import os import logging from typing import Dict, List, Optional from functools import lru_cache import re import gradio as gr try: from vector_db import VectorDatabase except ImportError: print("Error: Could not import VectorDatabase from vector_db.py.") print("Please ensure vector_db.py exists in the same directory and is correctly defined.") exit(1) try: from langchain_openai import ChatOpenAI except ImportError: print("Error: langchain-openai not found. Please install it: pip install langchain-openai") exit(1) from langchain.prompts import PromptTemplate from langchain.chains import LLMChain # Suppress warnings import warnings warnings.filterwarnings("ignore", category=SyntaxWarning) warnings.filterwarnings("ignore", category=UserWarning, message=".*You are using gradio version.*") warnings.filterwarnings("ignore", category=DeprecationWarning) # Enhanced logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s' ) # --- RAGSystem Class --- class RAGSystem: # (Keep the RAGSystem class exactly the same as in the previous version) # ... __init__ ... # ... extract_statutes ... # ... process_query_cached ... # ... process_query ... # ... get_states ... # ... load_pdf ... def __init__(self, vector_db: Optional[VectorDatabase] = None): logging.info("Initializing RAGSystem") self.vector_db = vector_db if vector_db else VectorDatabase() self.llm = None self.chain = None self.prompt_template_str = """You are a legal assistant specializing in tenant rights and landlord-tenant laws. Your goal is to provide accurate, detailed, and helpful answers grounded in legal authority. Use the provided statutes as the primary source when available. If no relevant statutes are found in the context, rely on your general knowledge to provide a pertinent and practical response, clearly indicating when you are doing so and prioritizing state-specific information over federal laws for state-specific queries. Instructions: * Use the context and statutes as the primary basis for your answer when available. * For state-specific queries, prioritize statutes or legal principles from the specified state over federal laws. * Cite relevant statutes (e.g., (AS § 34.03.220(a)(2))) explicitly in your answer when applicable. * If multiple statutes apply, list all relevant ones. * If no specific statute is found in the context, state this clearly (e.g., 'No specific statute was found in the provided context'), then provide a general answer based on common legal principles or practices, marked as such. * Include practical examples or scenarios to enhance clarity and usefulness. * Use bullet points or numbered lists for readability when appropriate. * Maintain a professional and neutral tone. Question: {query} State: {state} Statutes from context: {statutes} Context information: --- START CONTEXT --- {context} --- END CONTEXT --- Answer:""" self.prompt_template = PromptTemplate( input_variables=["query", "context", "state", "statutes"], template=self.prompt_template_str ) logging.info("RAGSystem initialized.") def extract_statutes(self, text: str) -> str: statute_pattern = r'\b(?:[A-Z]{2,}\.?\s+(?:Rev\.\s+)?Stat\.?|Code(?:\s+Ann\.?)?|Ann\.?\s+Laws|Statutes|CCP|USC|ILCS|Civ\.\s+Code|Penal\s+Code|Gen\.\s+Oblig\.\s+Law|R\.?S\.?|P\.?L\.?)\s+§\s*[\d\-]+(?:\.\d+)?(?:[\(\w\.\)]+)?|Title\s+\d+\s+USC\s+§\s*\d+(?:-\d+)?\b' statutes = re.findall(statute_pattern, text, re.IGNORECASE) valid_statutes = [] for statute in statutes: statute = statute.strip() if '§' in statute and any(char.isdigit() for char in statute): if not re.match(r'^\([\w\.]+\)$', statute) and 'http' not in statute: if len(statute) > 5: valid_statutes.append(statute) if valid_statutes: seen = set() unique_statutes = [s for s in valid_statutes if not (s.rstrip('.,;') in seen or seen.add(s.rstrip('.,;')))] logging.info(f"Extracted {len(unique_statutes)} unique statutes.") return "\n".join(f"- {s}" for s in unique_statutes) logging.info("No statutes found matching the pattern in the context.") return "No specific statutes found in the provided context." @lru_cache(maxsize=50) def process_query_cached(self, query: str, state: str, openai_api_key: str, n_results: int = 5) -> Dict[str, any]: logging.info(f"Processing query (cache key: '{query}'|'{state}'|key_hidden) with n_results={n_results}") if not state or state == "Select a state..." or "Error" in state: logging.warning("No valid state provided for query.") return {"answer": "
Error: Please select a valid state.
", "context_used": "N/A - Invalid Input"} if not query or not query.strip(): logging.warning("No query provided.") return {"answer": "
Error: Please enter your question.
", "context_used": "N/A - Invalid Input"} if not openai_api_key or not openai_api_key.strip() or not openai_api_key.startswith("sk-"): logging.warning("No valid OpenAI API key provided.") return {"answer": "
Error: Please provide a valid OpenAI API key (starting with 'sk-'). Get one from OpenAI.
", "context_used": "N/A - Invalid Input"} try: logging.info("Initializing temporary LLM and Chain for this query...") temp_llm = ChatOpenAI( temperature=0.2, openai_api_key=openai_api_key, model_name="gpt-3.5-turbo", max_tokens=1500, request_timeout=45 ) temp_chain = LLMChain(llm=temp_llm, prompt=self.prompt_template) logging.info("Temporary LLM and Chain initialized successfully.") except Exception as e: logging.error(f"LLM Initialization failed: {str(e)}", exc_info=True) error_msg = "Error: Failed to initialize AI model. Please check your network connection and API key validity." if "authentication" in str(e).lower(): error_msg = "Error: OpenAI API Key is invalid or expired. Please check your key." return {"answer": f"
{error_msg}
Details: {str(e)}
", "context_used": "N/A - LLM Init Failed"} context = "No relevant context found." statutes_from_context = "Statute retrieval skipped due to context issues." try: logging.info(f"Querying Vector DB for query: '{query[:50]}...' in state '{state}'...") results = self.vector_db.query(query, state=state, n_results=n_results) logging.info(f"Vector DB query successful for state '{state}'. Processing results...") context_parts = [] doc_results = results.get("document_results", {}) docs = doc_results.get("documents", [[]])[0] metadatas = doc_results.get("metadatas", [[]])[0] if docs and metadatas and len(docs) == len(metadatas): logging.info(f"Found {len(docs)} document chunks.") for i, doc_content in enumerate(docs): metadata = metadatas[i] state_label = metadata.get('state', 'Unknown State') chunk_id = metadata.get('chunk_id', 'N/A') context_parts.append(f"**Source: Document Chunk {chunk_id} (State: {state_label})**\n{doc_content}") state_results_data = results.get("state_results", {}) state_docs = state_results_data.get("documents", [[]])[0] state_metadatas = state_results_data.get("metadatas", [[]])[0] if state_docs and state_metadatas and len(state_docs) == len(state_metadatas): logging.info(f"Found {len(state_docs)} state summary documents.") for i, state_doc_content in enumerate(state_docs): metadata = state_metadatas[i] state_label = metadata.get('state', state) context_parts.append(f"**Source: State Summary (State: {state_label})**\n{state_doc_content}") if context_parts: context = "\n\n---\n\n".join(context_parts) logging.info(f"Constructed context with {len(context_parts)} parts. Length: {len(context)} chars.") try: statutes_from_context = self.extract_statutes(context) except Exception as e: logging.error(f"Error extracting statutes: {e}", exc_info=True) statutes_from_context = "Error extracting statutes from context." else: logging.warning("No relevant context parts found from vector DB query.") context = "No relevant context could be retrieved from the knowledge base for this query and state. The AI will answer from its general knowledge." statutes_from_context = "No specific statutes found as no context was retrieved." except Exception as e: logging.error(f"Vector DB query/context processing failed: {str(e)}", exc_info=True) context = f"Warning: Error retrieving documents from the knowledge base ({str(e)}). The AI will attempt to answer from its general knowledge, which may be less specific or accurate." statutes_from_context = "Statute retrieval skipped due to error retrieving context." try: logging.info("Invoking LLMChain with constructed input...") llm_input = {"query": query, "context": context, "state": state, "statutes": statutes_from_context} answer_dict = temp_chain.invoke(llm_input) answer_text = answer_dict.get('text', '').strip() if not answer_text: logging.warning("LLM returned an empty answer.") answer_text = "
The AI model returned an empty response. This might be due to the query, context limitations, or temporary issues. Please try rephrasing your question or try again later.
" else: logging.info("LLM generated answer successfully.") return {"answer": answer_text, "context_used": context} except Exception as e: logging.error(f"LLM processing failed: {str(e)}", exc_info=True) error_message = "Error: AI answer generation failed." details = f"Details: {str(e)}" if "authentication" in str(e).lower(): error_message = "Error: Authentication failed. Please double-check your OpenAI API key." details = "" elif "rate limit" in str(e).lower(): error_message = "Error: You've exceeded your OpenAI API rate limit or quota. Please check your usage and plan limits, or wait and try again." details = "" elif "context length" in str(e).lower(): error_message = "Error: The request was too long for the AI model. This can happen with very complex questions or extensive retrieved context." details = "Try simplifying your question or asking about a more specific aspect." elif "timeout" in str(e).lower(): error_message = "Error: The request to the AI model timed out. The service might be busy." details = "Please try again in a few moments." formatted_error = f"
{error_message}
" if details: formatted_error += f"
{details}
" return {"answer": formatted_error, "context_used": context} def process_query(self, query: str, state: str, openai_api_key: str, n_results: int = 5) -> Dict[str, any]: return self.process_query_cached(query.strip(), state, openai_api_key.strip(), n_results) def get_states(self) -> List[str]: try: states = self.vector_db.get_states() if not states: logging.warning("No states retrieved from vector_db. Returning empty list.") return [] valid_states = sorted(list(set(s for s in states if s and isinstance(s, str) and s != "Select a state..."))) logging.info(f"Retrieved {len(valid_states)} unique, valid states from VectorDatabase.") return valid_states except Exception as e: logging.error(f"Failed to get states from VectorDatabase: {str(e)}", exc_info=True) return ["Error: Could not load states"] def load_pdf(self, pdf_path: str) -> int: if not os.path.exists(pdf_path): logging.error(f"PDF file not found at path: {pdf_path}") raise FileNotFoundError(f"PDF file not found: {pdf_path}") try: logging.info(f"Attempting to load/verify data from PDF: {pdf_path}") num_states_processed = self.vector_db.process_and_load_pdf(pdf_path) doc_count = self.vector_db.document_collection.count() state_count = self.vector_db.state_collection.count() total_items = doc_count + state_count if total_items > 0: logging.info(f"Vector DB contains {total_items} items ({doc_count} docs, {state_count} states). PDF processed or data already existed.") current_states = self.get_states() return len(current_states) if current_states and "Error" not in current_states[0] else 0 else: logging.warning(f"PDF processing completed, but the vector database appears empty. Check PDF content and processing logs.") return 0 except Exception as e: logging.error(f"Failed to load or process PDF '{pdf_path}': {str(e)}", exc_info=True) raise RuntimeError(f"Failed to process PDF '{pdf_path}': {e}") from e # --- GRADIO INTERFACE --- def gradio_interface(self): # Wrapper function for the Gradio interface logic def query_interface_wrapper(api_key: str, query: str, state: str) -> str: logging.info(f"Gradio interface received query: '{query[:50]}...', state: '{state}'") # Re-validate inputs robustly if not api_key or not api_key.strip() or not api_key.startswith("sk-"): return "
Please provide a valid OpenAI API key (starting with 'sk-'). Get one here.
" if not state or state == "Select a state..." or "Error" in state: return "
Please select a valid state from the dropdown.
" if not query or not query.strip(): return "
Please enter your question in the text box.
" # Call the core processing logic result = self.process_query(query=query, state=state, openai_api_key=api_key) # Format the response for display answer = result.get("answer", "
An unexpected error occurred, and no answer was generated. Please check the logs or try again.
") # Add a header *only* if the answer is not an error message itself if not "
" in answer: formatted_response = f"

Response for {state}

{answer}" else: formatted_response = answer # Pass through error messages directly # Log context length for debugging (optional) context_used = result.get("context_used", "N/A") if isinstance(context_used, str) and "N/A" not in context_used: logging.debug(f"Context length used for query: {len(context_used)} characters.") else: logging.debug(f"No context was used or available for this query ({context_used}).") return formatted_response # --- Get Available States for Dropdown --- try: available_states_list = self.get_states() if not available_states_list or "Error" in available_states_list[0]: dropdown_choices = ["Error: Could not load states"] initial_value = dropdown_choices[0] logging.error("Could not load states for dropdown. UI will show error.") else: dropdown_choices = ["Select a state..."] + available_states_list initial_value = dropdown_choices[0] except Exception as e: logging.error(f"Unexpected critical error getting states: {e}", exc_info=True) dropdown_choices = ["Error: Critical failure loading states"] initial_value = dropdown_choices[0] # --- Prepare Example Queries --- example_queries_base = [ ["What are the rules for security deposit returns?", "California"], ["Can a landlord enter my apartment without notice?", "New York"], ["My landlord hasn't made necessary repairs. What can I do?", "Texas"], ["What are the limits on rent increases in my state?", "Florida"], ["Is my lease automatically renewed if I don't move out?", "Illinois"], ["What happens if I break my lease early?", "Washington"] ] example_queries = [] if available_states_list and "Error" not in available_states_list[0]: loaded_states_set = set(available_states_list) example_queries = [ex for ex in example_queries_base if ex[1] in loaded_states_set] if not example_queries: fallback_state = available_states_list[0] if available_states_list and "Error" not in available_states_list[0] else "California" example_queries.append(["What basic rights do tenants have?", fallback_state]) # --- Refined Custom CSS --- # Focus: Unified background, distinct white cards, proper centering, refined examples table custom_css = """ @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500;700&display=swap'); /* --- Base & Body --- */ body, .gradio-container { font-family: 'Roboto', sans-serif !important; background-color: #F5F7FA !important; /* Light grey base background */ color: #1F2A44; margin: 0; padding: 0; min-height: 100vh; font-size: 16px; /* Base font size */ -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } * { box-sizing: border-box; } /* --- Main Content Container --- */ .gradio-container > .flex.flex-col { /* Target the main content column */ max-width: 960px; /* Slightly wider max-width */ margin: 0 auto !important; /* Center the column */ padding: 3rem 1.5rem !important; /* More vertical padding */ gap: 2.5rem !important; /* Consistent gap between sections */ background-color: transparent !important; /* Ensure container itself is transparent */ } /* --- Card Styling (Applied to Groups) --- */ .card-style { background-color: #FFFFFF !important; /* White background for cards */ border: 1px solid #E5E7EB !important; /* Subtle border */ border-radius: 12px !important; padding: 2rem !important; /* Consistent padding inside cards */ box-shadow: 0 4px 12px rgba(101, 119, 134, 0.08) !important; /* Refined shadow */ overflow: hidden; /* Prevent content spill */ } /* Remove default Gradio Group padding if using custom padding */ .gradio-group { padding: 0 !important; border: none !important; background: none !important; box-shadow: none !important; } /* --- Header Section --- */ .header-section { background-color: transparent !important; /* Header blends */ padding: 1rem 0 !important; text-align: center !important; /* Center align all content */ border: none !important; box-shadow: none !important; } .header-logo { font-size: 2.8rem; color: #2563EB; margin-bottom: 0.75rem; display: block; /* Ensure centering */ } .header-title { font-size: 2rem; /* Larger title */ font-weight: 700; color: #111827; /* Darker title */ margin: 0 0 0.25rem 0; } .header-tagline { font-size: 1.1rem; color: #4B5563; margin: 0; } /* --- Introduction Section --- */ /* Uses card-style defined above */ .intro-card h3 { font-size: 1.5rem; font-weight: 600; color: #0369A1; /* Blue heading */ margin: 0 0 1rem 0; padding-bottom: 0.5rem; border-bottom: 1px solid #E0F2FE; /* Light blue underline */ } .intro-card p { font-size: 1rem; line-height: 1.6; color: #374151; /* Standard text color */ margin: 0 0 0.75rem 0; } .intro-card a { color: #0369A1; text-decoration: underline; font-weight: 500; } .intro-card a:hover { color: #0284C7; } .intro-card strong { font-weight: 600; color: #1F2A44; } /* --- Input Form Section --- */ /* Uses card-style */ .input-form-card h3 { font-size: 1.4rem; font-weight: 600; color: #1F2A44; margin: 0 0 1.75rem 0; padding-bottom: 0.75rem; border-bottom: 1px solid #E5E7EB; } .input-field-group { margin-bottom: 1.5rem; } .input-row { display: flex; gap: 1.5rem; flex-wrap: wrap; margin-bottom: 1.5rem; } .input-field { flex: 1; min-width: 220px; } /* Input Elements */ .gradio-textbox textarea, .gradio-dropdown select, .gradio-textbox input[type=password] { border: 1px solid #D1D5DB !important; border-radius: 8px !important; padding: 0.8rem 1rem !important; font-size: 1rem !important; /* Make inputs slightly larger */ background-color: #F9FAFB !important; color: #1F2A44 !important; transition: border-color 0.2s ease, box-shadow 0.2s ease; width: 100% !important; } .gradio-textbox textarea { min-height: 90px; } .gradio-textbox textarea:focus, .gradio-dropdown select:focus, .gradio-textbox input[type=password]:focus { border-color: #2563EB !important; box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.15) !important; outline: none !important; background-color: #FFFFFF !important; } .gradio-input-label, .gradio-output-label { /* Label styling */ font-size: 0.9rem !important; font-weight: 500 !important; color: #374151 !important; margin-bottom: 0.5rem !important; display: block !important; } .gradio-input-info { /* Info text */ font-size: 0.85rem !important; color: #6B7280 !important; margin-top: 0.3rem; } /* Buttons */ .button-row { display: flex; gap: 1rem; margin-top: 1.5rem; flex-wrap: wrap; justify-content: flex-end; } .gradio-button { border-radius: 8px !important; padding: 0.75rem 1.5rem !important; font-size: 0.95rem !important; font-weight: 500 !important; border: none !important; cursor: pointer; transition: background-color 0.2s ease, transform 0.1s ease, box-shadow 0.2s ease; } .gradio-button:hover:not(:disabled) { transform: translateY(-1px); box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); } .gradio-button:active:not(:disabled) { transform: scale(0.98); box-shadow: none; } .gradio-button:disabled { background: #E5E7EB !important; color: #9CA3AF !important; cursor: not-allowed; } .gr-button-primary { background-color: #2563EB !important; color: #FFFFFF !important; } .gr-button-primary:hover:not(:disabled) { background-color: #1D4ED8 !important; } .gr-button-secondary { background-color: #F3F4F6 !important; color: #374151 !important; border: 1px solid #D1D5DB !important; } .gr-button-secondary:hover:not(:disabled) { background-color: #E5E7EB !important; border-color: #9CA3AF !important; } /* --- Output Section --- */ /* Uses card-style */ .output-card .response-header { /* Style the H3 we add in Python */ font-size: 1.3rem; font-weight: 600; color: #1F2A44; margin: 0 0 0.75rem 0; } .output-card .divider { /* Style the HR we add */ border: none; border-top: 1px solid #E5E7EB; margin: 1rem 0 1.5rem 0; } .output-card .output-content-wrapper { /* Wrapper for the markdown content */ font-size: 1rem; line-height: 1.7; color: #374151; } .output-card .output-content-wrapper p { margin-bottom: 1rem; } .output-card .output-content-wrapper ul, .output-card .output-content-wrapper ol { margin-left: 1.5rem; margin-bottom: 1rem; padding-left: 1rem; } .output-card .output-content-wrapper li { margin-bottom: 0.5rem; } .output-card .output-content-wrapper strong, .output-card .output-content-wrapper b { font-weight: 600; color: #111827; } .output-card .output-content-wrapper a { color: #2563EB; text-decoration: underline; } .output-card .output-content-wrapper a:hover { color: #1D4ED8; } /* Error message styling */ .output-card .error-message { background-color: #FEF2F2; border: 1px solid #FECACA; border-left: 4px solid #F87171; border-radius: 8px; padding: 1rem 1.25rem; color: #B91C1C; font-weight: 500; margin-top: 0.5rem; } .output-card .error-details { font-size: 0.9rem; color: #991B1B; margin-top: 0.5rem; font-style: italic; } /* Placeholder text */ .output-card .placeholder { color: #9CA3AF; font-style: italic; text-align: center; padding: 2rem 1rem; display: block; } /* --- Examples Section --- */ /* Uses card-style */ .examples-card .gr-examples-header { /* Style the header Gradio adds */ font-size: 1.3rem !important; font-weight: 600 !important; color: #1F2A44 !important; margin: 0 0 1.5rem 0 !important; padding-bottom: 0.75rem !important; border-bottom: 1px solid #E5E7EB !important; } /* Style the TABLE generated by gr.Examples */ .examples-card .gr-examples-table { border-collapse: collapse !important; width: 100% !important; } .examples-card .gr-examples-table th, .examples-card .gr-examples-table td { text-align: left !important; padding: 0.75rem 1rem !important; border: 1px solid #E5E7EB !important; font-size: 0.95rem !important; color: #374151 !important; background-color: transparent !important; } .examples-card .gr-examples-table th { font-weight: 500 !important; background-color: #F9FAFB !important; color: #1F2A44 !important; } /* Style the example *rows* when clickable */ .examples-card .gr-examples-table tr { cursor: pointer; transition: background-color 0.2s ease; } .examples-card .gr-examples-table tr:hover td { background-color: #F3F4F6 !important; } /* --- Footer Section --- */ .footer-section { background-color: transparent !important; border-top: 1px solid #E5E7EB !important; padding: 2rem 1rem !important; margin-top: 1rem !important; /* Space above footer */ text-align: center !important; color: #6B7280 !important; font-size: 0.9rem !important; line-height: 1.6 !important; box-shadow: none !important; border-radius: 0 !important; } .footer-section strong { color: #374151; font-weight: 500; } .footer-section a { color: #2563EB; text-decoration: none; font-weight: 500; } .footer-section a:hover { color: #1D4ED8; text-decoration: underline; } /* --- Accessibility & Focus --- */ :focus-visible { outline: 2px solid #2563EB !important; outline-offset: 2px; box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.2) !important; } /* Remove default Gradio focus on button internal span */ .gradio-button span:focus { outline: none !important; } /* --- Responsive Adjustments --- */ @media (max-width: 768px) { .gradio-container > .flex.flex-col { padding: 2rem 1rem !important; gap: 2rem !important; } .card-style { padding: 1.5rem !important; } .header-title { font-size: 1.8rem; } .header-tagline { font-size: 1rem; } .input-row { flex-direction: column; gap: 1rem; margin-bottom: 1rem; } .button-row { justify-content: center; } } @media (max-width: 480px) { body { font-size: 15px; } .gradio-container > .flex.flex-col { padding: 1.5rem 1rem !important; gap: 1.5rem !important; } .card-style { padding: 1.25rem !important; border-radius: 10px !important;} .header-logo { font-size: 2.5rem; margin-bottom: 0.5rem;} .header-title { font-size: 1.5rem; } .header-tagline { font-size: 0.95rem; } .intro-card h3, .input-form-card h3, .output-card .response-header, .examples-card .gr-examples-header { font-size: 1.2rem !important; margin-bottom: 1rem !important; } .gradio-textbox textarea, .gradio-dropdown select, .gradio-textbox input[type=password] { font-size: 0.95rem !important; padding: 0.75rem !important; } .gradio-button { width: 100%; padding: 0.7rem 1.2rem !important; font-size: 0.9rem !important; } .button-row { flex-direction: column; gap: 0.75rem; } .footer-section { font-size: 0.85rem; padding: 1.5rem 1rem !important; } .examples-card .gr-examples-table th, .examples-card .gr-examples-table td { padding: 0.6rem 0.8rem !important; font-size: 0.9rem !important;} } /* Gradio Specific Overrides (Use sparingly) */ /* Force main container gap */ .gradio-container > .flex { gap: 2.5rem !important; } /* Ensure no weird margins collapse */ .gradio-markdown > *:first-child { margin-top: 0; } .gradio-markdown > *:last-child { margin-bottom: 0; } /* Remove border from dropdown wrapper if needed */ .gradio-dropdown { border: none !important; padding: 0 !important; } /* Remove border from textbox wrapper */ .gradio-textbox { border: none !important; padding: 0 !important; } """ # --- Gradio Blocks Layout --- with gr.Blocks(css=custom_css, title="Landlord-Tenant Rights Assistant") as demo: # The main container class is applied implicitly by Gradio, CSS targets it # Header Section (No Card Style) with gr.Group(elem_classes="header-section"): # Use Group for structure, styled via class gr.Markdown( """

Landlord-Tenant Rights Assistant

Your AI-powered guide to U.S. landlord-tenant laws

""", elem_id="app-title" ) # Introduction Section (Card Style) with gr.Group(elem_classes="card-style intro-card"): gr.Markdown( """

Discover Your Rights

Get accurate, AI-powered answers to your questions about landlord-tenant laws. Select your state, provide an OpenAI API key, and ask your question below.

Need an API key? Get one free here from OpenAI.

Note: This tool is for informational purposes only. Always consult a licensed attorney for legal advice specific to your situation.

""", elem_id="app-description" ) # Examples Section (Card Style) # Input Form Section (Card Style) with gr.Group(elem_classes="card-style input-form-card"): gr.Markdown("

Query Section

", elem_id="form-heading") with gr.Column(elem_classes="input-field-group"): api_key_input = gr.Textbox( label="OpenAI API Key", type="password", placeholder="Enter your API key (e.g., sk-...)", info="Required to process your question. Securely used per request, not stored.", elem_id="api-key-input", lines=1 ) with gr.Row(elem_classes="input-row"): with gr.Column(elem_classes="input-field"): query_input = gr.Textbox( label="Curious about landlord-tenant laws in your state? Ask away!", placeholder="E.g., What are the rules for security deposit returns in my state?", lines=4, max_lines=8, elem_id="query-input" ) with gr.Column(elem_classes="input-field"): state_input = gr.Dropdown( label="Select State", choices=dropdown_choices, value=initial_value, allow_custom_value=False, elem_id="state-dropdown" ) with gr.Row(elem_classes="button-row"): clear_button = gr.Button( "Clear Inputs", variant="secondary", elem_id="clear-button", elem_classes=["gr-button-secondary"] ) submit_button = gr.Button( "Submit Question", variant="primary", elem_id="submit-button", elem_classes=["gr-button-primary"] ) # Output Section (Card Style) with gr.Group(elem_classes="card-style output-card"): # Wrap the output markdown for better targeting if needed with gr.Column(): # Add column wrapper if needed for spacing/styling output = gr.Markdown( value="
The response will appear here after submitting a question.
", elem_id="output-content", elem_classes="output-content-wrapper" # Apply styling to this wrapper ) # Example Questions Section (Card Style) if example_queries: with gr.Group(elem_classes="card-style examples-card"): gr.Examples( examples=example_queries, inputs=[query_input, state_input], label="Example Sample Questions", # Uses .gr-examples-header class examples_per_page=6 ) else: with gr.Group(elem_classes="card-style examples-card"): # Still use card style for consistency gr.Markdown( "
Sample questions could not be loaded. Please ensure states are available.
" ) # Footer Section (No Card Style) with gr.Group(elem_classes="footer-section"): gr.Markdown( """ **Disclaimer**: This tool is for informational purposes only and does not constitute legal advice.

Developed by **Nischal Subedi**. Connect on LinkedIn or explore insights at Substack. """, elem_id="app-footer" ) # --- Event Listeners --- submit_button.click( fn=query_interface_wrapper, inputs=[api_key_input, query_input, state_input], outputs=output, api_name="submit_query" ) clear_button.click( fn=lambda: ( "", "", initial_value, "
Inputs cleared. Ready for your next question.
" ), inputs=[], outputs=[api_key_input, query_input, state_input, output] ) logging.info("Refined Gradio interface created successfully.") return demo # --- Main Execution Block --- if __name__ == "__main__": logging.info("Starting Landlord-Tenant Rights Bot application...") try: SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) DEFAULT_PDF_PATH = os.path.join(SCRIPT_DIR, "tenant-landlord.pdf") DEFAULT_DB_PATH = os.path.join(SCRIPT_DIR, "chroma_db") PDF_PATH = os.getenv("PDF_PATH", DEFAULT_PDF_PATH) VECTOR_DB_PATH = os.getenv("VECTOR_DB_PATH", DEFAULT_DB_PATH) os.makedirs(os.path.dirname(VECTOR_DB_PATH), exist_ok=True) os.makedirs(os.path.dirname(PDF_PATH), exist_ok=True) logging.info(f"Using PDF path: {PDF_PATH}") logging.info(f"Using Vector DB path: {VECTOR_DB_PATH}") if not os.path.exists(PDF_PATH): logging.error(f"FATAL: PDF file not found at the specified path: {PDF_PATH}") print(f"\n--- CONFIGURATION ERROR ---") print(f"The required PDF file ('{os.path.basename(PDF_PATH)}') was not found at:") print(f" {PDF_PATH}") print(f"Please ensure the file exists or set 'PDF_PATH' environment variable.") print(f"---------------------------\n") exit(1) logging.info("Initializing Vector Database...") vector_db_instance = VectorDatabase(persist_directory=VECTOR_DB_PATH) logging.info("Initializing RAG System...") rag = RAGSystem(vector_db=vector_db_instance) logging.info(f"Loading/Verifying data from PDF: {PDF_PATH}") states_loaded_count = rag.load_pdf(PDF_PATH) doc_count = vector_db_instance.document_collection.count() if vector_db_instance.document_collection else 0 state_count = vector_db_instance.state_collection.count() if vector_db_instance.state_collection else 0 total_items = doc_count + state_count if total_items > 0: logging.info(f"Data loading/verification complete. Vector DB contains {total_items} items. Found {states_loaded_count} distinct states.") else: logging.warning("Potential issue: PDF processed but Vector DB appears empty. Check PDF content/format and logs.") print("\nWarning: No data loaded from PDF or found in DB. Application might not function correctly.\n") logging.info("Setting up Gradio interface...") app_interface = rag.gradio_interface() SERVER_PORT = 7860 logging.info(f"Launching Gradio app on http://0.0.0.0:{SERVER_PORT}") print("\n--- Gradio App Running ---") print(f"Access the interface in your browser at: http://localhost:{SERVER_PORT} or http://:{SERVER_PORT}") print("--------------------------\n") app_interface.launch( server_name="0.0.0.0", server_port=SERVER_PORT, share=False, # enable_queue=True # Consider for higher traffic ) except FileNotFoundError as fnf_error: logging.error(f"Initialization failed due to a missing file: {str(fnf_error)}", exc_info=True) print(f"\n--- STARTUP ERROR: File Not Found ---") print(f"{str(fnf_error)}") print(f"---------------------------------------\n") exit(1) except ImportError as import_error: logging.error(f"Import error: {str(import_error)}. Check dependencies.", exc_info=True) print(f"\n--- STARTUP ERROR: Missing Dependency ---") print(f"Import Error: {str(import_error)}") print(f"Please ensure required libraries are installed (e.g., pip install -r requirements.txt).") print(f"-----------------------------------------\n") exit(1) except RuntimeError as runtime_error: logging.error(f"A runtime error occurred during setup: {str(runtime_error)}", exc_info=True) print(f"\n--- STARTUP ERROR: Runtime Problem ---") print(f"Runtime Error: {str(runtime_error)}") print(f"Check logs for details, often related to data loading or DB setup.") print(f"--------------------------------------\n") exit(1) except Exception as e: logging.error(f"An unexpected error occurred during application startup: {str(e)}", exc_info=True) print(f"\n--- FATAL STARTUP ERROR ---") print(f"An unexpected error stopped the application: {str(e)}") print(f"Check logs for detailed traceback.") print(f"---------------------------\n") exit(1)