     #     Assumptions
+#     #     -----------
+#     #     - Valid Hugging Face API token
+#     #     - Input dictionary contains 'context' and 'question' keys
+
+#     #     Notes
+#     #     -----
+#     #     Uses Qwen2.5-1.5B-Instruct model with controlled temperature
+#     #     """
+#     #     hf_client = InferenceClient(api_key=self.HF_TOKEN)
+#     #     formatted_prompt = self.prompt.format(**input_dict)
+
+#     #     response = hf_client.chat.completions.create(
+#     #         model="Qwen/Qwen2.5-1.5B-Instruct",
+#     #         messages=[
+#     #             {"role": "system", "content": formatted_prompt},
+#     #             {"role": "user", "content": input_dict["question"]},
+#     #         ],
+#     #         max_tokens=1000,
+#     #         temperature=0.2,
+#     #     )
+
+#     #     return response.choices[0].message.content
+#     from langchain.chat_models import ChatOpenAI
+#     from langchain.schema.messages import SystemMessage, HumanMessage
+
+#     def generate_response(self, input_dict: Dict[str, Any]) -> str:
+#         llm = ChatOpenAI(
+#         model="gpt-4",  # or "gpt-3.5-turbo"
+#         temperature=0.2,
+#         openai_api_key=self.OPENAI_API_KEY,
+#     )
+    
+#     messages = [
+#         SystemMessage(content=self.prompt.format(**input_dict)),
+#         HumanMessage(content=input_dict["question"]),
+#     ]
+    
+#     return llm(messages).content
+
+
+#     def setup_rag_chain(self) -> None:
+#         """
+#         Parameters
+#         ----------
+#         None
+
+#         Output
+#         ------
+#         None
+
+#         Purpose
+#         -------
+#         Sets up the RAG chain for processing questions and generating answers
+
+#         Assumptions
+#         -----------
+#         Retriever and response generator are properly initialized
+
+#         Notes
+#         -----
+#         Creates a chain that combines retrieval and response generation
+#         """
+
+#         self.prompt = PromptTemplate.from_template(
+#             """Use the following pieces of context to answer the question at the end.
+
+#             START OF CONTEXT:
+#             {context}
+#             END OF CONTEXT:
+            
+#             START OF QUESTION:
+#             {question}
+#             END OF QUESTION:
+
+#             If you do not know the answer, just say that you do not know.
+#             NEVER assume things.
+#             """
+#         )
+
+#         self.rag_chain = {
+#             "context": self.retriever | RunnableLambda(self.format_docs),
+#             "question": RunnablePassthrough(),
+#         } | RunnableLambda(self.generate_response)
+
+#     def process_question(self, question: str) -> str:
+#         """
+#         Parameters
+#         ----------
+#         **question:** str - The user's question to be answered
+
+#         Output
+#         ------
+#         str: The generated answer to the question
+
+#         Purpose
+#         -------
+#         Processes a user question through the RAG chain and returns an answer
+
+#         Assumptions
+#         -----------
+#         - Question is a non-empty string
+#         - RAG chain is properly initialized
+
+#         Notes
+#         -----
+#         Main interface for question-answering functionality
+#         """
+
+#         return self.rag_chain.invoke(question)
+
+
+# #############################################################################################################################
+# def setup_streamlit_ui() -> None:
+#     """
+#     Parameters
+#     ----------
+#     None
+
+#     Output
+#     ------
+#     None
+
+#     Purpose
+#     -------
+#     Sets up the Streamlit user interface with proper styling and layout
+
+#     Assumptions
+#     -----------
+#     - CSS file exists at ./static/styles/style.css
+#     - Image file exists at ./static/images/ctp.png
+
+#     Notes
+#     -----
+#     Handles all UI-related setup and styling
+#     """
+
+#     st.set_page_config(page_title="RAG Question Answering", page_icon="🤖")
+
+#     # Load CSS.
+#     with open("./static/styles/style.css") as f:
+#         st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+#     # Title and subtitles.
+#     st.markdown(
+#         '<h1 align="center" style="font-family: monospace; font-size: 2.1rem; margin-top: -4rem">RAG Question Answering</h1>',
+#         unsafe_allow_html=True,
+#     )
+#     st.markdown(
+#         '<h3 align="center" style="font-family: monospace; font-size: 1.5rem; margin-top: -2rem">Using Zoom Closed Captioning From The Lectures</h3>',
+#         unsafe_allow_html=True,
+#     )
+#     st.markdown(
+#         '<h2 align="center" style="font-family: monospace; font-size: 1.5rem; margin-top: 0rem">CUNY Tech Prep Tutorial 5</h2>',
+#         unsafe_allow_html=True,
+#     )
+
+#     # Display logo.
+#     left_co, cent_co, last_co = st.columns(3)
+#     with cent_co:
+#         st.image("./static/images/ctp.png")
+
+
+# #############################################################################################################################
+
+
+# def main():
+#     """
+#     Parameters
+#     ----------
+#     None
+
+#     Output
+#     ------
+#     None
+
+#     Purpose
+#     -------
+#     Main function that runs the Streamlit application
+
+#     Assumptions
+#     -----------
+#     All required environment variables and files are present
+
+#     Notes
+#     -----
+#     Entry point for the application
+#     """
+
+#     # Setup UI.
+#     setup_streamlit_ui()
+
+#     # Initialize RAG system.
+#     rag_system = RAGQuestionAnswering()
+
+#     # Create input elements.
+#     query = st.text_input("Question:", key="question_input")
+
+#     # Handle submission.
+#     if st.button("Submit", type="primary"):
+#         if query:
+#             with st.spinner("Generating response..."):
+#                 response = rag_system.process_question(query)
+#                 st.text_area("Answer:", value=response, height=200, disabled=True)
+#         else:
+#             st.warning("Please enter a question.")
+
+#     # Add GitHub link.
+#     st.markdown(
+#         """
+#         <p align="center" style="font-family: monospace; color: #FAF9F6; font-size: 1rem;">
+#         <b>Check out our <a href="https://github.com/GeorgiosIoannouCoder/" style="color: #FAF9F6;">GitHub repository</a></b>
+#         </p>
+#         """,
+#         unsafe_allow_html=True,
+#     )
+
+
+# #############################################################################################################################
+# if __name__ == "__main__":
+#     main()