Delete app.py

app.py

@@ -1,1133 +0,0 @@
-
-import os
-import gradio as gr
-from huggingface_hub import InferenceClient
-import torch
-import re
-import warnings
-import time
-import json
-# Removed specific transformers imports that might not be strictly necessary for InferenceClient
-# from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, BitsAndBytesConfig
-from sentence_transformers import SentenceTransformer, util, CrossEncoder
-import gspread
-# Removed google.auth.default as service account from dict is used
-# from google.auth import default
-from tqdm import tqdm
-from ddgs import DDGS
-import spacy
-from datetime import date, timedelta, datetime
-from dateutil.relativedelta import relativedelta # Corrected import
-import traceback
-import base64
-import dateparser
-from dateparser.search import search_dates
-import pytz
-# Removed userdata as secrets are accessed via environment variables in Spaces
-#from google.colab import userdata
-import os # Ensure os is imported for environment variables
-from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
-from huggingface_hub import HfApi, login # Import login for initial auth
-
-import faiss
-import numpy as np
-import pickle
-
-
-# --- SQL Logging Imports and Connection Placeholder (Removed for HF Space) ---
-# Removed SQL related code as per user's request to use HF Datasets
-# ---
-
-# Define the dataset name (replace with your actual Hugging Face username and desired dataset name)
-# Ensure this dataset is set to private on the Hugging Face Hub
-dataset_name = "Futuresony/Logs_Conversation" # REPLACE WITH YOUR ACTUAL DATASET NAME
-
-# Global variable to store the dataset
-conversation_dataset = None
-
-# Initialize HfApi for pushing - Use token from environment variable
-# No need to re-initialize HfApi with token here as login handles it
-# hf_api = HfApi(token=HF_TOKEN)
-
-
-# Suppress warnings
-warnings.filterwarnings("ignore", category=UserWarning)
-
-# Define global variables and load secrets from environment variables for HF Spaces
-# HF_TOKEN is now accessed via os.environ or handled by huggingface_hub login
-HF_TOKEN = os.getenv("HF_TOKEN") # Access HF_TOKEN from environment variable
-# 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"
-# GOOGLE_BASE64_CREDENTIALS is now accessed via os.environ
-GOOGLE_BASE64_CREDENTIALS = os.getenv("GOOGLE_BASE64_CREDENTIALS")
-
-
-# SECRET_API_KEY is now accessed via os.environ
-SECRET_API_KEY = os.getenv("APP_API_KEY")
-# Add a print statement to check if SECRET_API_KEY is loaded
-print(f"SECRET_API_KEY loaded: {'*' * len(SECRET_API_KEY) if SECRET_API_KEY else 'None'}")
-
-if not SECRET_API_KEY:
-    print("Warning: APP_API_KEY secret not set. API key validation will fail.")
-elif not SECRET_API_KEY.startswith("fs_"):
-    print("Warning: APP_API_KEY secret does not start with 'fs_'. Please check your secret.")
-
-# Authenticate with Hugging Face Hub using the token from environment variable
-try:
-    print("Attempting to authenticate with Hugging Face Hub...")
-    # login() automatically looks for HF_TOKEN in environment variables
-    login(add_to_git_credential=True)
-    print("Hugging Face Hub authentication successful.")
-except Exception as e:
-    print(f"Hugging Face Hub authentication failed: {e}")
-    print(traceback.format_exc())
-
-
-# Initialize InferenceClient for primary model (LLaMA-3.3-70B-Instruct)
-primary_client = InferenceClient("meta-llama/Llama-3.3-70B-Instruct", token=HF_TOKEN)
-print("Primary model (LLaMA-3.3-70B-Instruct) client initialized.")
-
-# Initialize InferenceClient for fallback model (Gemma-2-9b-it)
-fallback_client = InferenceClient("google/gemma-2-9b-it", token=HF_TOKEN)
-print("Fallback model (Gemma-2-9b-it) client initialized.")
-
-
-# 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:
-        import subprocess
-        subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
-        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)...")
-    embedder = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")
-    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
-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:
-        credentials_json = base64.b64decode(GOOGLE_BASE64_CREDENTIALS).decode('utf-8')
-        credentials = json.loads(credentials_json)
-        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(traceback.format_exc())
-        print("Please ensure your GOOGLE_BASE64_CREDENTIALS secret is correctly set and contains valid service account credentials.")
-        return False
-
-# Google Sheets Data Loading and Embedding for RAG
-data = []
-descriptions_for_embedding = []
-embeddings = torch.tensor([]) # This will store embeddings for RAG data
-business_info_available = False
-
-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
-    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:
-            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
-                descriptions_for_embedding = [f"Service: {row['Service']}. Description: {row['Description']}" for row in data]
-                if descriptions_for_embedding and embedder is not None:
-                    print("Encoding descriptions for RAG...")
-                    try:
-                        embeddings = embedder.encode(descriptions_for_embedding, convert_to_tensor=True)
-                        print("Encoding complete. RAG embeddings created.")
-                        business_info_available = True
-                    except Exception as e:
-                        print(f"Error during description encoding for RAG: {e}")
-                        embeddings = torch.tensor([])
-                        business_info_available = False
-                else:
-                    print("Skipping encoding descriptions for RAG: 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.")
-        else:
-            print("Business information retrieval (RAG) is 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) function - Reusing the existing one
-def retrieve_business_info(query: str, top_n: int = 3) -> list:
-    """
-    Retrieves relevant business information from loaded data based on a query.
-    """
-    global data, embeddings
-    if not business_info_available or embedder is None or not descriptions_for_embedding or not data or embeddings.numel() == 0:
-        print("Business information retrieval is not available or RAG data is empty.")
-        return []
-    try:
-        query_embedding = embedder.encode(query, convert_to_tensor=True)
-        # Ensure both tensors are on the same device for cosine similarity calculation
-        if query_embedding.device != embeddings.device:
-             query_embedding = query_embedding.to(embeddings.device)
-
-        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):
-    """
-    Performs a search using DuckDuckGo and returns a list of dictionaries.
-    """
-    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.
-    """
-    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:
-        found = search_dates(
-            query,
-            settings={
-                "PREFER_DATES_FROM": "future",
-                "RELATIVE_BASE": now
-            },
-            languages=['sw', 'en']
-        )
-        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'])
-
-        if is_swahili:
-            query_lower = query.lower().strip()
-            if query_lower in ['habari', 'mambo', 'habari gani']:
-                 return "Nzuri! Habari zako?"
-            elif query_lower in ['shikamoo']:
-                 return "Marahaba!"
-            elif query_lower in ['asante']:
-                 return "Karibu!"
-            elif query_lower in ['karibu']:
-                 return "Asante!"
-
-        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)
-
-        if parsed.date() == now.date():
-             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
-# Modified to include complexity check for routing to primary vs fallback
-def determine_tool_usage(query: str) -> tuple[str, str]:
-    """
-    Analyzes the query to determine if a specific tool is needed and its complexity.
-    Returns a tuple: (tool_name, complexity_level)
-    Complexity levels: 'simple' (fallback), 'complex' (primary)
-    """
-    query_lower = query.lower()
-
-    swahili_conversational_phrases = ['habari', 'mambo', 'shikamoo', 'karibu', 'asante', 'habari gani']
-    if any(swahili_phrase in query_lower for swahili_phrase in swahili_conversational_phrases):
-        print(f"Detected a Swahili conversational phrase: '{query}'. Using 'date_calculation' tool and 'simple' complexity.")
-        return "date_calculation", "simple" # Simple conversational queries routed to fallback
-
-    # Check for business info retrieval first
-    if business_info_available:
-         # Use a simple LLM call to check if the query is business-related
-         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 = primary_client.chat_completion( # Use primary client for this check
-                 messages=messages_business_check,
-                 max_tokens=10,
-                 temperature=0.1
-             ).choices[0].message.content.strip().lower()
-             if business_check_response == "yes":
-                 print(f"Detected as specific business info query based on LLM check: '{query}'. Using 'business_info_retrieval' tool and 'simple' complexity.")
-                 # Business info RAG is handled by the fallback model
-                 return "business_info_retrieval", "simple"
-             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.")
-
-    # Check for date/time calculation
-    date_time_check_result = perform_date_calculation(query)
-    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}'. Using 'date_calculation' tool and 'simple' complexity.")
-        # Date calculation is handled by the fallback model
-        return "date_calculation", "simple"
-
-    # Check if web search is needed for general knowledge or current info
-    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 = primary_client.chat_completion( # Use primary client for this check
-            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'. Using 'complex' complexity.")
-            # Web search queries are generally more complex and routed to primary
-            return "duckduckgo_search", "complex"
-        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.")
-
-    # If no specific tool is determined, route based on query complexity
-    messages_complexity = [{"role": "user", "content": f"Is the following query simple or complex? A simple query is a basic question, a greeting, or a question that can be answered with a short, direct response. A complex query requires detailed understanding, multiple steps, or external information synthesis. Respond ONLY with 'simple' or 'complex'. Query: {query}"}]
-    try:
-        complexity_response = primary_client.chat_completion( # Use primary client for complexity check
-             messages=messages_complexity,
-             max_tokens=10,
-             temperature=0.1
-        ).choices[0].message.content.strip().lower()
-
-        if "complex" in complexity_response:
-             print(f"Determined query complexity for '{query}': 'complex'. Using 'none' tool.")
-             return "none", "complex" # No tool, complex query routed to primary
-        else:
-             print(f"Determined query complexity for '{query}': 'simple'. Using 'none' tool.")
-             return "none", "simple" # No tool, simple query routed to fallback
-
-    except Exception as e:
-        print(f"Error during LLM call for complexity determination for query '{query}': {e}")
-        print(traceback.format_exc())
-        print(f"Defaulting query '{query}' to 'complex' due to error.")
-        return "none", "complex" # Default to complex on error
-
-
-# Function to summarize chat history
-def summarize_chat_history(chat_history: list[dict]) -> str:
-    """
-    Summarizes the provided chat history using the LLM.
-    Uses the primary client for summarization.
-    """
-    print("\n--- Summarizing chat history ---")
-    if not chat_history:
-        print("Chat history is empty, no summarization needed.")
-        return ""
-
-    history_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in chat_history])
-
-    prompt_for_summary = f"""
-Please provide a concise summary of the following conversation history.
-Conversation History:
-{history_text}
-
-Summary:
-"""
-    try:
-        messages_summary = [{"role": "user", "content": prompt_for_summary}]
-        summary_response = primary_client.chat_completion( # Use primary client
-            messages=messages_summary,
-            max_tokens=200, # Adjust based on desired summary length
-            temperature=0.3,
-            top_p=0.9
-        ).choices[0].message.content or ""
-        print("Chat history summarization successful using primary client.")
-        return summary_response.strip()
-    except Exception as e:
-        print(f"Error during LLM call for chat history summarization (primary client): {e}")
-        print(traceback.format_exc())
-        return "Unable to summarize previous conversation."
-
-
-# Function to generate text using the LLM, incorporating tool results if available
-# Modified to use primary or fallback client based on complexity
-def generate_text(prompt: str, tool_results: dict = None, chat_history: list[dict] = None, complexity_level: str = 'complex') -> str:
-    """
-    Generates text using the configured LLM (primary or fallback), optionally incorporating tool results and chat history.
-    Implements conversation summarization and windowing for long histories.
-    """
-    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.
-"""
-    messages = [{"role": "user", "content": persona_instructions}]
-
-    # --- Conversation Summarization and Windowing ---
-    SUMMARY_THRESHOLD = 10 # Summarize after 10 turns (5 user/assistant pairs)
-    HISTORY_WINDOW_SIZE = 4 # Keep the last 4 turns (2 user/assistant pairs)
-
-    if chat_history:
-        print(f"Current chat history length: {len(chat_history)}")
-        if len(chat_history) > SUMMARY_THRESHOLD:
-            print("Chat history exceeds threshold, summarizing older turns.")
-            history_to_summarize = chat_history[:-HISTORY_WINDOW_SIZE]
-            summary = summarize_chat_history(history_to_summarize) # summarize_chat_history uses primary client
-            if summary:
-                messages.append({"role": "assistant", "content": f"Summary of previous conversation: {summary}"})
-                print("Added summary to messages.")
-            recent_history = chat_history[-HISTORY_WINDOW_SIZE:]
-            print(f"Including last {len(recent_history)} turns from history.")
-            for message_dict in recent_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})
-        else:
-            print("Including full chat history in LLM prompt.")
-            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})
-
-
-    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 and results != "none": # Only include if results are not None or "none"
-                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})
-
-    generation_config = {
-        "temperature": 0.7,
-        "max_new_tokens": 500,
-        "top_p": 0.95,
-        "top_k": 50,
-        "do_sample": True,
-    }
-
-    try:
-        if complexity_level == 'complex':
-            print("Using primary client for generation.")
-            response = primary_client.chat_completion(
-                messages=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 primary client.")
-        else: # complexity_level == 'simple' or fallback needed
-            print("Using fallback client for generation.")
-            # Use fallback_client for chat completion with Gemma
-            response = fallback_client.chat_completion(
-                messages=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 fallback client.")
-
-
-        return response.strip()
-    except Exception as e:
-        print(f"Error during final LLM generation (primary or fallback): {e}")
-        print(traceback.format_exc())
-        return "An error occurred while generating the final response."
-
-# Function to log conversation data to the Hugging Face Dataset and push
-def log_conversation(user_query: str, model_response: str, tool_details: dict = None, user_id: str = None):
-    """
-    Logs conversation data (query, response, timestamp, optional details) to the Hugging Face Dataset
-    and pushes the changes to the Hub.
-    """
-    global conversation_dataset # Access the global dataset variable
-    global dataset_name # Access the dataset name
-
-    print("\n--- Attempting to log conversation to Hugging Face Dataset ---")
-
-    if conversation_dataset is None:
-        print("Warning: Hugging Face dataset not loaded or created. Skipping conversation logging.")
-        return
-
-    try:
-        timestamp = datetime.now().isoformat()
-        # Ensure tool_details is a JSON string or None
-        tool_details_json = json.dumps(tool_details) if tool_details is not None else None
-        # Handle potential None values for user_id
-        user_id_val = user_id if user_id is not None else "anonymous"
-
-        # Create a dictionary for the new log entry
-        new_log_entry = {
-            'timestamp': timestamp,
-            'user_id': user_id_val,
-            'user_query': user_query,
-            'model_response': model_response,
-            'tool_details': tool_details_json
-        }
-
-        # Append the new log entry to the 'train' split of the dataset
-        new_row_dataset = Dataset.from_dict({key: [value] for key, value in new_log_entry.items()})
-
-        # Check if the 'train' split exists before concatenating
-        if 'train' in conversation_dataset:
-             conversation_dataset['train'] = concatenate_datasets([conversation_dataset['train'], new_row_dataset])
-        else:
-             # If 'train' doesn't exist (e.g., first log entry), create it
-             conversation_dataset['train'] = new_dataset # Use the new_dataset with schema
-
-
-        print("Conversation data successfully added to the dataset object.")
-
-        # --- Pushing to the Hugging Face Hub ---
-        print(f"Attempting to push dataset to {dataset_name}...")
-        # Use the push_to_hub method of the DatasetDict
-        # Use commit_message for clarity
-        conversation_dataset.push_to_hub(dataset_name, token=HF_TOKEN, commit_message=f"Add conversation log: {timestamp}")
-        print(f"Successfully pushed dataset to {dataset_name}.")
-
-
-    except Exception as e:
-        print(f"An unexpected error occurred during Hugging Face Dataset logging and pushing: {e}")
-        print(traceback.format_exc())
-
-
-# Need to import concatenate_datasets
-from datasets import concatenate_datasets
-from huggingface_hub import HfApi # Ensure HfApi is imported
-
-# --- Caching Implementation ---
-# Define the path for the FAISS index file
-FAISS_INDEX_FILE = "cache.index"
-# Define the path for the metadata file (query text, response, timestamp)
-CACHE_METADATA_FILE = "cache_metadata.pkl"
-
-# Global variables for FAISS index and metadata
-faiss_index = None
-cache_metadata = {}
-
-# Dimension of the embeddings (should match your embedder model output dimension)
-# For 'sentence-transformers/paraphrase-MiniLM-L6-v2', the dimension is 384
-EMBEDDING_DIM = 384
-
-CACHE_SIMILARITY_THRESHOLD = 0.9 # Cosine similarity threshold for cache hit
-CACHE_EXPIRATION_DAYS = 7 # Cache entries expire after 7 days
-
-
-def initialize_cache():
-    """Initializes or loads the FAISS index and cache metadata."""
-    global faiss_index, cache_metadata
-    print("\n--- Initializing Cache ---")
-    if os.path.exists(FAISS_INDEX_FILE) and os.path.exists(CACHE_METADATA_FILE):
-        print("Loading existing cache...")
-        try:
-            faiss_index = faiss.read_index(FAISS_INDEX_FILE)
-            with open(CACHE_METADATA_FILE, 'rb') as f:
-                cache_metadata = pickle.load(f)
-            print(f"Cache loaded successfully. Current cache size: {faiss_index.ntotal}")
-            # Clean up expired entries on load
-            cleanup_expired_cache_entries()
-        except Exception as e:
-            print(f"Error loading cache files: {e}. Initializing new cache.")
-            print(traceback.format_exc())
-            faiss_index = faiss.IndexFlatL2(EMBEDDING_DIM) # Using L2 distance
-            cache_metadata = {}
-            save_cache() # Save empty cache
-    else:
-        print("No existing cache found. Initializing new cache.")
-        faiss_index = faiss.IndexFlatL2(EMBEDDING_DIM) # Using L2 distance
-        cache_metadata = {}
-        save_cache() # Save empty cache
-
-def save_cache():
-    """Saves the FAISS index and cache metadata to files."""
-    global faiss_index, cache_metadata
-    if faiss_index is None:
-        print("Warning: FAISS index not initialized. Cannot save cache.")
-        return
-    print("Saving cache...")
-    try:
-        faiss.write_index(faiss_index, FAISS_INDEX_FILE)
-        with open(CACHE_METADATA_FILE, 'wb') as f:
-            pickle.dump(cache_metadata, f)
-        print("Cache saved successfully.")
-    except Exception as e:
-        print(f"Error saving cache files: {e}")
-        print(traceback.format_exc())
-
-def get_query_embedding(query: str):
-    """Generates an embedding for the given query."""
-    if embedder is None:
-        print("Warning: Embedder not available. Cannot generate query embedding for caching.")
-        return None
-    try:
-        return embedder.encode(query, convert_to_tensor=False) # Return numpy array for FAISS
-    except Exception as e:
-        print(f"Error generating embedding for query '{query}': {e}")
-        print(traceback.format_exc())
-        return None
-
-def add_to_cache(query: str, response: str):
-    """Adds the query, response, and timestamp to the cache."""
-    global faiss_index, cache_metadata
-    if embedder is None or faiss_index is None:
-        print("Warning: Embedder or FAISS index not available. Cannot add query to cache.")
-        return
-
-    try:
-        query_embedding = get_query_embedding(query)
-        if query_embedding is None:
-            return
-
-        # Add the embedding to the FAISS index
-        faiss_index.add(np.array([query_embedding])) # Add expects a numpy array of shape (n, dim)
-
-        # Store metadata (query, response, timestamp) keyed by the FAISS index ID
-        # The last added embedding gets the index faiss_index.ntotal - 1
-        cache_id = faiss_index.ntotal - 1
-        now = datetime.now()
-        cache_metadata[cache_id] = {
-            'query': query, # Store original query for debugging/verification
-            'response': response,
-            'timestamp': now,
-            'count': 1 # Initialize count
-        }
-        print(f"Added query and response to cache with ID {cache_id}.")
-        save_cache() # Save cache after adding
-        print(f"Current cache size: {faiss_index.ntotal}")
-
-    except Exception as e:
-        print(f"Error adding query to cache: {e}")
-        print(traceback.format_exc())
-
-
-def check_cache(query: str):
-    """Checks the cache for a similar query and returns the cached response if found and not expired."""
-    global faiss_index, cache_metadata
-    if faiss_index is None or embedder is None or faiss_index.ntotal == 0:
-        print("Cache is empty or not available. Skipping cache check.")
-        return None
-
-    try:
-        query_embedding = get_query_embedding(query)
-        if query_embedding is None:
-            return None
-
-        # Search the FAISS index for similar embeddings
-        # D is distances, I is indices of the nearest neighbors
-        D, I = faiss_index.search(np.array([query_embedding]), 1) # Search for the 1 nearest neighbor
-
-        if I[0][0] != -1 and D[0][0] <= (1 - CACHE_SIMILARITY_THRESHOLD): # Check if a neighbor was found and distance is within threshold
-            cached_id = I[0][0]
-            print(f"Found potential cache hit with ID {cached_id} and distance {D[0][0]:.4f}.")
-
-            if cached_id in cache_metadata:
-                cached_data = cache_metadata[cached_id]
-                now = datetime.now()
-                # Check for expiration
-                if (now - cached_data['timestamp']).days <= CACHE_EXPIRATION_DAYS:
-                    print(f"Cache hit! Returning cached response for query: '{query}'")
-                    # Update timestamp and count on cache hit
-                    cache_metadata[cached_id]['timestamp'] = now
-                    cache_metadata[cached_id]['count'] += 1
-                    save_cache() # Save cache after updating metadata
-                    return cached_data['response']
-                else:
-                    print(f"Cache entry with ID {cached_id} found but expired.")
-                    # We could remove the expired entry here, but it's handled by cleanup_expired_cache_entries
-            else:
-                 print(f"Cache ID {cached_id} found in index but not in metadata. Cache inconsistency.")
-
-        print(f"No suitable cache entry found for query: '{query}'")
-        return None
-
-    except Exception as e:
-        print(f"Error during cache check: {e}")
-        print(traceback.format_exc())
-        return None
-
-def cleanup_expired_cache_entries():
-    """Removes expired entries from the cache and rebuilds the FAISS index if necessary."""
-    global faiss_index, cache_metadata
-    if faiss_index is None or faiss_index.ntotal == 0:
-        print("Cache is empty or not initialized. No expired entries to clean.")
-        return
-
-    print("Cleaning up expired cache entries...")
-    now = datetime.now()
-    expired_ids = [
-        cache_id for cache_id, cached_data in cache_metadata.items()
-        if (now - cached_data['timestamp']).days > CACHE_EXPIRATION_DAYS
-    ]
-
-    if expired_ids:
-        print(f"Found {len(expired_ids)} expired cache entries.")
-        # Remove from metadata
-        for cache_id in expired_ids:
-            del cache_metadata[cache_id]
-
-        # Rebuild FAISS index with non-expired entries
-        if cache_metadata:
-            print("Rebuilding FAISS index with non-expired entries...")
-            try:
-                # Get embeddings for non-expired entries
-                non_expired_embeddings = []
-                non_expired_metadata_list = sorted(cache_metadata.items()) # Sort by ID to maintain order
-                for cache_id, cached_data in non_expired_metadata_list:
-                    # Need to retrieve original query to re-embed
-                    original_query = cached_data.get('query')
-                    if original_query and embedder:
-                         try:
-                             non_expired_embeddings.append(embedder.encode(original_query, convert_to_tensor=False).tolist())
-                         except Exception as e:
-                             print(f"Error re-embedding query '{original_query}': {e}. Skipping.")
-
-
-                if non_expired_embeddings:
-                    print(f"Re-embedding {len(non_expired_embeddings)} non-expired queries.")
-                    faiss_index = faiss.IndexFlatL2(EMBEDDING_DIM)
-                    faiss_index.add(np.array(non_expired_embeddings))
-                    print(f"FAISS index rebuilt. New size: {faiss_index.ntotal}")
-                else:
-                     print("No non-expired entries to rebuild FAISS index. Clearing index.")
-                     faiss_index = faiss.IndexFlatL2(EMBEDDING_DIM)
-                     cache_metadata = {} # Clear metadata if index is cleared
-
-            except Exception as e:
-                print(f"Error rebuilding FAISS index: {e}")
-                print(traceback.format_exc())
-                # On error, it might be safer to clear the cache to avoid inconsistencies
-                print("Clearing cache due to rebuild error.")
-                faiss_index = faiss.IndexFlatL2(EMBEDDING_DIM)
-                cache_metadata = {}
-
-        else:
-            print("All cache entries expired. Clearing FAISS index and metadata.")
-            faiss_index = faiss.IndexFlatL2(EMBEDDING_DIM)
-            cache_metadata = {}
-
-        save_cache() # Save after cleanup
-    else:
-        print("No expired cache entries found.")
-
-
-# Main chat function with query breakdown and tool execution per question
-def chat(query: str, chat_history: list[dict], api_key: str):
-    """
-    Processes user queries by breaking down multi-part queries, determining and
-    executing appropriate tools for each question, and synthesizing results
-    using the LLM. Incorporates caching for repeated questions and routes
-    to primary or fallback model based on complexity.
-    """
-    print(f"\n--- chat function received new query ---")
-    print(f"  query: {query}")
-    print(f"  Validating against SECRET_API_KEY: {'*' * len(SECRET_API_KEY) if SECRET_API_KEY else 'None'}")
-    print(f"  chat_history: {chat_history}")
-    print(f"  api_key from UI: {'*' * len(api_key) if api_key else 'None'}")
-
-    if not SECRET_API_KEY:
-        print("Error: APP_API_KEY secret not set in Hugging Face Space Secrets.")
-        # Log failure before returning
-        log_conversation(
-             user_query=query,
-             model_response="API key validation failed: Application not configured correctly. APP_API_KEY secret is missing.",
-             tool_details={"validation_status": "failed", "reason": "secret_not_set"},
-             user_id="unknown"
-        )
-        return "API key validation failed: Application not configured correctly. APP_API_KEY secret is missing."
-
-    if api_key != SECRET_API_KEY:
-        print("Error: API key from UI does not match SECRET_API_KEY.")
-        # Log failure before returning
-        log_conversation(
-            user_query=query,
-            model_response="API key validation failed: Invalid API key provided.",
-            tool_details={"validation_status": "failed", "reason": "invalid_api_key"},
-            user_id="unknown"
-        )
-        return "API key validation failed: Invalid API key provided."
-
-    # --- Cache Check ---
-    cached_response = check_cache(query)
-    if cached_response:
-        print(f"Returning cached response for query: '{query}'")
-        # Log the cached response
-        try:
-            user_id_to_log = "anonymous"
-            if chat_history:
-                 for turn in chat_history:
-                     if turn.get("role") == "user" and "user_id:" in turn.get("content", "").lower():
-                          match = re.search(r"user_id:\s*(\S+)", turn.get("content", ""), re.IGNORECASE)
-                          if match:
-                               user_id_to_log = match.group(1)
-                               break
-
-            log_conversation(
-                user_query=query,
-                model_response=cached_response,
-                tool_details={"cache_status": "hit"},
-                user_id=user_id_to_log
-            )
-        except Exception as e:
-            print(f"Error during logging of cached response: {e}")
-            print(traceback.format_exc())
-
-        return cached_response
-
-    print("\n--- Breaking down query ---")
-    # Use the primary client for query breakdown as it's generally better at understanding complex queries
-    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 = primary_client.chat_completion( # Use primary client
-            messages=[{"role": "user", "content": prompt_for_question_breakdown}],
-            max_tokens=100,
-            temperature=0.1,
-            top_p=0.9
-        ).choices[0].message.content or ""
-        individual_questions = [line.strip() for line in messages_question_breakdown.split('\n') if line.strip()]
-        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 (primary client): {e}")
-        print(traceback.format_exc())
-        print(f"Proceeding with original query as a single question due to breakdown error.")
-        cleaned_questions = [query]
-
-    print("\n--- Determining tools and complexity per question ---")
-    determined_tools_and_complexity = {}
-    for question in cleaned_questions:
-        print(f"\nAnalyzing question for tool determination and complexity: '{question}'")
-        tool, complexity = determine_tool_usage(question) # determine_tool_usage uses primary client for checks
-        determined_tools_and_complexity[question] = {"tool": tool, "complexity": complexity}
-        print(f"Determined tool and complexity for '{question}': Tool='{tool}', Complexity='{complexity}'")
-
-    print("\nSummary of determined tools and complexity per question:")
-    for question, details in determined_tools_and_complexity.items():
-        print(f"'{question}': Tool='{details['tool']}', Complexity='{details['complexity']}'")
-
-    print("\n--- Executing tools and collecting results ---")
-    tool_results = {}
-    for question, details in determined_tools_and_complexity.items():
-        tool = details['tool']
-        print(f"\nExecuting tool '{tool}' for question: '{question}')")
-        result = None
-        if tool == "date_calculation":
-            result = perform_date_calculation(question)
-            tool_results[question] = result
-        elif tool == "duckduckgo_search":
-            result = perform_duckduckgo_search(question)
-            tool_results[question] = result
-        elif tool == "business_info_retrieval":
-            result = retrieve_business_info(question)
-            tool_results[question] = result
-        elif tool == "none":
-             print(f"Skipping tool execution for question: '{question}' as tool is 'none'. LLM will handle.")
-             tool_results[question] = "none"
-
-    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--------------------------")
-
-    print("\n--- Generating final response ---")
-
-    # Determine the overall complexity to choose the final generation model
-    # If any question was determined as 'complex', use the primary model
-    overall_complexity = 'simple'
-    for details in determined_tools_and_complexity.values():
-        if details['complexity'] == 'complex':
-            overall_complexity = 'complex'
-            break
-    print(f"Overall query complexity determined as: '{overall_complexity}'")
-
-
-    final_response = generate_text(query, tool_results, chat_history, complexity_level=overall_complexity)
-    print("\n--- Final Response from LLM ---")
-    print(final_response)
-    print("\n----------------------------")
-
-    # --- Add response to cache ---
-    # We add the entire query and final response to the cache, not individual questions.
-    add_to_cache(query, final_response)
-
-    try:
-        user_id_to_log = "anonymous"
-        if chat_history:
-             for turn in chat_history:
-                 if turn.get("role") == "user" and "user_id:" in turn.get("content", "").lower():
-                      match = re.search(r"user_id:\s*(\S+)", turn.get("content", ""), re.IGNORECASE)
-                      if match:
-                           user_id_to_log = match.group(1)
-                           break
-
-        logged_tool_details = {}
-        for question, details in determined_tools_and_complexity.items():
-            logged_tool_details[question] = {
-                 "tool_used": details['tool'],
-                 "complexity": details['complexity'],
-                 "raw_output": tool_results.get(question)
-            }
-        logged_tool_details["cache_status"] = "miss" # Log cache miss when generating a new response
-        logged_tool_details["model_used_for_generation"] = "primary" if overall_complexity == 'complex' else "fallback"
-
-
-        # Call the logging function (currently logs to Hugging Face Dataset)
-        log_conversation(
-            user_query=query,
-            model_response=final_response,
-            tool_details=logged_tool_details,
-            user_id=user_id_to_log
-        )
-    except Exception as e:
-        print(f"Error during conversation logging after response generation: {e}")
-        print(traceback.format_exc())
-
-    return final_response
-
-# Keep the Gradio interface setup as is for now
-if __name__ == "__main__":
-    # Load/Create Hugging Face Dataset on startup
-    try:
-        # Attempt to load the existing dataset
-        print(f"Attempting to load dataset from {dataset_name} on startup...")
-        # Use load_dataset for loading directly from the Hub
-        conversation_dataset = load_dataset(dataset_name, token=HF_TOKEN)
-        print(f"Successfully loaded existing dataset from {dataset_name} on startup.")
-        print(conversation_dataset)
-
-    except Exception as e:
-        print(f"Dataset not found or failed to load from {dataset_name} on startup: {e}")
-        print("Creating a new dataset object on startup...")
-
-        # Define the schema for conversation logs
-        # Using 'string' as the data type for simplicity, tool_details will be JSON string
-        log_schema = {
-            'timestamp': 'string',
-            'user_id': 'string',
-            'user_query': 'string',
-            'model_response': 'string',
-            'tool_details': 'string' # Store JSON string here
-        }
-
-        # Create an empty dataset with the defined schema
-        empty_data = {col: [] for col in log_schema.keys()}
-        new_dataset = Dataset.from_dict(empty_data)
-
-        # Wrap the dataset in a DatasetDict
-        conversation_dataset = DatasetDict({'train': new_dataset})
-
-        print(f"Created a new empty dataset object with schema: {log_schema}")
-        print(conversation_dataset)
-
-
-    authenticate_google_sheets()
-    load_business_info() # This will also create RAG embeddings if data is loaded
-
-    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.")
-
-
-    DESCRIPTION = """
-    # LLM with Tools (DuckDuckGo Search, Date Calculation, Business Info RAG, Hugging Face Dataset Logging) and Two-Tier Model System
-    Ask me anything! I can perform web searches, calculate dates, retrieve business information using RAG, and conversation data will be logged to a Hugging Face Dataset. I use a primary LLaMA-70B model for complex queries and a fallback Gemma-2-9b-it model for simpler ones and RAG synthesis.
-    """
-
-    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?"],
-            ["What is the time in East Africa and search for latest AI news"],
-            ["Who is Jackson Kisanga?"], # Example for business info retrieval
-            ["What is the weather like in London?"], # Example for web search
-            ["Tell me a joke."], # Example for simple query
-        ],
-        cache_examples=False,
-        type="messages",
-        description=DESCRIPTION,
-        fill_height=True,
-        additional_inputs=[
-            gr.Textbox(label="API Key", type="password", placeholder="Enter your API key (starts with fs_)", interactive=True)
-        ],
-        additional_inputs_accordion="API Key (Required)"
-    )
-
-    try:
-        # Initialize the cache before launching the demo
-        initialize_cache()
-        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.")