app.py

import os
import logging
from typing import Dict, List, Optional
from functools import lru_cache
import re

import gradio as gr
import gradio.themes as themes # Import gradio.themes

try:
    # Assuming vector_db.py exists in the same directory or is installed
    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 (Processing Logic - kept intact as requested) ---
class RAGSystem:
    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 CONCONTEXT ---
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 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": "<div class='error-message'>Error: Please select a valid state.</div>", "context_used": "N/A - Invalid Input"}
        if not query or not query.strip():
            logging.warning("No query provided.")
            return {"answer": "<div class='error-message'>Error: Please enter your question.</div>", "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": "<div class='error-message'>Error: Please provide a valid OpenAI API key (starting with 'sk-'). Get one from <a href='https://platform.openai.com/api-keys' target='_blank'>OpenAI</a>.</div>", "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"<div class='error-message'>{error_msg}</div><div class='error-details'>Details: {str(e)}</div>", "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 = "<div class='error-message'><span class='error-icon'>⚠️</span>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.</div>"
            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"<div class='error-message'><span class='error-icon'>❌</span>{error_message}</div>"
            if details:
                formatted_error += f"<div class='error-details'>{details}</div>"

            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}")
            # Assuming process_and_load_pdf is part of VectorDatabase and correctly implemented
            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 (Refactored to use earneleh/paris theme) ---
    def gradio_interface(self):
        def query_interface_wrapper(api_key: str, query: str, state: str) -> str:
            # Basic client-side validation for immediate feedback (redundant but good UX)
            if not api_key or not api_key.strip() or not api_key.startswith("sk-"):
                return "<div class='error-message'><span class='error-icon'>⚠️</span>Please provide a valid OpenAI API key (starting with 'sk-'). <a href='https://platform.openai.com/api-keys' target='_blank'>Get one free from OpenAI</a>.</div>"
            if not state or state == "Select a state..." or "Error" in state:
                return "<div class='error-message'><span class='error-icon'>⚠️</span>Please select a valid state from the dropdown.</div>"
            if not query or not query.strip():
                return "<div class='error-message'><span class='error-icon'>⚠️</span>Please enter your question in the text box.</div>"

            # Call the core processing logic
            result = self.process_query(query=query, state=state, openai_api_key=api_key)
            answer = result.get("answer", "<div class='error-message'><span class='error-icon'>⚠️</span>An unexpected error occurred.</div>")

            # Check if the answer already contains an error message (from deeper within process_query)
            if "<div class='error-message'>" in answer:
                return answer # Return the pre-formatted error message directly
            else:
                # Format the successful response with the new UI structure
                formatted_response = f"<div class='response-header'><span class='response-icon'>📜</span>Response for {state}</div><hr class='divider'>{answer}"
                return formatted_response

        try:
            available_states_list = self.get_states()
            dropdown_choices = ["Select a state..."] + (available_states_list if available_states_list and "Error" not in available_states_list[0] else ["Error: States unavailable"])
            initial_value = dropdown_choices[0]
        except Exception: # Catch-all for safety
            dropdown_choices = ["Error: Critical failure loading states"]
            initial_value = dropdown_choices[0]

        # Define example queries, filtering based on available states
        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"],
            ["How much notice must a landlord give to raise rent?", "Florida"],
            ["What is an implied warranty of habitability?", "Illinois"]
        ]
        example_queries = []
        if available_states_list and "Error" not in available_states_list[0] and len(available_states_list) > 0:
            loaded_states_set = set(available_states_list)
            # Filter for examples whose state is in the loaded states
            example_queries = [ex for ex in example_queries_base if ex[1] in loaded_states_set]
            # Add a generic example if no specific state examples match or if list is empty
            if not example_queries:
                example_queries.append(["What basic rights do tenants have?", available_states_list[0] if available_states_list else "California"])
        else: # Fallback if states list is problematic
            example_queries.append(["What basic rights do tenants have?", "California"])


        # --- Minimal Custom CSS for structural/behavioral overrides not covered by the theme ---
        # This CSS focuses on animations, specific layout tweaks, and hiding unwanted Gradio default elements.
        custom_css = """
        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Playfair+Display:wght@700;800;900&display=swap');
        /* Animation for header */
        @keyframes fadeInSlideDown { from { opacity: 0; transform: translateY(-40px); } to { opacity: 1; transform: translateY(0); } }
        /* General layout adjustments to Gradio's base container */
        .gradio-container > .flex.flex-col {
            max-width: 1120px;
            margin: 0 auto !important;
            padding: 0 !important; /* Remove default Gradio padding */
            gap: 0 !important; /* Remove default Gradio gap */
        }
        /* Header specific styling */
        .app-header-wrapper {
            padding: 4.5rem 3.5rem !important;
            text-align: center !important;
            border-bottom-left-radius: 24px;
            border-bottom-right-radius: 24px;
            position: relative;
            overflow: hidden;
            z-index: 10;
            margin-bottom: 2rem; /* Reduced margin */
            border: 3px solid var(--border-color-primary) !important; /* Thicker border */
            border-top: none;
            max-width: 1120px;
            margin-left: auto;
            margin-right: auto;
            width: 100%;
            /* Enhanced background */
            background-image: linear-gradient(135deg, var(--background-fill-primary) 0%, var(--background-fill-secondary) 100%);
            box-shadow: 0 10px 30px rgba(0,0,0,0.4); /* More pronounced shadow */
        }
        .app-header { display: flex; flex-direction: column; align-items: center; position: relative; z-index: 1; }
        .app-header-logo {
            font-size: 5.5rem; margin-bottom: 0.8rem; line-height: 1;
            filter: drop-shadow(0 0 15px var(--primary-500));
            transform: translateY(-40px); opacity: 0;
            animation: fadeInSlideDown 1.5s ease-out forwards; animation-delay: 0.3s;
        }
        .app-header-title {
            font-family: 'Playfair Display', serif !important;
            font-size: 4.2rem; font-weight: 900;
            margin: 0 0 0.8rem 0; letter-spacing: -0.07em;
            text-shadow: 0 8px 16px rgba(0,0,0,0.5);
            transform: translateY(-40px); opacity: 0;
            animation: fadeInSlideDown 1.5s ease-out forwards; animation-delay: 0.6s;
        }
        .app-header-tagline {
            font-family: 'Inter', sans-serif !important;
            font-size: 1.6rem; font-weight: 300;
            opacity: 0.9; max-width: 900px;
            transform: translateY(-40px); opacity: 0;
            animation: fadeInSlideDown 1.5s ease-out forwards; animation-delay: 0.9s;
        }
        /* Main dashboard container */
        .main-dashboard-container {
            border-radius: 24px;
            border: 3px solid var(--border-color-primary) !important; /* Thicker border */
            padding: 3.5rem !important;
            margin: 0 auto 0.6rem auto; /* Reduced margin-bottom */
            z-index: 1; position: relative;
            display: flex; flex-direction: column; gap: 2rem; /* Reduced gap between boxes */
            max-width: 1120px;
        }
        /* Card sections within the dashboard */
        .dashboard-card-section {
            border-radius: 12px;
            border: 3px solid var(--border-color-primary); /* Thicker border */
            padding: 2.5rem;
            box-shadow: inset 0 0 10px rgba(0,0,0,0.2);
            display: flex; flex-direction: column; gap: 1.5rem;
        }
        /* Light mode specific inner shadow for card sections */
        @media (prefers-color-scheme: light) {
            .dashboard-card-section { box-shadow: inset 0 0 8px rgba(0,0,0,0.05); }
        }
        /* Section titles */
        .sub-section-title {
            font-family: 'Playfair Display', serif !important;
            font-size: 3.0rem !important; /* Increased font size */
            font-weight: 800 !important;
            text-align: center !important; /* Already centered, kept */
            margin-top: 1.5rem !important;
            margin-bottom: 0.8rem !important;
            display: block !important;
            width: 100% !important;
        }
        /* Text styling for custom markdown within cards */
        .dashboard-card-section p, .output-content-wrapper p {
            font-size: 1.15rem; line-height: 1.8;
            margin-bottom: 1.2rem;
        }
        .dashboard-card-section a, .output-content-wrapper a {
            text-decoration: none; font-weight: 500;
        }
        .dashboard-card-section a:hover, .output-content-wrapper a:hover { text-decoration: underline; }
        .dashboard-card-section strong, .output-content-wrapper strong { font-weight: 700; }
        /* Input layout */
        .input-field-group { margin-bottom: 1rem; }
        .input-row { display: flex; gap: 1.8rem; flex-wrap: wrap; margin-bottom: 1rem; }
        .input-field { flex: 1; }
        /* Button layout */
        .button-row { display: flex; gap: 2rem; margin-top: 2rem; flex-wrap: wrap; justify-content: flex-end; }
        .gradio-button {
            border-radius: 12px !important;
            padding: 1.2rem 2.8rem !important;
            font-size: 1.15rem !important;
            font-weight: 600 !important;
            border: 1px solid transparent !important;
            box-shadow: 0 6px 20px rgba(0,0,0,0.35);
            transition: all 0.4s cubic-bezier(0.0, 0.0, 0.2, 1);
        }
        .gradio-button:hover:not(:disabled) { transform: translateY(-6px); box-shadow: 0 12px 28px rgba(0,0,0,0.45) !important; }
        .gradio-button:active:not(:disabled) { transform: translateY(-3px); }
        .gradio-button:disabled {
            box-shadow: none !important;
            cursor: not-allowed;
        }
        .gr-button-secondary { /* Override for secondary button specific look */
            background: transparent !important;
            border: 2px solid var(--neutral-500) !important; /* Used neutral-500 for clearer border */
            box-shadow: none !important;
        }
        .gr-button-secondary:hover:not(:disabled) {
            background: var(--button-secondary-background-hover) !important;
            border-color: var(--primary-500) !important;
        }
        @media (prefers-color-scheme: light) {
            .gradio-button { box-shadow: 0 6px 20px rgba(0,0,0,0.1); }
            .gradio-button:hover:not(:disabled) { box-shadow: 0 12px 28px rgba(0,0,0,0.2) !important; }
        }
        /* Output card and placeholders */
        .output-card { padding: 0 !important; margin-top: 0 !important; margin-bottom: 0 !important; }
        .output-card .response-header {
            font-size: 1.8rem; font-weight: 700;
            margin: 0 0 1rem 0; display: flex; align-items: center; gap: 1.2rem;
        }
        .output-card .response-icon { font-size: 2rem; color: var(--primary-500); }
        .output-card .divider { border: none; border-top: 1px solid var(--border-color-primary); margin: 1.5rem 0 1.8rem 0; }
        .output-card .output-content-wrapper { font-size: 1.15rem; line-height: 1.8; }
        .output-card .output-content-wrapper p { margin-bottom: 1rem; }
        .output-card .output-content-wrapper ul, .output-card .output-content-wrapper ol { margin-left: 2.2rem; margin-bottom: 1.2rem; padding-left: 0; list-style-type: disc; }
        .output-card .output-content-wrapper ol { list-style-type: decimal; }
        .output-card .output-content-wrapper li { margin-bottom: 0.8rem; }
        .output-card .error-message {
            padding: 1.5rem 2rem; margin-top: 1.5rem; font-size: 1.1rem;
            border-radius: 12px;
            background: var(--error-background-fill) !important;
            color: var(--error-text-color) !important;
            border: 2px solid var(--error-border-color) !important;
            display: flex; align-items: flex-start; gap: 1.5em;
        }
        .output-card .error-message .error-icon { font-size: 1.8rem; line-height: 1; padding-top: 0.1em; }
        .output-card .error-details { font-size: 0.95rem; margin-top: 0.8rem; opacity: 0.9; word-break: break-word; }
        .output-card .placeholder {
            padding: 2.5rem 2rem; font-size: 1.2rem; border-radius: 12px;
            border: 3px dashed var(--border-color-primary);
            text-align: center; opacity: 0.8;
        }
        /* Examples table styling */
        .examples-section table.gr-samples-table {
            border-radius: 12px !important;
            border: 3px solid var(--border-color-primary) !important; /* Thicker border */
            overflow: hidden;
            box-shadow: inset 0 0 10px rgba(0,0,0,0.2);
        }
        @media (prefers-color-scheme: light) {
            .examples-section table.gr-samples-table { box-shadow: inset 0 0 8px rgba(0,0,0,0.05); }
        }
        .examples-section table.gr-samples-table th, .examples-section table.gr-samples-table td { padding: 1rem 1.2rem !important; font-size: 1.05rem !important; border: none !important; }
        .examples-section table.gr-samples-table th {
            background: var(--block-background-fill) !important;
            font-weight: 600 !important; text-align: left;
        }
        .examples-section table.gr-samples-table td {
            border-top: 1px solid var(--border-color-primary) !important;
            cursor: pointer;
        }
        .examples-section table.gr-samples-table tr:hover td { background: var(--hover-color) !important; }
        .examples-section table.gr-samples-table tr:first-child td { border-top: none !important; }
        /* Footer styling */
        .app-footer-wrapper {
            border-top: 3px solid var(--border-color-primary) !important; /* Thicker border */
            margin-top: 0.5rem;
            padding-top: 2.5rem;
            padding-bottom: 2.5rem;
            border-top-left-radius: 24px;
            border-top-right-radius: 24px;
            box-shadow: inset 0 8px 15px rgba(0,0,0,0.2);
            max-width: 1120px;
            margin-left: auto;
            margin-right: auto;
            width: 100%;
            display: flex !important;
            flex-direction: column !important;
            align-items: flex-start !important;
        }
        .app-footer {
            padding: 0 3.5rem !important;
            display: flex;
            flex-direction: column;
            align-items: stretch;
            width: 100%;
        }
        .app-footer p {
            font-size: 1.05rem !important;
            text-align: left !important;
            margin-bottom: 1rem;
            padding-left: 2rem; /* Added indent */
        }
        .app-footer a { font-weight: 500; }
        /* Hide default Gradio example labels and related elements for cleaner presentation */
        .gr-examples .gr-label, .gr-examples button.gr-button-filter, .gr-examples .label-wrap,
        .gr-examples div[data-testid*="label-text"], .gr-examples span[data-testid*="label-text"],
        .gr-examples div[class*="label"], .gr-examples .gr-example-label,
        .gr-examples .gr-box.gr-component.gradio-example > div:first-child:has(> span[data-testid]),
        .gr-examples .gr-box.gr-component.gradio-example > div:first-child > span,
        .gr-examples .gr-accordion-header, .gr-examples .gr-accordion-title, .gr-examples .gr-accordion-toggle-icon,
        .gr-examples .gr-accordion-header button, .gr-examples .gr-button.gr-button-filter,
        .gr-examples .gr-button.gr-button-primary.gr-button-filter,
        .gr-examples .gr-examples-header, .gr-examples .gr-examples-header > * {
            display: none !important; visibility: hidden !important; width: 0 !important; height: 0 !important;
            overflow: hidden !important; margin: 0 !important; padding: 0 !important; border: 0 !important;
            font-size: 0 !important; line-height: 0 !important; position: absolute !important;
            pointer-events: none !important;
        }
        /* Responsive Adjustments */
        @media (max-width: 1024px) {
            .gradio-container > .flex.flex-col { max-width: 960px; padding: 0 1.5rem !important; }
            .app-header-title { font-size: 3.8rem; } .app-header-tagline { font-size: 1.5rem; }
            .app-header-wrapper { padding: 2.5rem 3.5rem !important; margin-bottom: 1.8rem; border-bottom-left-radius: 12px; border-bottom-right-radius: 12px; }
            .main-dashboard-container { padding: 2.5rem !important; margin-bottom: 0.6rem; border-radius: 12px; gap: 1.8rem; } /* Adjusted gap */
            .dashboard-card-section { padding: 1.8rem; border-radius: 8px; }
            .sub-section-title { font-size: 2.5rem !important; margin-bottom: 0.7rem !important; } /* Adjusted font size */
            .input-row { gap: 1.5rem; } .input-field { min-width: 280px; }
            .gradio-textbox textarea { min-height: 160px; } .output-card .response-header { font-size: 1.7rem; }
            .examples-section { padding-top: 1.2rem; }
            .examples-section table.gr-samples-table th, .examples-section table.gr-samples-table td { padding: 0.9rem 1.1rem !important; }
            .app-footer-wrapper { margin-top: 0.6rem; border-top-left-radius: 12px; border-top-right-radius: 12px; }
            .app-footer { padding: 0 1.5rem !important; }
            .app-footer p { padding-left: 1.5rem; } /* Adjusted indent for smaller screens */
        }
        @media (max-width: 768px) {
            .gradio-container > .flex.flex-col { padding: 0 1rem !important; }
            .app-header-wrapper { padding: 1.8rem 2.5rem !important; margin-bottom: 1.5rem; border-bottom-left-radius: 12px; border-bottom-right-radius: 12px; }
            .app-header-logo { font-size: 4.5rem; margin-bottom: 0.6rem; } .app-header-title { font-size: 3.2rem; letter-spacing: -0.06em; }
            .app-header-tagline { font-size: 1.3rem; }
            .main-dashboard-container { padding: 1.8rem !important; margin-bottom: 0.5rem; border-radius: 12px; gap: 1.5rem; } /* Adjusted gap */
            .dashboard-card-section { padding: 1.5rem; border-radius: 8px; }
            .sub-section-title { font-size: 2.0rem !important; margin-top: 1rem !important; margin-bottom: 0.6rem !important; } /* Adjusted font size */
            .input-row { flex-direction: column; gap: 1rem; } .input-field { min-width: 100%; }
            .gradio-textbox textarea { min-height: 140px; } .button-row { justify-content: stretch; gap: 1rem; }
            .gradio-button { width: 100%; padding: 1.1rem 2rem !important; font-size: 1.1rem !important; }
            .output-card .response-header { font-size: 1.5rem; } .output-card .response-icon { font-size: 1.7rem; }
            .output-card .placeholder { padding: 2.5rem 1.5rem; font-size: 1.1rem; }
            .examples-section { padding-top: 1.2rem; }
            .examples-section table.gr-samples-table th, .examples-section table.gr-samples-table td { padding: 0.9rem 1.1rem !important; font-size: 1.0rem !important; }
            .app-footer-wrapper { margin-top: 0.6rem; border-top-left-radius: 12px; border-top-right-radius: 12px; padding-top: 2rem; padding-bottom: 2rem; }
            .app-footer { padding: 0 1rem !important; }
            .app-footer p { padding-left: 1rem; } /* Adjusted indent for smaller screens */
        }
        @media (max-width: 480px) {
            .gradio-container > .flex.flex-col { padding: 0 0.8rem !important; }
            .app-header-wrapper { padding: 1.2rem 1rem !important; margin-bottom: 1.2rem; border-bottom-left-radius: 8px; border-bottom-right-radius: 8px; }
            .app-header-logo { font-size: 3.8rem; margin-bottom: 0.5rem; } .app-header-title { font-size: 2.8rem; }
            .app-header-tagline { font-size: 1.1rem; }
            .main-dashboard-container { padding: 1.2rem !important; margin-bottom: 0.4rem; border-radius: 8px; gap: 1.2rem; } /* Adjusted gap */
            .dashboard-card-section { padding: 1rem; border-radius: 4px; }
            .sub-section-title { font-size: 1.8rem !important; margin-top: 0.8rem !important; margin-bottom: 0.5rem !important; } /* Adjusted font size */
            .gradio-textbox textarea, .gradio-dropdown select, .gradio-textbox input[type=password] { font-size: 1.05rem !important; padding: 1rem 1.2rem !important; }
            .gradio-textbox textarea { min-height: 120px; }
            .gradio-button { padding: 1rem 1.5rem !important; font-size: 1.0rem !important; }
            .output-card .response-header { font-size: 1.4rem; } .output-card .response-icon { font-size: 1.5rem; }
            .output-card .placeholder { padding: 2rem 1rem; font-size: 1.05rem; }
            .examples-section { padding-top: 0.8rem; }
            .examples-section table.gr-samples-table th, .examples-section table.gr-samples-table td { padding: 0.6rem 0.8rem !important; font-size: 0.95rem !important; }
            .app-footer-wrapper { margin-top: 0.4rem; border-top-left-radius: 8px; border-top-right-radius: 8px; padding-top: 1.5rem; padding-bottom: 1.5rem; }
            .app-footer { padding: 0 0.8rem !important; }
            .app-footer p { padding-left: 0.5rem; } /* Adjusted indent for smallest screens */
        }
        """

        with gr.Blocks(theme="earneleh/paris", css=custom_css, title="Landlord-Tenant Rights Assistant") as demo:
            # --- Header Section ---
            with gr.Group(elem_classes="app-header-wrapper"):
                gr.Markdown(
                    """
                    <div class="app-header">
                        <span class="app-header-logo">⚖️</span>
                        <h1 class="app-header-title">Landlord-Tenant Rights Assistant</h1>
                        <p class="app-header-tagline">Empowering You with State-Specific Legal Insights</p>
                    </div>
                    """
                )

            # --- Main Dashboard Console Container ---
            with gr.Column(elem_classes="main-dashboard-container"):

                # --- Section 1: Introduction and Disclaimer Card ---
                with gr.Group(elem_classes="dashboard-card-section"):
                    gr.Markdown("<h3 class='sub-section-title'>Welcome & Disclaimer</h3>")
                    gr.Markdown(
                        """
                        <p>Navigate landlord-tenant laws with ease. This assistant provides detailed, state-specific answers grounded in legal authority.</p>
                        <p><strong>Disclaimer:</strong> This tool is for informational purposes only and does not constitute legal advice. For specific legal guidance, always consult a licensed attorney in your jurisdiction.</p>
                        """
                    )

                # --- Section 2: OpenAI API Key Input Card ---
                with gr.Group(elem_classes="dashboard-card-section"):
                    gr.Markdown("<h3 class='sub-section-title'>OpenAI API Key</h3>")
                    api_key_input = gr.Textbox(
                        label="OpenAI API Key", # Keep label for theme compatibility
                        type="password", placeholder="Enter your API key (e.g., sk-...)",
                        info="Required to process your query. Securely used per request, not stored. <a href='https://platform.openai.com/api-keys' target='_blank'>Get one free from OpenAI</a>.", lines=1,
                        elem_classes=["input-field-group"]
                    )

                # --- Section 3: Query Input and State Selection Card ---
                with gr.Group(elem_classes="dashboard-card-section"):
                    gr.Markdown("<h3 class='sub-section-title'>Ask Your Question</h3>")
                    with gr.Row(elem_classes="input-row"):
                        with gr.Column(elem_classes="input-field", scale=3):
                            query_input = gr.Textbox(
                                label="Question", placeholder="E.g., What are the rules for security deposit returns in my state?",
                                lines=5, max_lines=10,
                                elem_classes=["input-field-group"]
                            )
                        with gr.Column(elem_classes="input-field", scale=1):
                            state_input = gr.Dropdown(
                                label="Select State", choices=dropdown_choices, value=initial_value,
                                allow_custom_value=False,
                                elem_classes=["input-field-group"]
                            )
                    with gr.Row(elem_classes="button-row"):
                        clear_button = gr.Button("Clear", variant="secondary", elem_classes=["gr-button-secondary"])
                        submit_button = gr.Button("Submit Query", variant="primary", elem_classes=["gr-button-primary"])

                # --- Section 4: Output Display Card ---
                with gr.Group(elem_classes="dashboard-card-section"):
                    gr.Markdown("<h3 class='sub-section-title'>Legal Assistant's Response</h3>")
                    output = gr.Markdown(
                        value="<div class='placeholder output-card'>The answer will appear here after submitting your query.</div>",
                        elem_classes="output-content-wrapper output-card"
                    )

                # --- Section 5: Example Questions Section ---
                with gr.Group(elem_classes="dashboard-card-section examples-section"):
                    gr.Markdown("<h3 class='sub-section-title'>Example Questions to Ask</h3>")
                    if example_queries:
                        gr.Examples(
                            examples=example_queries, inputs=[query_input, state_input],
                            examples_per_page=5,
                            label="" # Hide default Examples label
                        )
                    else:
                        gr.Markdown("<div class='placeholder'>Sample questions could not be loaded.</div>")

            # --- Footer Section ---
            with gr.Group(elem_classes="app-footer-wrapper"):
                gr.Markdown(
                    """
                    <div class="app-footer">
                        <p>This tool is for informational purposes only and does not constitute legal advice. For legal guidance, always consult with a licensed attorney in your jurisdiction.</p>
                        <p>Developed by <strong>Nischal Subedi</strong>.
                        Connect on <a href="https://www.linkedin.com/in/nischal1/" target='_blank'>LinkedIn</a>
                        or explore insights at <a href="https://datascientistinsights.substack.com/" target='_blank'>Substack</a>.</p>
                    </div>
                    """
                )

            # --- Event Listeners (Logic remains identical) ---
            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,
                    "<div class='placeholder output-card'>Inputs cleared. Ready for your next question.</div>"
                ),
                inputs=[], outputs=[api_key_input, query_input, state_input, output]
            )
            logging.info("Gradio interface created with earneleh/paris theme and refined custom CSS.")
            return demo

# --- Main Execution Block (remains untouched from original logic, just fixed the exit) ---
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)

        logging.info(f"Attempting to load PDF from: {PDF_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 ---\nPDF file ('{os.path.basename(PDF_PATH)}') not found at: {PDF_PATH}.\nPlease ensure it exists or set 'PDF_PATH' environment variable.\n---------------------------\n")
            exit(1) # This exit is correct: if file not found

        # This `if` block checks for readability *only if the file exists*.
        # The erroneous `exit(1)` that was likely at line 701 has been removed.
        if not os.access(PDF_PATH, os.R_OK):
            logging.error(f"FATAL: PDF file at '{PDF_PATH}' exists but is not readable. Check file permissions.")
            print(f"\n--- PERMISSION ERROR ---\nPDF file ('{os.path.basename(PDF_PATH)}') found but not readable at: {PDF_PATH}\nPlease check file permissions (e.g., using 'chmod +r' in terminal).\n---------------------------\n")
            exit(1) # This exit is correct: if file exists but is not readable

        logging.info(f"PDF file '{os.path.basename(PDF_PATH)}' found and is readable.")

        vector_db_instance = VectorDatabase(persist_directory=VECTOR_DB_PATH)
        rag = RAGSystem(vector_db=vector_db_instance)

        rag.load_pdf(PDF_PATH)

        app_interface = rag.gradio_interface()
        SERVER_PORT = int(os.getenv("PORT", 7860)) # Use PORT env var if on Spaces/Cloud, else 7860

        logging.info(f"Launching Gradio app on http://0.0.0.0:{SERVER_PORT}")
        print(f"\n--- Gradio App Running ---\nAccess at: http://localhost:{SERVER_PORT} or your public Spaces URL\n--------------------------\n")
        app_interface.launch(server_name="0.0.0.0", server_port=SERVER_PORT, share=False) # share=False is typical for Spaces

    except ModuleNotFoundError as e:
        if "vector_db" in str(e):
             logging.error(f"FATAL: Could not import VectorDatabase. Ensure 'vector_db.py' is in the same directory and 'chromadb', 'langchain', 'pypdf', 'sentence-transformers' are installed.", exc_info=True)
             print(f"\n--- MISSING DEPENDENCY OR FILE ---\nCould not find/import 'vector_db.py' or one of its dependencies.\nError: {e}\nPlease ensure 'vector_db.py' is present and all required packages (chromadb, langchain, pypdf, sentence-transformers, etc.) are in your requirements.txt and installed.\n---------------------------\n")
        else:
            logging.error(f"Application startup failed due to a missing module: {str(e)}", exc_info=True)
            print(f"\n--- FATAL STARTUP ERROR - MISSING MODULE ---\n{str(e)}\nPlease ensure all dependencies are installed.\nCheck logs for more details.\n---------------------------\n")
        exit(1)
    except FileNotFoundError as e:
        logging.error(f"Application startup failed due to a missing file: {str(e)}", exc_info=True)
        print(f"\n--- FATAL STARTUP ERROR - FILE NOT FOUND ---\n{str(e)}\nPlease ensure the file exists at the specified path.\nCheck logs for more details.\n---------------------------\n")
        exit(1)
    except Exception as e:
        logging.error(f"Application startup failed: {str(e)}", exc_info=True)
        print(f"\n--- FATAL STARTUP ERROR ---\n{str(e)}\nCheck logs for more details.\n---------------------------\n")
        exit(1)