Upload main.py

main.py

@@ -1,320 +1,335 @@
-from langchain_openai import OpenAIEmbeddings
-from langchain_openai import ChatOpenAI
-from langchain_chroma import Chroma
-import chromadb
-from chromadb.config import Settings
-from langchain_core.prompts import ChatPromptTemplate
-from typing import List
-
-from langchain_core.documents import Document
-from langchain_core.runnables import RunnablePassthrough
-from langchain_core.output_parsers import XMLOutputParser
-import gradio as gr
-import pandas as pd
-import logging
-from langchain_core.exceptions import OutputParserException
-import os
-from dotenv import load_dotenv
-
-from sympy.codegen.ast import continue_
-
-import azure.cosmos.cosmos_client as cosmos_client
-import azure.cosmos.exceptions as exceptions
-from azure.cosmos.partition_key import PartitionKey
-
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_core.vectorstores import InMemoryVectorStore
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.chains import create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.runnables import RunnableLambda
-import datetime
-import uuid
-import hashlib
-load_dotenv()
-# Constants
-PERSIST_DIRECTORY = "chroma_store"
-K_VALUE = 5
-
-xml_system = """You're a helpful AI assistant. Given a user question and some scientific literature 
-documents which highlight research on different roof cover materials (e.g., asphalt shingles, metal, tile) 
-and their performance against natural hazards(e.g., wind, hail), answer the user 
-question. 
-You’re a helpful AI assistant. Given a user question and scientific literature on various roof cover materials (e.g., asphalt shingles, metal, tile) 
-and their performance against natural hazards (e.g., wind, hail), provide clear, concise, and informed answers without unnecessary fluff.
-	
-When addressing questions about ‘what is the best roof,’ consider the following factors:
-	•	Geography, aesthetic preferences, budget, frequency of weather-related hazards, roof cover performance, and how performance changes with age.
-	•	For the insurance industry, the ‘best roof’ depends on the specific hazards (their location and frequency), performance expectations and predictability, and the cost of materials.
-	
-If none of the articles answer the question, simply say that there are no articles relevant to your inquiry.
-Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that 
-justifies the answer and the ID and also Source Name of the quote article. Return a citation for every quote across all articles 
-that justify the answer. Use the following format for your final output:
-<cited_answer>
-    <answer></answer>
-    <citations>
-        <citation><source_id></source_id><source></source><quote></quote></citation>
-        <citation><source_id></source_id><source></source><quote></quote></citation>
-        ...
-    </citations>
-</cited_answer>
-Here are the articles:{context}"""
-
-xml_prompt = ChatPromptTemplate.from_messages([("system", xml_system), ("human", "{input}")])
-
-ENV = os.getenv('ENV')
-HOST = os.getenv('ACCOUNT_HOST')
-MASTER_KEY = os.getenv('ACCOUNT_KEY')
-DATABASE_ID = os.getenv('COSMOS_DATABASE')
-CONTAINER_ID = os.getenv('COSMOS_CONTAINER')
-HISTORY_CONTAINER_ID = os.getenv('COSMOS_HISTORY_CONTAINER')
-client = cosmos_client.CosmosClient(HOST, {'masterKey': MASTER_KEY}, user_agent="CosmosDBPythonQuickstart", user_agent_overwrite=True)
-database = client.get_database_client(DATABASE_ID)
-container = database.get_container_client(CONTAINER_ID)
-history_container = database.get_container_client(HISTORY_CONTAINER_ID)
-
-
-def format_docs_xml(docs: List[Document]) -> str:
-    formatted_docs = [
-        f"<source id=\"{i}\">\n<source>{doc.metadata['source']}</source>\n<article_snippet>{doc.page_content}</article_snippet>\n</source>"
-        for i, doc in enumerate(docs)
-    ]
-    return f"\n\n<sources>\n{chr(10).join(formatted_docs)}\n</sources>"
-
-def parse_output_with_error_handling(output):
-    try:
-        return XMLOutputParser()
-    except Exception:
-        # return {'answer':{'cited_answer': [{'answer': ''},{'citations': []}]}}
-        return XMLOutputParser().parse("")
-
-
-llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=os.getenv("OPEN_API_KEY"))
-
-
-rag_chain_from_docs = (
-        RunnablePassthrough.assign(context=(lambda x: format_docs_xml(x["context"])))
-        | xml_prompt
-        | llm
-        | XMLOutputParser()
-)
-
-settings = Settings(persist_directory=PERSIST_DIRECTORY)
-vectordb = Chroma(embedding_function=OpenAIEmbeddings(api_key=os.getenv("OPEN_API_KEY")), persist_directory=PERSIST_DIRECTORY)
-retriever = vectordb.as_retriever(search_type="mmr", search_kwargs={"k": K_VALUE})
-retrieve_docs = (lambda x: x["input"]) | retriever
-chain = RunnablePassthrough.assign(context=retrieve_docs).assign(
-    answer=rag_chain_from_docs
-)
-
-
-pdf_loader = PyPDFLoader("refine.pdf")
-pdf_docs = pdf_loader.load()
-
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
-splits = text_splitter.split_documents(pdf_docs)
-pdf_vectorstore = InMemoryVectorStore.from_documents(
-    documents=splits, embedding=OpenAIEmbeddings(api_key=os.getenv("OPEN_API_KEY"))
-)
-
-pdf_retriever = pdf_vectorstore.as_retriever()
-
-refine_system_prompt = (
-    "You are an assistant for question-answering tasks. "
-    "Use the following pieces of retrieved context to answer "
-    "the question. provide clear, concise, and informed answers without unnecessary fluff. "
-    "If you cannot answer the question with the retrieved context, only say that 'Nothing' "
-    "\n\n"
-    "{context}"
-)
-
-refine_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", refine_system_prompt),
-        ("human", "{input}"),
-    ]
-)
-
-question_answer_chain = create_stuff_documents_chain(llm, refine_prompt)
-pdf_rag_chain = create_retrieval_chain(pdf_retriever, question_answer_chain)
-
-def get_article_info(df, file_name):
-    title = df[df["file_name"] == file_name]["title"].iloc[0]
-    link = df[df["file_name"] == file_name]["link"].iloc[0]
-    return title, link
-
-
-df = pd.read_csv("articles_db.csv")
-
-
-def vectordb_search(query):
-    titles, links = [], []
-    question_search = retriever.invoke(query)
-    for item in question_search:
-        edited_item = item.metadata["source"].replace("\\", "/").replace("Articles/", "").replace("Articles\\", "")
-        title, link = get_article_info(df, edited_item)
-        if title not in titles:
-            titles.append(title)
-        # if link not in links:
-            links.append(link)
-    return "\n".join([f"- [{title}]({link})" for title, link in zip(titles, links)])
-
-RESPONSE_ID = ""
-
-
-def llm_response(query):
-    global RESPONSE_ID
-    RESPONSE_ID = str(uuid.uuid4())
-    chat = {}
-    titles, links, res_titles, res_links = [], [], [], []
-    chat["id"] = RESPONSE_ID
-    chat["partitionKey"] = "RoofingRoadmap"
-    chat["user"] = query
-    chat["env"] = ENV
-
-    pdf_answer = pdf_rag_chain.invoke({"input": f"{query}"})
-    try:
-        if 'f wave' in query.lower() or 'f-wave' in query.lower() or 'fwave' in query.lower():
-            query = query.replace('f wave', 'f-wave shingle').replace('f-wave', 'f-wave shingle').replace('fwave', 'f-wave shingle')
-        result = chain.invoke({"input": query})
-        if pdf_answer['answer'] == 'Nothing':
-            answer = result['answer']['cited_answer'][0]["answer"]
-            citations = result['answer']['cited_answer'][1]['citations']
-            for citation in citations:
-                try:
-                    edited_item = citation['citation'][1]["source"].replace("\\", "/").replace("Articles/", "").replace("Articles\\", "")
-                    title, link = get_article_info(df, edited_item)
-                    if title not in titles:
-                        titles.append(title)
-                    # if link not in links:
-                        links.append(link)
-                except (TypeError, KeyError, IndexError):
-                    # Handle the error or simply pass if citation does not have the expected keys
-                    continue
-        else:
-            answer = pdf_answer['answer']
-
-        if not result['answer']['cited_answer'][1]['citations']:
-            answer_with_citations = f"{answer}"
-        else:
-            question_search = retriever.invoke(query)
-            for res_item in question_search:
-                edited_item = res_item.metadata["source"].replace("\\", "/").replace("Articles/", "").replace("Articles\\", "")
-                res_title, res_link = get_article_info(df, edited_item)
-                if res_title not in res_titles and res_title not in titles:
-                    res_titles.append(res_title)
-                # if res_link not in res_links and res_link not in links:
-                    res_links.append(res_link)
-
-
-    except OutputParserException as e:
-        if pdf_answer['answer'] == 'Nothing':
-            answer = "Your search is beyond the scope of this tool at this time. Please explore the rest of [IBHS website](https://ibhs.org) to find research on this topic."
-            return answer
-        else:
-            answer = pdf_answer['answer']
-
-    finally:
-        chat["ai"] = answer
-        chat["timestamp"] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-        container.create_item(body=chat)
-
-    # Build the answer with superscript citations
-    answer_with_citations = f"{answer}"
-    for i, (title, link) in enumerate(zip(titles, links), start=1):
-        answer_with_citations += f" <sup>[[{i}]({link})]</sup> "
-
-    # Build the references section with clickable links
-    if not links:
-        markdown_list = f"{answer_with_citations}"
-    else:
-        citations_section = "\n\nCitations:\n" + "\n".join(
-            [f"[{i}]: [{title}]({link})" for i, (title, link) in enumerate(zip(titles, links), start=1)]
-        )
-        markdown_list = f"{answer_with_citations}{citations_section}"
-    # Combine answer and citations for final markdown output
-
-
-    if not res_links:
-        return markdown_list
-    else:
-        markdown_list += f"\n\n\nHere is a list of articles that can provide more information about your inquiry:\n"
-        markdown_list += "\n".join([f"- [{res_title}]({res_link})" for res_title, res_link in zip(res_titles, res_links)])
-
-    return markdown_list
-
-def vote(value, data: gr.LikeData):
-    chat_vote = {}
-    chat_vote["id"] = str(uuid.uuid4())
-    chat_vote["chat_id"] = RESPONSE_ID
-    chat_vote["partitionKey"] = "RoofingRoadmapVotes"
-    chat_vote["response"] = data.value[0].split('<sup>', 1)[0].split('\n', 1)[0]
-    chat_vote["env"] = ENV
-    chat_vote["timestamp"] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-
-    if data.liked:
-        chat_vote["vote"] = "upvote"
-    else:
-        chat_vote["vote"] = "downvote"
-
-    history_container.create_item(body=chat_vote)
-
-def show_feedback_column(visible):
-    if visible:
-        # If visible, hide the column
-        return gr.update(visible=False), gr.update(value=""), False
-    else:
-        # If not visible, show the column and clear the Textbox
-        return gr.update(visible=True), "", True
-
-def user_feedback(value, ):
-    chat_feedback = {}
-    chat_feedback["id"] = str(uuid.uuid4())
-    chat_feedback["chat_id"] = RESPONSE_ID
-    chat_feedback["partitionKey"] = "RoofingRoadmapFeedback"
-    chat_feedback["feedback"] = value
-    chat_feedback["env"] = ENV
-    chat_feedback["timestamp"] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-    history_container.create_item(body=chat_feedback)
-    return gr.update(visible=False), ""
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Find literature to answer your question!")
-    gr.Markdown("### Ask a question about the wind and hail performance of asphalt shingle, metal, and tile roofs.")
-    with gr.Row():
-        with gr.Column():
-            chatbot = gr.Chatbot(type="messages", height=400)
-            chatbot.like(vote, chatbot, None)
-            msg = gr.Textbox(label="Hit the Enter to send your question", placeholder="What's on your mind?", show_copy_button=True)
-            with gr.Row():
-                send = gr.Button("Send", variant="secondary", scale=3)
-                feedback = gr.Button("Feedback", variant="stop", scale=1)
-            with gr.Column(visible=False, elem_id="feedback_column") as feedback_column:
-                usr_msg = gr.Textbox(label="Submit feedback to IBHS", info="What went wrong?", placeholder="Give us as much detail as you can!", lines=3)
-                usr_submit = gr.Button("Submit", variant="secondary")
-
-
-
-            def user(user_message, history: list):
-                return "", history + [{"role": "user", "content": user_message}]
-
-
-            def bot(history: list):
-                bot_message = llm_response(history[-1]['content'])
-                history.append({"role": "assistant", "content": ""})
-                for character in bot_message:
-                    history[-1]['content'] += character
-                    yield history
-
-
-            feedback_column_state = gr.State(False)
-            msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
-            send.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
-            feedback.click(fn=show_feedback_column, inputs=[feedback_column_state], outputs=[feedback_column, usr_msg, feedback_column_state])
-            usr_submit.click(user_feedback, usr_msg, outputs=[feedback_column, usr_msg])
-    gr.Markdown("*Our chatbot is constantly learning and improving to better serve you!*")
-    gr.Markdown("#### Additional questions? Contact IBHS Membership Manager Larry Scott at [lscott@ibhs.org]().")
-
-if __name__ == "__main__":
+from langchain_openai import OpenAIEmbeddings
+from langchain_openai import ChatOpenAI
+from langchain_chroma import Chroma
+import chromadb
+from chromadb.config import Settings
+from langchain_core.prompts import ChatPromptTemplate
+from typing import List
+
+from langchain_core.documents import Document
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import XMLOutputParser
+import gradio as gr
+import pandas as pd
+import logging
+from langchain_core.exceptions import OutputParserException
+import os
+from dotenv import load_dotenv
+
+from sympy.codegen.ast import continue_
+
+import azure.cosmos.cosmos_client as cosmos_client
+import azure.cosmos.exceptions as exceptions
+from azure.cosmos.partition_key import PartitionKey
+
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.runnables import RunnableLambda
+import datetime
+import uuid
+import hashlib
+load_dotenv()
+# Constants
+PERSIST_DIRECTORY = "chroma_store"
+K_VALUE = 5
+
+xml_system = """You're a helpful AI assistant. Given a user question and some scientific literature 
+documents which highlight research on different roof cover materials (e.g., asphalt shingles, metal, tile) 
+and their performance against natural hazards(e.g., wind, hail), answer the user 
+question. 
+You’re a helpful AI assistant. Given a user question and scientific literature on various roof cover materials (e.g., asphalt shingles, metal, tile) 
+and their performance against natural hazards (e.g., wind, hail), provide clear, concise, and informed answers without unnecessary fluff.
+	
+When addressing questions about ‘what is the best roof,’ consider the following factors:
+	•	Geography, aesthetic preferences, budget, frequency of weather-related hazards, roof cover performance, and how performance changes with age.
+	•	For the insurance industry, the ‘best roof’ depends on the specific hazards (their location and frequency), performance expectations and predictability, and the cost of materials.
+	
+If none of the articles answer the question, simply say that there are no articles relevant to your inquiry.
+Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that 
+justifies the answer and the ID and also Source Name of the quote article. Return a citation for every quote across all articles 
+that justify the answer. Use the following format for your final output:
+<cited_answer>
+    <answer></answer>
+    <citations>
+        <citation><source_id></source_id><source></source><quote></quote></citation>
+        <citation><source_id></source_id><source></source><quote></quote></citation>
+        ...
+    </citations>
+</cited_answer>
+Here are the articles:{context}"""
+
+xml_prompt = ChatPromptTemplate.from_messages([("system", xml_system), ("human", "{input}")])
+
+ENV = os.getenv('ENV')
+HOST = os.getenv('ACCOUNT_HOST')
+MASTER_KEY = os.getenv('ACCOUNT_KEY')
+DATABASE_ID = os.getenv('COSMOS_DATABASE')
+CONTAINER_ID = os.getenv('COSMOS_CONTAINER')
+HISTORY_CONTAINER_ID = os.getenv('COSMOS_HISTORY_CONTAINER')
+client = cosmos_client.CosmosClient(HOST, {'masterKey': MASTER_KEY}, user_agent="CosmosDBPythonQuickstart", user_agent_overwrite=True)
+database = client.get_database_client(DATABASE_ID)
+container = database.get_container_client(CONTAINER_ID)
+history_container = database.get_container_client(HISTORY_CONTAINER_ID)
+
+
+def format_docs_xml(docs: List[Document]) -> str:
+    formatted_docs = [
+        f"<source id=\"{i}\">\n<source>{doc.metadata['source']}</source>\n<article_snippet>{doc.page_content}</article_snippet>\n</source>"
+        for i, doc in enumerate(docs)
+    ]
+    return f"\n\n<sources>\n{chr(10).join(formatted_docs)}\n</sources>"
+
+def parse_output_with_error_handling(output):
+    try:
+        return XMLOutputParser()
+    except Exception:
+        # return {'answer':{'cited_answer': [{'answer': ''},{'citations': []}]}}
+        return XMLOutputParser().parse("")
+
+
+llm = ChatOpenAI(model="gpt-4o", temperature=0)
+
+
+rag_chain_from_docs = (
+        RunnablePassthrough.assign(context=(lambda x: format_docs_xml(x["context"])))
+        | xml_prompt
+        | llm
+        | XMLOutputParser()
+)
+
+settings = Settings(persist_directory=PERSIST_DIRECTORY)
+vectordb = Chroma(embedding_function=OpenAIEmbeddings(), persist_directory=PERSIST_DIRECTORY)
+retriever = vectordb.as_retriever(search_type="mmr", search_kwargs={"k": K_VALUE})
+retrieve_docs = (lambda x: x["input"]) | retriever
+chain = RunnablePassthrough.assign(context=retrieve_docs).assign(
+    answer=rag_chain_from_docs
+)
+
+
+pdf_loader = PyPDFLoader("refine.pdf")
+pdf_docs = pdf_loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
+splits = text_splitter.split_documents(pdf_docs)
+pdf_vectorstore = InMemoryVectorStore.from_documents(
+    documents=splits, embedding=OpenAIEmbeddings()
+)
+
+pdf_retriever = pdf_vectorstore.as_retriever()
+
+refine_system_prompt = (
+    "You are an assistant for question-answering tasks. "
+    "Use the following pieces of retrieved context to answer "
+    "the question. provide clear, concise, and informed answers without unnecessary fluff. "
+    "If you cannot answer the question with the retrieved context, only say that 'Nothing' "
+    "\n\n"
+    "{context}"
+)
+
+refine_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", refine_system_prompt),
+        ("human", "{input}"),
+    ]
+)
+
+question_answer_chain = create_stuff_documents_chain(llm, refine_prompt)
+pdf_rag_chain = create_retrieval_chain(pdf_retriever, question_answer_chain)
+
+def get_article_info(df, file_name):
+    title = df[df["file_name"] == file_name]["title"].iloc[0]
+    link = df[df["file_name"] == file_name]["link"].iloc[0]
+    return title, link
+
+
+df = pd.read_csv("articles_db.csv")
+
+
+def vectordb_search(query):
+    titles, links = [], []
+    question_search = retriever.invoke(query)
+    for item in question_search:
+        edited_item = item.metadata["source"].replace("Articles/", "")
+        title, link = get_article_info(df, edited_item)
+        if title not in titles:
+            titles.append(title)
+        # if link not in links:
+            links.append(link)
+    return "\n".join([f"- [{title}]({link})" for title, link in zip(titles, links)])
+
+
+def initialize_session(session_id):
+    # If no session_id exists, generate a new one
+    if session_id is None:
+        session_id = str(uuid.uuid4())
+    return session_id
+
+
+def llm_response(query, session_id):
+    print("session_id", session_id)
+    chat = {}
+    titles, links, res_titles, res_links = [], [], [], []
+    session_id = initialize_session(session_id)
+    chat["id"] = str(uuid.uuid4())
+    chat["chat_id"] = session_id
+    chat["partitionKey"] = "RoofingRoadmap"
+    chat["user"] = query
+    chat["env"] = ENV
+
+    pdf_answer = pdf_rag_chain.invoke({"input": f"{query}"})
+    try:
+        if 'f wave' in query.lower() or 'f-wave' in query.lower() or 'fwave' in query.lower():
+            query = query.replace('f wave', 'f-wave shingle').replace('f-wave', 'f-wave shingle').replace('fwave', 'f-wave shingle')
+        result = chain.invoke({"input": query})
+        if pdf_answer['answer'] == 'Nothing':
+            answer = result['answer']['cited_answer'][0]["answer"]
+            citations = result['answer']['cited_answer'][1]['citations']
+            for citation in citations:
+                try:
+                    edited_item = citation['citation'][1]["source"].replace("Articles/", "")
+                    title, link = get_article_info(df, edited_item)
+                    if title not in titles:
+                        titles.append(title)
+                    # if link not in links:
+                        links.append(link)
+                except (TypeError, KeyError, IndexError):
+                    # Handle the error or simply pass if citation does not have the expected keys
+                    continue
+        else:
+            answer = pdf_answer['answer']
+
+        if not result['answer']['cited_answer'][1]['citations']:
+            answer_with_citations = f"{answer}"
+        else:
+            question_search = retriever.invoke(query)
+            for res_item in question_search:
+                edited_item = res_item.metadata["source"].replace("Articles/", "")
+                res_title, res_link = get_article_info(df, edited_item)
+                if res_title not in res_titles and res_title not in titles:
+                    res_titles.append(res_title)
+                # if res_link not in res_links and res_link not in links:
+                    res_links.append(res_link)
+
+
+    except OutputParserException as e:
+        if pdf_answer['answer'] == 'Nothing':
+            answer = "Your search is beyond the scope of this tool at this time. Please explore the rest of [IBHS website](https://ibhs.org) to find research on this topic."
+            return answer
+        else:
+            answer = pdf_answer['answer']
+
+    finally:
+        chat["ai"] = answer
+        chat["timestamp"] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+        container.create_item(body=chat)
+
+    # Build the answer with superscript citations
+    answer_with_citations = f"{answer}"
+    for i, (title, link) in enumerate(zip(titles, links), start=1):
+        answer_with_citations += f" <sup>[[{i}]({link})]</sup> "
+
+    # Build the references section with clickable links
+    if not links:
+        markdown_list = f"{answer_with_citations}"
+    else:
+        citations_section = "\n\nCitations:\n" + "\n".join(
+            [f"[{i}]: [{title}]({link})" for i, (title, link) in enumerate(zip(titles, links), start=1)]
+        )
+        markdown_list = f"{answer_with_citations}{citations_section}"
+    # Combine answer and citations for final markdown output
+
+
+    if not res_links:
+        return markdown_list
+    else:
+        markdown_list += f"\n\n\nHere is a list of articles that can provide more information about your inquiry:\n"
+        markdown_list += "\n".join([f"- [{res_title}]({res_link})" for res_title, res_link in zip(res_titles, res_links)])
+
+    return markdown_list
+
+def vote(value, data: gr.LikeData, session_id: str = None):
+    session_id = initialize_session(session_id)
+    print("session_id", session_id)
+    chat_vote = {}
+    chat_vote["id"] = str(uuid.uuid4())
+    chat_vote["chat_id"] = session_id
+    chat_vote["partitionKey"] = "RoofingRoadmapVotes"
+    chat_vote["response"] = data.value[0].split('<sup>', 1)[0].split('\n', 1)[0]
+    chat_vote["env"] = ENV
+    chat_vote["timestamp"] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+    if data.liked:
+        chat_vote["vote"] = "upvote"
+    else:
+        chat_vote["vote"] = "downvote"
+
+    history_container.create_item(body=chat_vote)
+
+def show_feedback_column(visible):
+    if visible:
+        # If visible, hide the column
+        return gr.update(visible=False), gr.update(value=""), False
+    else:
+        # If not visible, show the column and clear the Textbox
+        return gr.update(visible=True), "", True
+
+def user_feedback(value, session_id):
+    session_id = initialize_session(session_id)
+    print("session_id", session_id)
+    chat_feedback = {}
+    chat_feedback["id"] = str(uuid.uuid4())
+    chat_feedback["chat_id"] = session_id
+    chat_feedback["partitionKey"] = "RoofingRoadmapFeedback"
+    chat_feedback["feedback"] = value
+    chat_feedback["env"] = ENV
+    chat_feedback["timestamp"] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    history_container.create_item(body=chat_feedback)
+    return gr.update(visible=False), "", session_id
+
+with gr.Blocks() as demo:
+    # Create a state variable; this is unique per session.
+    session_id = gr.State(None)
+
+    gr.Markdown("## Find literature to answer your question!")
+    gr.Markdown("### Ask a question about the wind and hail performance of asphalt shingle, metal, and tile roofs.")
+    with gr.Row():
+        with gr.Column():
+            chatbot = gr.Chatbot(type="messages", height=400)
+            chatbot.like(vote, [chatbot, session_id], None)
+            msg = gr.Textbox(label="Hit the Enter to send your question", placeholder="What's on your mind?", show_copy_button=True)
+            with gr.Row():
+                send = gr.Button("Send", variant="secondary", scale=3)
+                feedback = gr.Button("Feedback", variant="stop", scale=1)
+            with gr.Column(visible=False, elem_id="feedback_column") as feedback_column:
+                usr_msg = gr.Textbox(label="Submit feedback to IBHS", info="What went wrong?", placeholder="Give us as much detail as you can!", lines=3)
+                usr_submit = gr.Button("Submit", variant="secondary")
+
+
+
+            def user(user_message, history: list):
+                return "", history + [{"role": "user", "content": user_message}]
+
+
+            def bot(history: list, session_id_i):
+                if session_id_i is None:
+                    session_id_i = initialize_session(session_id_i)
+                bot_message = llm_response(history[-1]['content'], session_id_i)
+                history.append({"role": "assistant", "content": ""})
+                for character in bot_message:
+                    history[-1]['content'] += character
+                    yield history, session_id_i
+
+
+            feedback_column_state = gr.State(False)
+            msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, session_id], [chatbot,session_id])
+            send.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, [chatbot, session_id], [chatbot, session_id])
+            feedback.click(fn=show_feedback_column, inputs=[feedback_column_state], outputs=[feedback_column, usr_msg, feedback_column_state])
+            usr_submit.click(user_feedback, [usr_msg, session_id], outputs=[feedback_column, usr_msg, session_id])
+    gr.Markdown("*Our chatbot is constantly learning and improving to better serve you!*")
+    gr.Markdown("#### Additional questions? Contact IBHS Membership Manager Larry Scott at [lscott@ibhs.org]().")
+
+if __name__ == "__main__":
     demo.launch()