# app.py - Combined Script
# Combined Imports
import os
import gradio as gr
from huggingface_hub import InferenceClient
import torch
import re
import warnings
import time
import json
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, BitsAndBytesConfig
from sentence_transformers import SentenceTransformer, util, CrossEncoder
import gspread
# from google.colab import auth
from google.auth import default
from tqdm import tqdm
from ddgs import DDGS # Updated import
import spacy
from datetime import date, timedelta, datetime # Import datetime
from dateutil.relativedelta import relativedelta # Corrected typo
import traceback # Import traceback
import base64 # Import base64
import dateparser # Import dateparser
from dateparser.search import search_dates
import pytz # Import pytz for timezone handling
# from google.colab import userdata # Import userdata

# Suppress warnings
warnings.filterwarnings("ignore", category=UserWarning)
# Define global variables and load secrets
HF_TOKEN = os.getenv("HF_TOKEN")
# Add a print statement to check if HF_TOKEN is loaded
print(f"HF_TOKEN loaded: {'*' * len(HF_TOKEN) if HF_TOKEN else 'None'}")
SHEET_ID = "19ipxC2vHYhpXCefpxpIkpeYdI43a1Ku2kYwecgUULIw"
# Use userdata.get() for Google Credentials
GOOGLE_BASE64_CREDENTIALS = os.getenv("GOOGLE_BASE64_CREDENTIALS")
# Initialize InferenceClient
# client = InferenceClient("google/gemma-2-9b-it", token=HF_TOKEN)
# client = InferenceClient("meta-llama/Llama-4-Scout-17B-16E-Instruct", token=HF_TOKEN)

client = InferenceClient("meta-llama/Llama-3.3-70B-Instruct", token=HF_TOKEN)
# Load spacy model for sentence splitting
nlp = None
try:
    nlp = spacy.load("en_core_web_sm")
    print("SpaCy model 'en_core_web_sm' loaded.")
except OSError:
    print("SpaCy model 'en_core_web_sm' not found. Downloading...")
    try:
        os.system("python -m spacy download en_core_web_sm")
        nlp = spacy.load("en_core_web_sm")
        print("SpaCy model 'en_core_web_sm' downloaded and loaded.")
    except Exception as e:
        print(f"Failed to download or load SpaCy model: {e}")
# Load SentenceTransformer for RAG/business info retrieval and semantic detection
embedder = None
try:
    print("Attempting to load Sentence Transformer (sentence-transformers/paraphrase-MiniLM-L6-v2)...")
    # Use the model provided by the user for semantic detection as well
    embedder = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2") # Or 'all-MiniLM-L6-v2' if preferred
    print("Sentence Transformer loaded.")
except Exception as e:
     print(f"Error loading Sentence Transformer: {e}")
# Load a Cross-Encoder model for re-ranking retrieved documents
reranker = None
try:
    print("Attempting to load Cross-Encoder Reranker (cross-encoder/ms-marco-MiniLM-L6-v2)...")
    reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2')
    print("Cross-Encoder Reranker loaded.")
except Exception as e:
    print(f"Error loading Cross-Encoder Reranker: {e}")
    print("Please ensure the model identifier 'cross-encoder/ms-marco-MiniLM-L6-v2' is correct and accessible on Hugging Face Hub.")
    print(traceback.format_exc())
    reranker = None
# Google Sheets Authentication
gc = None # Global variable for gspread client
def authenticate_google_sheets():
    """Authenticates with Google Sheets using base64 encoded credentials."""
    global gc
    print("Authenticating Google Account...")
    if not GOOGLE_BASE64_CREDENTIALS:
        print("Error: GOOGLE_BASE64_CREDENTIALS secret not found.")
        return False
    try:
        # Decode the base64 credentials
        credentials_json = base64.b64decode(GOOGLE_BASE64_CREDENTIALS).decode('utf-8')
        credentials = json.loads(credentials_json)
        # Authenticate using service account from dictionary
        gc = gspread.service_account_from_dict(credentials)
        print("Google Sheets authentication successful via service account.")
        return True
    except Exception as e:
        print(f"Google Sheets authentication failed: {e}")
        print("Please ensure your GOOGLE_BASE64_CREDENTIALS secret is correctly set and contains valid service account credentials.")
        print(traceback.format_exc())
        return False
# Google Sheets Data Loading and Embedding
data = [] # Global variable to store loaded data
descriptions_for_embedding = []
embeddings = torch.tensor([])
business_info_available = False # Flag to indicate if business info was loaded successfully
def load_business_info():
    """Loads business information from Google Sheet and creates embeddings."""
    global data, descriptions_for_embedding, embeddings, business_info_available
    business_info_available = False # Reset flag
    if gc is None:
        print("Skipping Google Sheet loading: Google Sheets client not authenticated.")
        return
    if not SHEET_ID:
         print("Error: SHEET_ID not set.")
         return
    try:
        sheet = gc.open_by_key(SHEET_ID).sheet1
        print(f"Successfully opened Google Sheet with ID: {SHEET_ID}")
        data_records = sheet.get_all_records()
        if not data_records:
            print(f"Warning: No data records found in Google Sheet with ID: {SHEET_ID}")
            data = []
            descriptions_for_embedding = []
        else:
            # Filter out rows missing 'Service' or 'Description'
            filtered_data = [row for row in data_records if row.get('Service') and row.get('Description')]
            if not filtered_data:
                print("Warning: Filtered data is empty after checking for 'Service' and 'Description'.")
                data = []
                descriptions_for_embedding = []
            else:
                data = filtered_data
                # Use BOTH Service and Description for embedding
                descriptions_for_embedding = [f"Service: {row['Service']}. Description: {row['Description']}" for row in data]
                # Only encode if descriptions_for_embedding are found and embedder is available
                if descriptions_for_embedding and embedder is not None:
                    print("Encoding descriptions...")
                    try:
                        embeddings = embedder.encode(descriptions_for_embedding, convert_to_tensor=True)
                        print("Encoding complete.")
                        business_info_available = True
                    except Exception as e:
                        print(f"Error during description encoding: {e}")
                        embeddings = torch.tensor([])
                        business_info_available = False
                else:
                    print("Skipping encoding descriptions: No descriptions found or embedder not available.")
                    embeddings = torch.tensor([])
                    business_info_available = False
        print(f"Loaded {len(descriptions_for_embedding)} entries from Google Sheet for embedding/RAG.")
        if not business_info_available:
            print("Business information retrieval (RAG) is NOT available.")
    except gspread.exceptions.SpreadsheetNotFound:
        print(f"Error: Google Sheet with ID '{SHEET_ID}' not found.")
        print("Please check the SHEET_ID and ensure your authenticated Google Account has access to this sheet.")
        business_info_available = False
    except Exception as e:
        print(f"An error occurred while accessing the Google Sheet: {e}")
        print(traceback.format_exc())
        business_info_available = False
# Business Info Retrieval (RAG)
def retrieve_business_info(query: str, top_n: int = 3) -> list:
    """
    Retrieves relevant business information from loaded data based on a query.
    Args:
        query: The user's query string.
        top_n: The number of top relevant entries to retrieve.
    Returns:
        A list of dictionaries, where each dictionary is a relevant row from the
        Google Sheet data. Returns an empty list if RAG is not available or
        no relevant information is found.
    """
    global data
    if not business_info_available or embedder is None or not descriptions_for_embedding or not data:
        print("Business information retrieval is not available or data is empty.")
        return []
    try:
        query_embedding = embedder.encode(query, convert_to_tensor=True)
        cosine_scores = util.cos_sim(query_embedding, embeddings)[0]
        top_results_indices = torch.topk(cosine_scores, k=min(top_n, len(data)))[1].tolist()
        top_results = [data[i] for i in top_results_indices]
        if reranker is not None and top_results:
            print("Re-ranking top results...")
            rerank_pairs = [(query, descriptions_for_embedding[i]) for i in top_results_indices]
            rerank_scores = reranker.predict(rerank_pairs)
            reranked_indices = sorted(range(len(rerank_scores)), key=lambda i: rerank_scores[i], reverse=True)
            reranked_results = [top_results[i] for i in reranked_indices]
            print("Re-ranking complete.")
            return reranked_results
        else:
            return top_results
    except Exception as e:
        print(f"Error during business information retrieval: {e}")
        print(traceback.format_exc())
        return []
# Function to perform DuckDuckGo Search and return results with URLs
def perform_duckduckgo_search(query: str, max_results: int = 5): # Reduced max_results for multi-part queries
    """
    Performs a search using DuckDuckGo and returns a list of dictionaries.
    Includes a delay to avoid rate limits.
    Returns an empty list and prints an error if search fails.
    """
    print(f"Executing Tool: perform_duckduckgo_search with query='{query}')")
    search_results_list = []
    try:
        time.sleep(1)
        with DDGS() as ddgs:
            search_query = query.strip()
            if not search_query or len(search_query.split()) < 2:
                 print(f"Skipping search for short query: '{search_query}'")
                 return []
            print(f"Sending search query to DuckDuckGo: '{search_query}'")
            results_generator = ddgs.text(search_query, max_results=max_results)
            results_found = False
            for r in results_generator:
                search_results_list.append(r)
                results_found = True
            print(f"Raw results from DuckDuckGo: {search_results_list}")
            if not results_found and max_results > 0:
                 print(f"DuckDuckGo search for '{search_query}' returned no results.")
            elif results_found:
                 print(f"DuckDuckGo search for '{search_query}' completed. Found {len(search_results_list)} results.")
    except Exception as e:
        print(f"Error during Duckduckgo search for '{search_query if 'search_query' in locals() else query}': {e}")
        print(traceback.format_exc())
        return []
    return search_results_list
# Define the new semantic date/time detection and calculation function using dateparser
def perform_date_calculation(query: str) -> str or None:
    """
    Analyzes query for date/time information using dateparser.
    If dateparser finds a date, it returns a human-friendly response string.
    Otherwise, it returns None.
    It is designed to handle multiple languages and provide the time for East Africa (Tanzania).
    """
    print(f"Executing Tool: perform_date_calculation with query='{query}') using dateparser.search_dates")
    try:
        eafrica_tz = pytz.timezone('Africa/Dar_es_Salaam')
        now = datetime.now(eafrica_tz)
    except pytz.UnknownTimeZoneError:
        print("Error: Unknown timezone 'Africa/Dar_es_Salaam'. Using default system time.")
        now = datetime.now()
    try:
        # Try parsing with Swahili first, then English
        found = search_dates(
            query,
            settings={
                "PREFER_DATES_FROM": "future",
                "RELATIVE_BASE": now
            },
            languages=['sw', 'en'] # Prioritize Swahili
        )
        if not found:
            print("dateparser.search_dates could not parse any date/time.")
            return None
        text_snippet, parsed = found[0]
        print(f"dateparser.search_dates found: text='{text_snippet}', parsed='{parsed}'")
        is_swahili = any(swahili_phrase in query.lower() for swahili_phrase in ['tarehe', 'siku', 'saa', 'muda', 'leo', 'kesho', 'jana', 'ngapi', 'gani', 'mwezi', 'mwaka', 'habari', 'mambo', 'shikamoo', 'karibu', 'asante'])

        # Check for specific Swahili greetings and respond appropriately
        if is_swahili:
            query_lower = query.lower().strip()
            if query_lower in ['habari', 'mambo', 'habari gani']:
                 return "Nzuri! Habari zako?" # Common Swahili response to greetings
            elif query_lower in ['shikamoo']:
                 return "Marahaba!" # Response to Shikamoo
            elif query_lower in ['asante']:
                 return "Karibu!" # Response to Asante
            elif query_lower in ['karibu']:
                 return "Asante!" # Response to Karibu

        # Handle timezone information
        if now.tzinfo is not None and parsed.tzinfo is None:
            parsed = now.tzinfo.localize(parsed)
        elif now.tzinfo is None and parsed.tzinfo is not None:
             parsed = parsed.replace(tzinfo=None)
        # Check if the parsed date is today and time is close to now or midnight
        if parsed.date() == now.date():
             # Consider it "now" if within a small time window or if no specific time was parsed (midnight)
             if abs((parsed - now).total_seconds()) < 60 or parsed.time() == datetime.min.time():
                 print("Query parsed to today's date and time is close to 'now' or midnight, returning current time/date.")
                 if is_swahili:
                     return f"Kwa saa za Afrika Mashariki (Tanzania), tarehe ya leo ni {now.strftime('%A, %d %B %Y')} na saa ni {now.strftime('%H:%M:%S')}."
                 else:
                     return f"In East Africa (Tanzania), the current date is {now.strftime('%A, %d %B %Y')} and the time is {now.strftime('%H:%M:%S')}."
             else:
                  print(f"Query parsed to a specific time today: {parsed.strftime('%H:%M:%S')}")
                  if is_swahili:
                       return f"Hiyo inafanyika leo, {parsed.strftime('%A, %d %B %Y')}, saa {parsed.strftime('%H:%M:%S')} saa za Afrika Mashariki."
                  else:
                       return f"That falls on today, {parsed.strftime('%A, %d %B %Y')}, at {parsed.strftime('%H:%M:%S')} East Africa Time."
        else:
            print(f"Query parsed to a specific date: {parsed.strftime('%A, %d %B %Y')} at {parsed.strftime('%H:%M:%S')}")
            time_str = parsed.strftime('%H:%M:%S')
            date_str = parsed.strftime('%A, %d %B %Y')
            if parsed.tzinfo:
                 tz_name = parsed.tzinfo.tzname(parsed) or 'UTC'
                 if is_swahili:
                     return f"Hiyo inafanyika tarehe {date_str} saa {time_str} {tz_name}."
                 else:
                      return f"That falls on {date_str} at {time_str} {tz_name}."
            else:
                 if is_swahili:
                      return f"Hiyo inafanyika tarehe {date_str} saa {time_str}."
                 else:
                      return f"That falls on {date_str} at {time_str}."
    except Exception as e:
        print(f"Error during dateparser.search_dates execution: {e}")
        print(traceback.format_exc())
        return f"An error occurred while parsing date/time: {e}"

# Function to determine if a query requires a tool or can be answered directly
def determine_tool_usage(query: str) -> str:
    """
    Analyzes the query to determine if a specific tool is needed.
    Returns the name of the tool ('duckduckgo_search', 'business_info_retrieval',
    'date_calculation') or 'none' if no specific tool is clearly indicated.
    Prioritizes business information retrieval, then specific tools based on keywords
    and LLM judgment.
    """
    query_lower = query.lower()

    # Check for specific Swahili greetings or conversational phrases that should be handled by date_calculation
    swahili_conversational_phrases = ['habari', 'mambo', 'shikamoo', 'karibu', 'asante', 'habari gani']
    if any(phrase in query_lower for phrase in swahili_conversational_phrases):
        print(f"Detected a Swahili conversational phrase: '{query}'. Using 'date_calculation' tool for initial handling.")
        return "date_calculation"

    # 1. Prioritize Business Info Retrieval if RAG is available
    if business_info_available:
         messages_business_check = [{"role": "user", "content": f"Does the following query ask about a specific person, service, offering, or description that is likely to be found *only* within a specific business's internal knowledge base, and not general knowledge? For example, questions about 'Salum' or 'Jackson Kisanga' are likely business-related, while questions about 'the current president of the USA' or 'who won the Ballon d'Or' are general knowledge. Answer only 'yes' or 'no'. Query: {query}"}]
         try:
             business_check_response = client.chat_completion(
                 messages=messages_business_check,
                 max_tokens=10,
                 temperature=0.1
             ).choices[0].message.content.strip().lower()
             # Ensure the response explicitly contains "yes" and is not just a substring match
             if business_check_response == "yes":
                 print(f"Detected as specific business info query based on LLM check: '{query}'")
                 return "business_info_retrieval"
             else:
                 print(f"LLM check indicates not a specific business info query: '{query}'")
         except Exception as e:
             print(f"Error during LLM call for business info check for query '{query}': {e}")
             print(traceback.format_exc())
             print(f"Proceeding without business info check for query '{query}' due to error.")

    # 2. Check for Date Calculation (only if not a simple greeting handled above)
    date_time_check_result = perform_date_calculation(query) # Re-run date_calculation to check for actual dates
    if date_time_check_result is not None and not any(phrase in query_lower for phrase in swahili_conversational_phrases):
        print(f"Detected as date/time calculation query based on dateparser result for: '{query}'")
        return "date_calculation"

    # 3. Use LLM to determine if DuckDuckGo search is needed
    messages_tool_determination_search = [{"role": "user", "content": f"Does the following query require searching the web for current or general knowledge information (e.g., news, facts, definitions, current events)? Respond ONLY with 'duckduckgo_search' or 'none'. Query: {query}"}]
    try:
        search_determination_response = client.chat_completion(
            messages=messages_tool_determination_search,
            max_tokens=20,
            temperature=0.1,
            top_p=0.9
        ).choices[0].message.content or ""
        response_lower = search_determination_response.strip().lower()
        if "duckduckgo_search" in response_lower:
            print(f"Model-determined tool for '{query}': 'duckduckgo_search'")
            return "duckduckgo_search"
        else:
            print(f"Model-determined tool for '{query}': 'none' (for search)")
    except Exception as e:
        print(f"Error during LLM call for search tool determination for query '{query}': {e}")
        print(traceback.format_exc())
        print(f"Proceeding without search tool check for query '{query}' due to error.")

    # 4. If none of the specific tools are determined, default to 'none'
    print(f"No specific tool determined for '{query}'. Defaulting to 'none'.")
    return "none"


# Function to generate text using the LLM, incorporating tool results if available
def generate_text(prompt: str, tool_results: dict = None, chat_history: list[dict] = None) -> str:
    """
    Generates text using the configured LLM, optionally incorporating tool results and chat history.
    Args:
        prompt: The initial prompt for the LLM (the user's latest query).
        tool_results: A dictionary containing results from executed tools.
                      Keys are tool names, values are their outputs.
        chat_history: The history of the conversation as a list of dictionaries
                      (as provided by Gradio ChatInterface with type="messages").
    Returns:
        The generated text from the LLM.
    """
    # Add persona instructions to the beginning of the prompt
    persona_instructions = """You are absa_ai, an AI developed on August 7, 2025, by the absa team. Your knowledge about business data comes from the company's internal Google Sheet.
You are a friendly and helpful chatbot. Respond to greetings appropriately (e.g., "Hello!", "Hi there!", "Habari!"). If the user uses Swahili greetings or simple conversational phrases, respond in Swahili. Otherwise, respond in English unless the query is clearly in Swahili. Handle conversational flow and ask follow-up questions when appropriate.
If the user asks a question about other companies or general knowledge, answer their question. However, subtly remind them that your primary expertise and purpose are related to Absa-specific information.
"""
    # Build the messages list for the chat completion API
    messages = [{"role": "user", "content": persona_instructions}] # Start with the persona instructions

    if chat_history:
        print("Including chat history in LLM prompt.")
        # Iterate through the chat_history provided by Gradio (list of dictionaries)
        # Add only 'user' and 'assistant' roles to the LLM context
        for message_dict in chat_history:
            role = message_dict.get("role")
            content = message_dict.get("content")
            if role in ["user", "assistant"] and content is not None:
                messages.append({"role": role, "content": content})


    # Add the current user prompt and tool results
    current_user_content = prompt
    if tool_results and any(tool_results.values()):
        current_user_content += "\n\nTool Results:\n"
        for question, results in tool_results.items():
            if results:
                current_user_content += f"--- Results for: {question} ---\n"
                if isinstance(results, list):
                    for i, result in enumerate(results):
                        if isinstance(result, dict) and 'Service' in result and 'Description' in result:
                            current_user_content += f"Business Info {i+1}:\nService: {result.get('Service', 'N/A')}\nDescription: {result.get('Description', 'N/A')}\n\n"
                        elif isinstance(result, dict) and 'url' in result:
                            current_user_content += f"Search Result {i+1}:\nTitle: {result.get('title', 'N/A')}\nURL: {result.get('url', 'N/A')}\nSnippet: {result.get('body', 'N/A')}\n\n"
                        else:
                            current_user_content += f"{result}\n\n"
                elif isinstance(results, dict):
                    for key, value in results.items():
                        current_user_content += f"{key}: {value}\n"
                    current_user_content += "\n"
                else:
                    current_user_content += f"{results}\n\n"

        current_user_content += "Based on the provided tool results and the conversation history, answer the user's latest query. If a question was answered by a tool, use the tool's result directly in your response. Maintain the language of the original query if possible, especially for simple greetings or direct questions answered by tools."
        print("Added tool results and instruction to final prompt.")
    else:
         current_user_content += "Based on the conversation history, answer the user's latest query."
         print("No tool results to add to final prompt, relying on conversation history.")

    messages.append({"role": "user", "content": current_user_content})


    print(f"Sending messages to LLM:\n---\n{messages}\n---")
    generation_config = {
        "temperature": 0.7,
        "max_new_tokens": 500,
        "top_p": 0.95,
        "top_k": 50,
        "do_sample": True,
    }
    try:
        response = client.chat_completion(
            messages=messages, # Pass the list of messages
            max_tokens=generation_config.get("max_new_tokens", 512),
            temperature=generation_config.get("temperature", 0.7),
            top_p=generation_config.get("top_p", 0.95)
        ).choices[0].message.content or ""
        print("LLM generation successful using chat_completion.")
        return response
    except Exception as e:
        print(f"Error during final LLM generation: {e}")
        print(traceback.format_exc())
        return "An error occurred while generating the final response."
# Main chat function with query breakdown and tool execution per question
def chat(query: str, chat_history: list[list[str]]):
    """
    Processes user queries by breaking down multi-part queries, determining and
    executing appropriate tools for each question, and synthesizing results
    using the LLM. Prioritizes business information retrieval.
    """
    print(f"Received query: {query}")

    # Step 1: Query Breakdown
    print("\n--- Breaking down query ---")
    prompt_for_question_breakdown = f"""
Analyze the following query and list each distinct question found within it.
Present each question on a new line, starting with a hyphen.
Query: {query}
"""
    try:
        messages_question_breakdown = [{"role": "user", "content": prompt_for_question_breakdown}]
        question_breakdown_response = client.chat_completion(
            messages=messages_question_breakdown,
            max_tokens=100,
            temperature=0.1,
            top_p=0.9
        ).choices[0].message.content or ""
        individual_questions = [line.strip() for line in question_breakdown_response.split('\n') if line.strip()]
        # Remove any notes the LLM might add during breakdown
        cleaned_questions = [re.sub(r'^[-*]?\s*', '', q) for q in individual_questions if not q.strip().lower().startswith('note:')]
        print("Individual questions identified:")
        for q in cleaned_questions:
            print(f"- {q}")
    except Exception as e:
        print(f"Error during LLM call for question breakdown: {e}")
        print(traceback.format_exc())
        cleaned_questions = [query] # Fallback to treating the whole query as one question

    # Step 2: Tool Determination per Question
    print("\n--- Determining tools per question ---")
    determined_tools = {}
    for question in cleaned_questions:
        print(f"\nAnalyzing question for tool determination: '{question}'")
        determined_tools[question] = determine_tool_usage(question)
        print(f"Determined tool for '{question}': '{determined_tools[question]}'")
    print("\nSummary of determined tools per question:")
    for question, tool in determined_tools.items():
        print(f"'{question}': '{tool}'")

    # Step 3: Execute Tools and Step 4: Synthesize Results
    print("\n--- Executing tools and collecting results ---")
    tool_results = {}
    for question, tool in determined_tools.items():
        print(f"\nExecuting tool '{tool}' for question: '{question}'")
        result = None
        if tool == "date_calculation":
            result = perform_date_calculation(question)
        elif tool == "duckduckgo_search":
            result = perform_duckduckgo_search(question)
        elif tool == "business_info_retrieval":
            result = retrieve_business_info(question)
        elif tool == "none":
             # If tool is 'none', the LLM will answer this part using its internal knowledge
             # in the final response generation step. We don't need a specific tool result here.
             print(f"Skipping tool execution for question: '{question}' as tool is 'none'. LLM will handle.")
             result = None # Set result to None so it's not included in tool_results for 'none' tool
        # Only store results if they are not None (i.e., tool was executed and returned something)
        if result is not None:
             tool_results[question] = result

    print("\n--- Collected Tool Results ---")
    if tool_results:
        for question, result in tool_results.items():
            print(f"\nQuestion: {question}")
            print(f"Result: {result}")
    else:
        print("No tool results were collected.")
    print("\n--------------------------")

    # Step 5: Final Response Generation
    print("\n--- Generating final response ---")
    # Pass the chat_history (which is a list of dictionaries when using type="messages")
    final_response = generate_text(query, tool_results, chat_history)
    print("\n--- Final Response from LLM ---")
    print(final_response)
    print("\n----------------------------")

    # Return only the latest AI response as a string for Gradio's ChatInterface
    return final_response


# Keep the Gradio interface setup as is for now
if __name__ == "__main__":
    # Authenticate Google Sheets when the script starts
    authenticate_google_sheets()
    # Load business info after authentication
    load_business_info()
    # Check if spacy model, embedder, and reranker loaded correctly
    if nlp is None:
        print("Warning: SpaCy model not loaded. Sentence splitting may not work correctly.")
    if embedder is None:
        print("Warning: Sentence Transformer (embedder) not loaded. RAG will not be available.")
    if reranker is None:
        print("Warning: Cross-Encoder Reranker not loaded. Re-ranking of RAG results will not be performed.")
    if not business_info_available:
        print("Warning: Business information (Google Sheet data) not loaded successfully. "
              "RAG will not be available. Please ensure the GOOGLE_BASE64_CREDENTIALS secret is set correctly.")
    print("Launching Gradio Interface...")
    import gradio as gr

    DESCRIPTION = """
    # LLM with Tools (DuckDuckGo Search, Date Calculation, Business Info RAG)
    Ask me anything! I can perform web searches, calculate dates, and retrieve business information.
    """

    demo = gr.ChatInterface(
        fn=chat,
        stop_btn=None,
        examples=[
            ["Hello there! How are you doing?"],
            ["What is the current time in East Africa?"],
            ["Tell me about the 'Project Management' service from Absa."],
            ["Search the web for the latest news on AI."],
            ["Habari!"],
            ["What is the date next Tuesday?"],
        ],
        cache_examples=False,
        type="messages",
        description=DESCRIPTION,
        fill_height=True,
    )

    try:
        demo.launch(debug=True, show_error=True)
    except Exception as e:
        print(f"Error launching Gradio interface: {e}")
        print(traceback.format_exc())
        print("Please check the console output for more details on the error.")