| # import streamlit as st | |
| # from langchain_openai import OpenAIEmbeddings # Changed to OpenAI embeddings | |
| # from langchain_chroma import Chroma | |
| # from langchain_groq import ChatGroq | |
| # import os | |
| # from dotenv import load_dotenv | |
| # # Page config | |
| # st.set_page_config( | |
| # page_title="UK Construction Regulations Assistant", | |
| # page_icon="🏗️", | |
| # layout="wide" | |
| # ) | |
| # # Load environment variables | |
| # load_dotenv() | |
| # # Initialize RAG components | |
| # @st.cache_resource | |
| # def init_rag(): | |
| # """Initialize RAG components with caching""" | |
| # try: | |
| # # Check if main_chroma_data exists | |
| # if not os.path.exists("./main_chroma_data"): | |
| # st.error("Error: main_chroma_data directory not found. Please check the directory path.") | |
| # return None, None | |
| # # Initialize embeddings | |
| # try: | |
| # embeddings = OpenAIEmbeddings( | |
| # api_key=os.getenv("OPENAI_API_KEY") | |
| # ) | |
| # except Exception as e: | |
| # st.error(f"Error initializing embeddings: {str(e)}") | |
| # return None, None | |
| # # Initialize vector store | |
| # try: | |
| # vectorstore = Chroma( | |
| # collection_name="main_construction_rag", | |
| # embedding_function=embeddings, | |
| # persist_directory="./main_chroma_data" | |
| # ) | |
| # except Exception as e: | |
| # st.error(f"Error initializing vector store: {str(e)}") | |
| # return None, None | |
| # # Check if GROQ API key is set | |
| # groq_api_key = os.getenv("GROQ_API_KEY") | |
| # if not groq_api_key: | |
| # st.error("Error: GROQ_API_KEY not found in environment variables") | |
| # return None, None | |
| # # Initialize LLM | |
| # try: | |
| # llm = ChatGroq( | |
| # api_key=groq_api_key, | |
| # model_name="llama-3.3-70b-versatile", | |
| # temperature=0 | |
| # ) | |
| # except Exception as e: | |
| # st.error(f"Error initializing LLM: {str(e)}") | |
| # return None, None | |
| # return vectorstore, llm | |
| # except Exception as e: | |
| # st.error(f"Error initializing RAG system: {str(e)}") | |
| # return None, None | |
| # # Initialize | |
| # vectorstore, llm = init_rag() | |
| # # Sidebar for feedback | |
| # with st.sidebar: | |
| # st.title("📝 Feedback") | |
| # feedback = st.text_area("Share your feedback on the answers:", height=100) | |
| # if st.button("Submit Feedback"): | |
| # st.success("Thank you for your feedback!") | |
| # # Main interface | |
| # st.title("🏗️ UK Construction Regulations Assistant") | |
| # st.markdown(""" | |
| # This AI assistant helps answer questions about UK construction regulations using: | |
| # - Official Building Regulations documents | |
| # - Expert YouTube content from LABC, RICS, and other authorities | |
| # - Technical documentation and guidance | |
| # """) | |
| # # User input | |
| # question = st.text_input("Enter your question about UK construction regulations:") | |
| # if st.button("Get Answer"): | |
| # if not question: | |
| # st.warning("Please enter a question.") | |
| # elif vectorstore is None or llm is None: | |
| # st.error("RAG system not properly initialized. Please check the errors above.") | |
| # else: | |
| # with st.spinner("Searching regulations and generating answer..."): | |
| # try: | |
| # # Get relevant documents | |
| # docs = vectorstore.similarity_search(question, k=4) | |
| # contexts = [doc.page_content for doc in docs] | |
| # # Generate answer | |
| # context_text = "\n\n".join(contexts) | |
| # prompt = f"""Based on the following context from UK Building Regulations, provide a clear and detailed answer to the question. | |
| # Include specific references to regulations where available. | |
| # Question: {question} | |
| # Context: {context_text} | |
| # Answer:""" | |
| # response = llm.invoke(prompt) | |
| # # Display answer | |
| # st.markdown("### Answer") | |
| # st.write(response.content) | |
| # # Display sources | |
| # with st.expander("View Source Documents"): | |
| # for i, context in enumerate(contexts, 1): | |
| # st.markdown(f"**Source {i}:**") | |
| # st.markdown(context) | |
| # st.divider() | |
| # # Add thumbs up/down for answer quality | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # if st.button("👍 Helpful"): | |
| # st.success("Thank you for your feedback!") | |
| # with col2: | |
| # if st.button("👎 Not Helpful"): | |
| # st.info("Thank you for your feedback. Please let us know how we can improve in the sidebar.") | |
| # except Exception as e: | |
| # st.error(f"Error generating answer: {str(e)}") | |
| # # Footer | |
| # st.markdown("---") | |
| # st.markdown("*This is a research project. Always verify information with official sources.*") | |
| # # SQLite compatibility fix for Chromadb | |
| # import sqlite3 | |
| # print(f"SQLite version: {sqlite3.sqlite_version}") | |
| # # Try alternative vector store approach if SQLite version is too old | |
| # import os | |
| # os.environ["LANGCHAIN_CHROMA_ALLOW_DEPRECATED_BACKEND"] = "true" | |
| # import streamlit as st | |
| # from langchain_huggingface import HuggingFaceEmbeddings | |
| # from langchain_chroma import Chroma # Updated from langchain_community.vectorstores | |
| # from langchain_groq import ChatGroq | |
| # import os | |
| # from dotenv import load_dotenv | |
| # from cloud_storage import download_vectorstore | |
| # # Page config | |
| # st.set_page_config( | |
| # page_title="UK Building Regulations Assistant", | |
| # page_icon="🏗️", | |
| # layout="wide" | |
| # ) | |
| # # Load environment variables | |
| # load_dotenv() | |
| # # Initialize RAG components | |
| # @st.cache_resource | |
| # def init_rag(): | |
| # """Initialize RAG components with caching""" | |
| # try: | |
| # # Check if main_chroma_data exists | |
| # if not os.path.exists("./main_chroma_data"): | |
| # download_vectorstore() | |
| # # st.error("Error: main_chroma_data directory not found. Please check the directory path.") | |
| # # return None, None | |
| # # Initialize embeddings | |
| # try: | |
| # embeddings = HuggingFaceEmbeddings( | |
| # model_name="sentence-transformers/all-mpnet-base-v2", | |
| # encode_kwargs={'normalize_embeddings': True} # Added for stability | |
| # ) | |
| # except Exception as e: | |
| # st.error(f"Error initializing embeddings: {str(e)}") | |
| # return None, None | |
| # # Initialize vector store | |
| # # try: | |
| # # vectorstore = Chroma( | |
| # # collection_name="main_construction_rag", | |
| # # embedding_function=embeddings, | |
| # # persist_directory="./main_chroma_data" | |
| # # ) | |
| # # except Exception as e: | |
| # # st.error(f"Error initializing vector store: {str(e)}") | |
| # # return None, None | |
| # # Initialize vector store | |
| # try: | |
| # vectorstore = Chroma( | |
| # collection_name="main_construction_rag", | |
| # embedding_function=embeddings, | |
| # persist_directory="./main_chroma_data" | |
| # ) | |
| # except Exception as e: | |
| # st.warning("Using deprecated backend due to SQLite version constraints") | |
| # # Use alternative initialization if needed | |
| # from langchain_community.vectorstores import Chroma as ChromaDeprecated | |
| # vectorstore = ChromaDeprecated( | |
| # collection_name="main_construction_rag", | |
| # embedding_function=embeddings, | |
| # persist_directory="./main_chroma_data" | |
| # ) | |
| # # Check if GROQ API key is set | |
| # groq_api_key = os.getenv("GROQ_API_KEY") | |
| # if not groq_api_key: | |
| # st.error("Error: GROQ_API_KEY not found in environment variables") | |
| # return None, None | |
| # # Initialize LLM | |
| # try: | |
| # llm = ChatGroq( | |
| # api_key=groq_api_key, | |
| # model_name="llama-3.3-70b-versatile", | |
| # temperature=0.1 | |
| # ) | |
| # except Exception as e: | |
| # st.error(f"Error initializing LLM: {str(e)}") | |
| # return None, None | |
| # return vectorstore, llm | |
| # except Exception as e: | |
| # st.error(f"Error initializing RAG system: {str(e)}") | |
| # return None, None | |
| # # Initialize | |
| # vectorstore, llm = init_rag() | |
| # # Sidebar for feedback | |
| # with st.sidebar: | |
| # st.title("📝 StructureGPT Feedback") | |
| # feedback = st.text_area("Share your feedback on the answers:", height=100) | |
| # if st.button("Submit Feedback"): | |
| # st.success("Thank you for your feedback!") | |
| # # Main interface | |
| # st.title("🏗️ StructureGPT - UK Building Regulations AI Assistant") | |
| # st.markdown(""" | |
| # This AI assistant helps answer questions about UK building regulations using: | |
| # - Official Building Regulations documents | |
| # - Expert YouTube content from LABC, RICS, and other authorities | |
| # - Technical documentation and guidance | |
| # """) | |
| # # Add testing phase notice with warning styling | |
| # st.warning(""" | |
| # ⚠️ **TESTING PHASE** - StructureGPT is currently in beta testing, focusing only on UK Building Regulations Parts A (Structure), B (Fire Safety), and C (Site Preparation and Resistance to Contaminants and Moisture). Additional regulation parts will be added soon. | |
| # """) | |
| # # User input | |
| # question = st.text_input("Enter your question about UK building regulations:") | |
| # if st.button("Get Answer"): | |
| # if not question: | |
| # st.warning("Please enter a question.") | |
| # elif vectorstore is None or llm is None: | |
| # st.error("RAG system not properly initialized. Please check the errors above.") | |
| # else: | |
| # with st.spinner("Searching regulations and generating answer..."): | |
| # try: | |
| # # Get relevant documents | |
| # docs = vectorstore.similarity_search(question, k=4) | |
| # contexts = [doc.page_content for doc in docs] | |
| # # Generate answer | |
| # context_text = "\n\n".join(contexts) | |
| # prompt = f"""Based on the following context from UK Building Regulations, provide a clear and detailed answer to the question. | |
| # Include specific references to regulations where available. | |
| # Question: {question} | |
| # Context: {context_text} | |
| # Answer:""" | |
| # response = llm.invoke(prompt) | |
| # # Display answer | |
| # st.markdown("### Answer") | |
| # st.write(response.content) | |
| # # Display sources | |
| # with st.expander("View Source Documents"): | |
| # for i, context in enumerate(contexts, 1): | |
| # st.markdown(f"**Source {i}:**") | |
| # st.markdown(context) | |
| # st.divider() | |
| # # Add thumbs up/down for answer quality | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # if st.button("👍 Helpful"): | |
| # st.success("Thank you for your feedback!") | |
| # with col2: | |
| # if st.button("👎 Not Helpful"): | |
| # st.info("Thank you for your feedback. Please let us know how we can improve in the sidebar.") | |
| # except Exception as e: | |
| # st.error(f"Error generating answer: {str(e)}") | |
| # # Footer | |
| # st.markdown("---") | |
| # st.markdown("*StructureGPT is a research project in testing phase. Currently supporting Parts A (Structure), B (Fire Safety), and C (Site Preparation) of UK Building Regulations. Always verify information with official sources.*") | |
| # SQLite compatibility fix for Chromadb | |
| import sqlite3 | |
| print(f"SQLite version: {sqlite3.sqlite_version}") | |
| # Try alternative vector store approach if SQLite version is too old | |
| import os | |
| os.environ["LANGCHAIN_CHROMA_ALLOW_DEPRECATED_BACKEND"] = "true" | |
| import streamlit as st | |
| from langchain_huggingface import HuggingFaceEmbeddings | |
| from langchain_chroma import Chroma | |
| from langchain_groq import ChatGroq | |
| import os | |
| from dotenv import load_dotenv | |
| from cloud_storage import download_vectorstore | |
| # Page config | |
| st.set_page_config( | |
| page_title="StructureGPT - UK Building Regulations Assistant", | |
| page_icon="🏗️", | |
| layout="wide" | |
| ) | |
| # Load environment variables | |
| load_dotenv() | |
| # Initialize RAG components | |
| def init_rag(): | |
| """Initialize RAG components with caching""" | |
| try: | |
| # Check if main_chroma_data exists | |
| if not os.path.exists("./main_chroma_data"): | |
| download_vectorstore() | |
| # Initialize embeddings | |
| try: | |
| embeddings = HuggingFaceEmbeddings( | |
| model_name="sentence-transformers/all-mpnet-base-v2", | |
| encode_kwargs={'normalize_embeddings': True} # Added for stability | |
| ) | |
| except Exception as e: | |
| st.error(f"Error initializing embeddings: {str(e)}") | |
| return None, None, None | |
| # Initialize vector store | |
| try: | |
| vectorstore = Chroma( | |
| collection_name="main_construction_rag", | |
| embedding_function=embeddings, | |
| persist_directory="./main_chroma_data" | |
| ) | |
| except Exception as e: | |
| st.warning("Using deprecated backend due to SQLite version constraints") | |
| # Use alternative initialization if needed | |
| from langchain_community.vectorstores import Chroma as ChromaDeprecated | |
| vectorstore = ChromaDeprecated( | |
| collection_name="main_construction_rag", | |
| embedding_function=embeddings, | |
| persist_directory="./main_chroma_data" | |
| ) | |
| # Check if GROQ API key is set | |
| groq_api_key = os.getenv("GROQ_API_KEY") | |
| if not groq_api_key: | |
| st.error("Error: GROQ_API_KEY not found in environment variables") | |
| return None, None, None | |
| # Initialize LLMs - both models | |
| try: | |
| llm_70b = ChatGroq( | |
| api_key=groq_api_key, | |
| model_name="llama-3.3-70b-versatile", | |
| temperature=0.1 | |
| ) | |
| llm_8b = ChatGroq( | |
| api_key=groq_api_key, | |
| model_name="llama3-8b-8192", | |
| temperature=0.1 | |
| ) | |
| except Exception as e: | |
| st.error(f"Error initializing LLMs: {str(e)}") | |
| return None, None, None | |
| return vectorstore, llm_70b, llm_8b | |
| except Exception as e: | |
| st.error(f"Error initializing RAG system: {str(e)}") | |
| return None, None, None | |
| # Initialize | |
| vectorstore, llm_70b, llm_8b = init_rag() | |
| # Sidebar for model selection and feedback | |
| with st.sidebar: | |
| st.title("🔧 Model Settings") | |
| # Model selection toggle | |
| model_option = st.radio( | |
| "Select Model:", | |
| ["Llama-3.3-70B (More accurate, slower)", "Llama3-8B (Faster, less accurate)"], | |
| index=0, # Default to 70B model | |
| help="Choose between more accurate (70B) or faster (8B) model" | |
| ) | |
| # Display selected model details | |
| if model_option == "Llama-3.3-70B (More accurate, slower)": | |
| st.info("Using Llama-3.3-70B: Higher accuracy but slightly slower responses") | |
| selected_llm = llm_70b | |
| else: | |
| st.info("Using Llama3-8B: Faster responses with good accuracy") | |
| selected_llm = llm_8b | |
| st.divider() | |
| # Feedback section | |
| st.title("📝 Feedback") | |
| feedback = st.text_area("Share your feedback on the answers:", height=100) | |
| if st.button("Submit Feedback"): | |
| st.success("Thank you for your feedback!") | |
| # Main interface | |
| st.title("🏗️ StructureGPT - UK Building Regulations AI Assistant") | |
| st.markdown(""" | |
| This AI assistant helps answer questions about UK building regulations using: | |
| - Official Building Regulations documents | |
| - Expert YouTube content from LABC, RICS, and other authorities | |
| - Technical documentation and guidance | |
| """) | |
| # Add testing phase notice with warning styling | |
| st.warning(""" | |
| ⚠️ **TESTING PHASE** - StructureGPT is currently in beta testing, focusing only on UK Building Regulations Parts A (Structure), B (Fire Safety), and C (Site Preparation and Resistance to Contaminants and Moisture). Additional regulation parts will be added soon. | |
| """) | |
| # User input | |
| question = st.text_input("Enter your question about UK building regulations:") | |
| if st.button("Get Answer"): | |
| if not question: | |
| st.warning("Please enter a question.") | |
| elif vectorstore is None or selected_llm is None: | |
| st.error("RAG system not properly initialized. Please check the errors above.") | |
| else: | |
| with st.spinner(f"Searching regulations and generating answer using {model_option.split(' ')[0]}..."): | |
| try: | |
| # Get relevant documents | |
| docs = vectorstore.similarity_search(question, k=4) | |
| contexts = [doc.page_content for doc in docs] | |
| # Generate answer | |
| context_text = "\n\n".join(contexts) | |
| prompt = f"""Based on the following context from UK Building Regulations, provide a clear and detailed answer to the question. | |
| Include specific references to regulations where available. | |
| Question: {question} | |
| Context: {context_text} | |
| Answer:""" | |
| response = selected_llm.invoke(prompt) | |
| # Display answer | |
| st.markdown("### Answer") | |
| st.write(response.content) | |
| # Display model used | |
| st.caption(f"Answer generated using {model_option.split(' ')[0]}") | |
| # Display sources | |
| with st.expander("View Source Documents"): | |
| for i, context in enumerate(contexts, 1): | |
| st.markdown(f"**Source {i}:**") | |
| st.markdown(context) | |
| st.divider() | |
| # Add feedback section | |
| st.subheader("Was this answer helpful?") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.button("👍 Helpful"): | |
| st.success("Thank you for your feedback!") | |
| with col2: | |
| if st.button("👎 Not Helpful"): | |
| st.info("Thank you for your feedback. Please let us know how we can improve in the sidebar.") | |
| except Exception as e: | |
| st.error(f"Error generating answer: {str(e)}") | |
| # Footer | |
| st.markdown("---") | |
| st.markdown("*StructureGPT is a research project in testing phase. Currently supporting Parts A (Structure), B (Fire Safety), and C (Site Preparation) of UK Building Regulations. Always verify information with official sources.*") |