__init__.py

import requests
from bs4 import BeautifulSoup
import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta
import logging
from concurrent.futures import ThreadPoolExecutor, as_completed
from langchain_google_genai import ChatGoogleGenerativeAI
from config import Config
import numpy as np
from typing import Optional, Tuple, List, Dict
from rag import get_answer
import time
from tenacity import retry, stop_after_attempt, wait_exponential

# Set up logging
logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
                    handlers=[logging.FileHandler("app.log"),
                              logging.StreamHandler()])

logger = logging.getLogger(__name__)

# Initialize the Gemini model
llm = ChatGoogleGenerativeAI(api_key=Config.GEMINI_API_KEY, model="gemini-1.5-flash-latest", temperature=0.5)

# Configuration for Google Custom Search API
GOOGLE_API_KEY = Config.GOOGLE_API_KEY
SEARCH_ENGINE_ID = Config.SEARCH_ENGINE_ID


@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=2, max=8), reraise=True)
def invoke_llm(prompt):
    return llm.invoke(prompt)


class DataSummarizer:
    def __init__(self):
        pass

    def google_search(self, query: str) -> Optional[str]:
        start_time = time.time()
        try:
            url = "https://www.googleapis.com/customsearch/v1"
            params = {
                'key': GOOGLE_API_KEY,
                'cx': SEARCH_ENGINE_ID,
                'q': query
            }
            response = requests.get(url, params=params)
            response.raise_for_status()
            search_results = response.json()
            logger.info("google_search took %.2f seconds", time.time() - start_time)

            # Summarize the search results using Gemini
            items = search_results.get('items', [])
            content = "\n\n".join([f"{item.get('title', '')}\n{item.get('snippet', '')}" for item in items])
            prompt = f"Summarize the following search results:\n\n{content}"
            summary_response = invoke_llm(prompt)
            return summary_response.content.strip()
        except Exception as e:
            logger.error(f"Error during Google Search API request: {e}")
            return None

    def extract_content_from_item(self, item: Dict) -> Optional[str]:
        try:
            snippet = item.get('snippet', '')
            title = item.get('title', '')
            return f"{title}\n{snippet}"
        except Exception as e:
            logger.error(f"Error extracting content from item: {e}")
            return None

    def calculate_moving_average(self, df: pd.DataFrame, window: int = 20) -> Optional[pd.Series]:
        start_time = time.time()
        try:
            result = df['close'].rolling(window=window).mean()
            logger.info("calculate_moving_average took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating moving average: {e}")
            return None

    def calculate_rsi(self, df: pd.DataFrame, window: int = 14) -> Optional[pd.Series]:
        start_time = time.time()
        try:
            delta = df['close'].diff()
            gain = delta.where(delta > 0, 0).rolling(window=window).mean()
            loss = -delta.where(delta < 0, 0).rolling(window=window).mean()
            rs = gain / loss
            result = 100 - (100 / (1 + rs))
            logger.info("calculate_rsi took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating RSI: {e}")
            return None

    def calculate_ema(self, df: pd.DataFrame, window: int = 20) -> Optional[pd.Series]:
        start_time = time.time()
        try:
            result = df['close'].ewm(span=window, adjust=False).mean()
            logger.info("calculate_ema took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating EMA: {e}")
            return None

    def calculate_bollinger_bands(self, df: pd.DataFrame, window: int = 20) -> Optional[pd.DataFrame]:
        start_time = time.time()
        try:
            ma = df['close'].rolling(window=window).mean()
            std = df['close'].rolling(window=window).std()
            upper_band = ma + (std * 2)
            lower_band = ma - (std * 2)
            result = pd.DataFrame({'MA': ma, 'Upper Band': upper_band, 'Lower Band': lower_band})
            logger.info("calculate_bollinger_bands took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating Bollinger Bands: {e}")
            return None

    def calculate_macd(self, df: pd.DataFrame, short_window: int = 12, long_window: int = 26, signal_window: int = 9) -> \
            Optional[pd.DataFrame]:
        start_time = time.time()
        try:
            short_ema = df['close'].ewm(span=short_window, adjust=False).mean()
            long_ema = df['close'].ewm(span=long_window, adjust=False).mean()
            macd = short_ema - long_ema
            signal = macd.ewm(span=signal_window, adjust=False).mean()
            result = pd.DataFrame({'MACD': macd, 'Signal Line': signal})
            logger.info("calculate_macd took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating MACD: {e}")
            return None

    def calculate_volatility(self, df: pd.DataFrame, window: int = 20) -> Optional[pd.Series]:
        start_time = time.time()
        try:
            log_returns = np.log(df['close'] / df['close'].shift(1))
            result = log_returns.rolling(window=window).std() * np.sqrt(window)
            logger.info("calculate_volatility took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating volatility: {e}")
            return None

    def calculate_atr(self, df: pd.DataFrame, window: int = 14) -> Optional[pd.Series]:
        start_time = time.time()
        try:
            high_low = df['high'] - df['low']
            high_close = np.abs(df['high'] - df['close'].shift())
            low_close = np.abs(df['low'] - df['close'].shift())
            true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
            result = true_range.rolling(window=window).mean()
            logger.info("calculate_atr took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating ATR: {e}")
            return None

    def calculate_obv(self, df: pd.DataFrame) -> Optional[pd.Series]:
        start_time = time.time()
        try:
            result = (np.sign(df['close'].diff()) * df['volume']).fillna(0).cumsum()
            logger.info("calculate_obv took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating OBV: {e}")
            return None

    def calculate_yearly_summary(self, df: pd.DataFrame) -> Optional[pd.DataFrame]:
        start_time = time.time()
        try:
            df['year'] = pd.to_datetime(df['date']).dt.year
            yearly_summary = df.groupby('year').agg({
                'close': ['mean', 'max', 'min'],
                'volume': 'sum'
            })
            yearly_summary.columns = ['_'.join(col) for col in yearly_summary.columns]
            logger.info("calculate_yearly_summary took %.2f seconds", time.time() - start_time)
            return yearly_summary
        except Exception as e:
            logger.error(f"Error calculating yearly summary: {e}")
            return None

    def get_full_last_year(self, df: pd.DataFrame) -> Optional[pd.DataFrame]:
        start_time = time.time()
        try:
            today = datetime.today().date()
            last_year_start = datetime(today.year - 1, 1, 1).date()
            last_year_end = datetime(today.year - 1, 12, 31).date()
            mask = (df['date'] >= last_year_start) & (df['date'] <= last_year_end)
            result = df.loc[mask]
            logger.info("get_full_last_year took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error filtering data for the last year: {e}")
            return None

    def calculate_ytd_performance(self, df: pd.DataFrame) -> Optional[float]:
        start_time = time.time()
        try:
            today = datetime.today().date()
            year_start = datetime(today.year, 1, 1).date()
            mask = (df['date'] >= year_start) & (df['date'] <= today)
            ytd_data = df.loc[mask]
            opening_price = ytd_data.iloc[0]['open']
            closing_price = ytd_data.iloc[-1]['close']
            result = ((closing_price - opening_price) / opening_price) * 100
            logger.info("calculate_ytd_performance took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating YTD performance: {e}")
            return None

    def calculate_pe_ratio(self, current_price: float, eps: float) -> Optional[float]:
        start_time = time.time()
        try:
            if eps == 0:
                raise ValueError("EPS cannot be zero for P/E ratio calculation.")
            result = current_price / eps
            logger.info("calculate_pe_ratio took %.2f seconds", time.time() - start_time)
            return result
        except Exception as e:
            logger.error(f"Error calculating P/E ratio: {e}")
            return None

    def fetch_google_snippet(self, query: str) -> Optional[str]:
        try:
            search_url = f"https://www.google.com/search?q={query}"
            headers = {
                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36"
            }
            response = requests.get(search_url, headers=headers)
            soup = BeautifulSoup(response.text, 'html.parser')
            snippet_classes = [
                'BNeawe iBp4i AP7Wnd',
                'BNeawe s3v9rd AP7Wnd',
                'BVG0Nb',
                'kno-rdesc'
            ]
            snippet = None
            for cls in snippet_classes:
                snippet = soup.find('div', class_=cls)
                if snippet:
                    break
            return snippet.get_text() if snippet else "Snippet not found."
        except Exception as e:
            logger.error(f"Error fetching Google snippet: {e}")
            return None


def extract_ticker_from_response(response: str) -> Optional[str]:
    start_time = time.time()
    try:
        if "is **" in response and "**." in response:
            result = response.split("is **")[1].split("**.")[0].strip()
            logger.info("extract_ticker_from_response took %.2f seconds", time.time() - start_time)
            return result
        result = response.strip()
        logger.info("extract_ticker_from_response took %.2f seconds", time.time() - start_time)
        return result
    except Exception as e:
        logger.error(f"Error extracting ticker from response: {e}")
        return None


def detect_translate_entity_and_ticker(query: str) -> Tuple[Optional[str], Optional[str], Optional[str], Optional[str]]:
    try:
        start_time = time.time()

        # Step 1: Detect Language
        prompt = f"Detect the language for the following text: {query}"
        response = invoke_llm(prompt)
        detected_language = response.content.strip()
        logger.info(f"Language detected: {detected_language}")

        # Step 2: Translate to English (if necessary)
        translated_query = query
        if detected_language != "English":
            prompt = f"Translate the following text to English: {query}"
            response = invoke_llm(prompt)
            translated_query = response.content.strip()
            logger.info(f"Translation completed: {translated_query}")
            print(f"Translation: {translated_query}")

        # Step 3: Detect Entity
        prompt = f"Detect the entity in the following text that is a company name: {translated_query}"
        response = invoke_llm(prompt)
        detected_entity = response.content.strip()
        logger.info(f"Entity detected: {detected_entity}")
        print(f"Entity: {detected_entity}")

        if not detected_entity:
            logger.error("No entity detected")
            return detected_language, None, translated_query, None

        # Step 4: Get Stock Ticker
        prompt = f"What is the stock ticker symbol for the company {detected_entity}?"
        response = invoke_llm(prompt)
        stock_ticker = extract_ticker_from_response(response.content.strip())

        if not stock_ticker:
            logger.error("No stock ticker detected")
            return detected_language, detected_entity, translated_query, None

        logger.info("detect_translate_entity_and_ticker took %.2f seconds", time.time() - start_time)
        return detected_language, detected_entity, translated_query, stock_ticker
    except Exception as e:
        logger.error(f"Error in detecting, translating, or extracting entity and ticker: {e}")
        return None, None, None, None


def fetch_stock_data_yahoo(symbol: str) -> pd.DataFrame:
    start_time = time.time()
    try:
        stock = yf.Ticker(symbol)
        logger.info(f"Fetching data for symbol: {symbol}")

        end_date = datetime.now()
        start_date = end_date - timedelta(days=3 * 365)

        historical_data = stock.history(start=start_date, end=end_date)
        if historical_data.empty:
            raise ValueError(f"No historical data found for symbol: {symbol}")

        historical_data = historical_data.rename(
            columns={"Open": "open", "High": "high", "Low": "low", "Close": "close", "Volume": "volume"}
        )

        historical_data.reset_index(inplace=True)
        historical_data['date'] = historical_data['Date'].dt.date
        historical_data = historical_data.drop(columns=['Date'])
        historical_data = historical_data[['date', 'open', 'high', 'low', 'close', 'volume']]

        if 'close' not in historical_data.columns:
            raise KeyError("The historical data must contain a 'close' column.")

        logger.info("fetch_stock_data_yahoo took %.2f seconds", time.time() - start_time)
        return historical_data
    except Exception as e:
        logger.error(f"Failed to fetch stock data for {symbol} from Yahoo Finance: {e}")
        return pd.DataFrame()


def fetch_current_stock_price(symbol: str) -> Optional[float]:
    start_time = time.time()
    try:
        stock = yf.Ticker(symbol)
        result = stock.info['currentPrice']
        logger.info("fetch_current_stock_price took %.2f seconds", time.time() - start_time)
        return result
    except Exception as e:
        logger.error(f"Failed to fetch current stock price for {symbol}: {e}")
        return None


def format_stock_data_for_gemini(stock_data: pd.DataFrame) -> str:
    start_time = time.time()
    try:
        if stock_data.empty:
            return "No historical data available."

        formatted_data = "Historical stock data for the last three years:\n\n"
        formatted_data += "Date       | Open   | High   | Low    | Close  | Volume\n"
        formatted_data += "------------------------------------------------------\n"

        for index, row in stock_data.iterrows():
            formatted_data += f"{row['date']} | {row['open']:.2f} | {row['high']:.2f} | {row['low']:.2f} | {row['close']:.2f} | {int(row['volume'])}\n"

        logger.info("format_stock_data_for_gemini took %.2f seconds", time.time() - start_time)
        return formatted_data
    except Exception as e:
        logger.error(f"Error formatting stock data for Gemini: {e}")
        return "Error formatting stock data."


def fetch_company_info_yahoo(symbol: str) -> Dict:
    start_time = time.time()
    try:
        if not symbol:
            return {"error": "Invalid symbol"}

        stock = yf.Ticker(symbol)
        company_info = stock.info
        logger.info("fetch_company_info_yahoo took %.2f seconds", time.time() - start_time)
        return {
            "name": company_info.get("longName", "N/A"),
            "sector": company_info.get("sector", "N/A"),
            "industry": company_info.get("industry", "N/A"),
            "marketCap": company_info.get("marketCap", "N/A"),
            "summary": company_info.get("longBusinessSummary", "N/A"),
            "website": company_info.get("website", "N/A"),
            "address": company_info.get("address1", "N/A"),
            "city": company_info.get("city", "N/A"),
            "state": company_info.get("state", "N/A"),
            "country": company_info.get("country", "N/A"),
            "phone": company_info.get("phone", "N/A")
        }
    except Exception as e:
        logger.error(f"Error fetching company info for {symbol}: {e}")
        return {"error": str(e)}


def format_company_info_for_gemini(company_info: Dict) -> str:
    start_time = time.time()
    try:
        if "error" in company_info:
            return f"Error fetching company info: {company_info['error']}"

        formatted_info = (f"\nCompany Information:\n"
                          f"Name: {company_info['name']}\n"
                          f"Sector: {company_info['sector']}\n"
                          f"Industry: {company_info['industry']}\n"
                          f"Market Cap: {company_info['marketCap']}\n"
                          f"Summary: {company_info['summary']}\n"
                          f"Website: {company_info['website']}\n"
                          f"Address: {company_info['address']}, {company_info['city']}, {company_info['state']}, {company_info['country']}\n"
                          f"Phone: {company_info['phone']}\n")

        logger.info("format_company_info_for_gemini took %.2f seconds", time.time() - start_time)
        return formatted_info
    except Exception as e:
        logger.error(f"Error formatting company info for Gemini: {e}")
        return "Error formatting company info."


def fetch_company_news_yahoo(symbol: str) -> List[Dict]:
    start_time = time.time()
    try:
        stock = yf.Ticker(symbol)
        news = stock.news
        if not news:
            raise ValueError(f"No news found for symbol: {symbol}")
        logger.info("fetch_company_news_yahoo took %.2f seconds", time.time() - start_time)
        return news
    except Exception as e:
        logger.error(f"Failed to fetch news for {symbol} from Yahoo Finance: {e}")
        return []


def format_company_news_for_gemini(news: List[Dict]) -> str:
    start_time = time.time()
    try:
        if not news:
            return "No news available."

        formatted_news = "Latest company news:\n\n"
        for article in news:
            formatted_news += (f"Title: {article['title']}\n"
                               f"Publisher: {article['publisher']}\n"
                               f"Link: {article['link']}\n"
                               f"Published: {article['providerPublishTime']}\n\n")

        logger.info("format_company_news_for_gemini took %.2f seconds", time.time() - start_time)
        return formatted_news
    except Exception as e:
        logger.error(f"Error formatting company news for Gemini: {e}")
        return "Error formatting company news."


def send_to_gemini_for_summarization(content: str) -> str:
    start_time = time.time()
    try:
        unified_content = " ".join(content)
        prompt = f"Summarize the main points of this article.\n\n{unified_content}"
        response = invoke_llm(prompt)
        logger.info("send_to_gemini_for_summarization took %.2f seconds", time.time() - start_time)
        return response.content.strip()
    except Exception as e:
        logger.error(f"Error sending content to Gemini for summarization: {e}")
        return "Error summarizing content."


def answer_question_with_data(question: str, data: Dict) -> str:
    start_time = time.time()
    try:
        data_str = ""
        for key, value in data.items():
            data_str += f"{key}:\n{value}\n\n"

        prompt = (f"You are a financial advisor. Begin your answer by stating that and only give the answer after.\n"
                  f"Using the following data, answer this question: {question}\n\nData:\n{data_str}\n"
                  f"Make your answer in the best form and professional.\n"
                  f"Don't say anything about the source of the data.\n"
                  f"If you don't have the data to answer, say this data is not available yet. If the data is not available in the stock history data, say this was a weekend and there is no data for it.")
        response = invoke_llm(prompt)
        logger.info("answer_question_with_data took %.2f seconds", time.time() - start_time)
        return response.content.strip()
    except Exception as e:
        logger.error(f"Error answering question with data: {e}")
        return "Error answering question."


def calculate_metrics(stock_data: pd.DataFrame, summarizer: DataSummarizer, company_info: Dict) -> Dict[str, str]:
    start_time = time.time()
    try:
        moving_average = summarizer.calculate_moving_average(stock_data)
        rsi = summarizer.calculate_rsi(stock_data)
        ema = summarizer.calculate_ema(stock_data)
        bollinger_bands = summarizer.calculate_bollinger_bands(stock_data)
        macd = summarizer.calculate_macd(stock_data)
        volatility = summarizer.calculate_volatility(stock_data)
        atr = summarizer.calculate_atr(stock_data)
        obv = summarizer.calculate_obv(stock_data)
        yearly_summary = summarizer.calculate_yearly_summary(stock_data)
        ytd_performance = summarizer.calculate_ytd_performance(stock_data)

        eps = company_info.get('trailingEps', None)
        if eps:
            current_price = stock_data.iloc[-1]['close']
            pe_ratio = summarizer.calculate_pe_ratio(current_price, eps)
            formatted_metrics = {
                "Moving Average": moving_average.to_string(),
                "RSI": rsi.to_string(),
                "EMA": ema.to_string(),
                "Bollinger Bands": bollinger_bands.to_string(),
                "MACD": macd.to_string(),
                "Volatility": volatility.to_string(),
                "ATR": atr.to_string(),
                "OBV": obv.to_string(),
                "Yearly Summary": yearly_summary.to_string(),
                "YTD Performance": f"{ytd_performance:.2f}%",
                "P/E Ratio": f"{pe_ratio:.2f}"
            }
        else:
            formatted_metrics = {
                "Moving Average": moving_average.to_string(),
                "RSI": rsi.to_string(),
                "EMA": ema.to_string(),
                "Bollinger Bands": bollinger_bands.to_string(),
                "MACD": macd.to_string(),
                "Volatility": volatility.to_string(),
                "ATR": atr.to_string(),
                "OBV": obv.to_string(),
                "Yearly Summary": yearly_summary.to_string(),
                "YTD Performance": f"{ytd_performance:.2f}%"
            }

        logger.info("calculate_metrics took %.2f seconds", time.time() - start_time)
        return formatted_metrics
    except Exception as e:
        logger.error(f"Error calculating metrics: {e}")
        return {"Error": "Error calculating metrics"}


def prepare_data(formatted_stock_data: str, formatted_company_info: str, formatted_company_news: str,
                 google_results: str, formatted_metrics: Dict[str, str], google_snippet: str, rag_response: str) -> \
Dict[str, str]:
    start_time = time.time()
    collected_data = {
        "Formatted Stock Data": formatted_stock_data,
        "Formatted Company Info": formatted_company_info,
        "Formatted Company News": formatted_company_news,
        "Google Search Results": google_results,
        "Google Snippet": google_snippet,
        "RAG Response": rag_response,
        "Calculations": formatted_metrics
    }
    collected_data.update(formatted_metrics)
    logger.info("prepare_data took %.2f seconds", time.time() - start_time)
    return collected_data


def main():
    print("Welcome to the Financial Data Chatbot. How can I assist you today?")

    summarizer = DataSummarizer()
    conversation_history = []

    while True:
        user_input = input("You: ")

        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("Goodbye! Have a great day!")
            break

        conversation_history.append(f"You: {user_input}")

        try:
            # Detect language, entity, translation, and stock ticker
            language, entity, translation, stock_ticker = detect_translate_entity_and_ticker(user_input)

            logger.info(
                f"Detected Language: {language}, Entity: {entity}, Translation: {translation}, Stock Ticker: {stock_ticker}")

            if entity and stock_ticker:
                with ThreadPoolExecutor() as executor:
                    futures = {
                        executor.submit(fetch_stock_data_yahoo, stock_ticker): "stock_data",
                        executor.submit(fetch_company_info_yahoo, stock_ticker): "company_info",
                        executor.submit(fetch_company_news_yahoo, stock_ticker): "company_news",
                        executor.submit(fetch_current_stock_price, stock_ticker): "current_stock_price",
                        executor.submit(get_answer, user_input): "rag_response",
                        executor.submit(summarizer.google_search, user_input): "google_results",
                        executor.submit(summarizer.fetch_google_snippet, user_input): "google_snippet"
                    }
                    results = {futures[future]: future.result() for future in as_completed(futures)}

                stock_data = results.get("stock_data", pd.DataFrame())
                formatted_stock_data = format_stock_data_for_gemini(
                    stock_data) if not stock_data.empty else "No historical data available."

                company_info = results.get("company_info", {})
                formatted_company_info = format_company_info_for_gemini(
                    company_info) if company_info else "No company info available."

                company_news = results.get("company_news", [])
                formatted_company_news = format_company_news_for_gemini(
                    company_news) if company_news else "No news available."

                current_stock_price = results.get("current_stock_price", None)

                formatted_metrics = calculate_metrics(stock_data, summarizer,
                                                      company_info) if not stock_data.empty else {
                    "Error": "No stock data for metrics"}

                google_results = results.get("google_results", "No additional news found through Google Search.")
                google_snippet = results.get("google_snippet", "Snippet not found.")

                rag_response = results.get("rag_response", "No response from RAG.")

                collected_data = prepare_data(formatted_stock_data, formatted_company_info, formatted_company_news,
                                              google_results, formatted_metrics, google_snippet, rag_response)
                collected_data[
                    "Current Stock Price"] = f"${current_stock_price:.2f}" if current_stock_price is not None else "N/A"

                conversation_history.append(f"RAG Response: {rag_response}")
                history_context = "\n".join(conversation_history)

                answer = answer_question_with_data(f"{history_context}\n\nUser's query: {translation}", collected_data)

                print(f"\nBot: {answer}")
                conversation_history.append(f"Bot: {answer}")

            else:
                with ThreadPoolExecutor() as executor:
                    futures = {
                        executor.submit(get_answer, user_input): "rag_response",
                        executor.submit(summarizer.google_search, user_input): "google_results",
                        executor.submit(summarizer.fetch_google_snippet, user_input): "google_snippet"
                    }
                    results = {futures[future]: future.result() for future in as_completed(futures)}

                google_results = results.get("google_results", "No additional news found through Google Search.")
                google_snippet = results.get("google_snippet", "Snippet not found.")
                rag_response = results.get("rag_response", "No response from RAG.")

                collected_data = prepare_data("", "", "", google_results, {}, google_snippet, rag_response)

                conversation_history.append(f"RAG Response: {rag_response}")
                history_context = "\n".join(conversation_history)

                answer = answer_question_with_data(f"{history_context}\n\nUser's query: {user_input}", collected_data)

                print(f"\nBot: {answer}")
                conversation_history.append(f"Bot: {answer}")

        except Exception as e:
            logger.error(f"An error occurred: {e}")
            response = "An error occurred while processing your request. Please try again later."
            print(f"Bot: {response}")
            conversation_history.append(f"Bot: {response}")

if __name__ == "__main__":
    main()