app.py

from flask import Flask, request, jsonify
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
import threading
import streamlit as st
import json

# Initialize Flask app
app = Flask(__name__)

# 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 google_search(self, query: str) -> Optional[str]:
        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()
            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]:
        try:
            result = df['close'].rolling(window=window).mean()
            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]:
        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))
            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]:
        try:
            result = df['close'].ewm(span=window, adjust=False).mean()
            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]:
        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})
            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]:
        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})
            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]:
        try:
            log_returns = np.log(df['close'] / df['close'].shift(1))
            result = log_returns.rolling(window=window).std() * np.sqrt(window)
            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]:
        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()
            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]:
        try:
            result = (np.sign(df['close'].diff()) * df['volume']).fillna(0).cumsum()
            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]:
        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]
            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]:
        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]
            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]:
        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
            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]:
        try:
            if eps == 0:
                raise ValueError("EPS cannot be zero for P/E ratio calculation.")
            result = current_price / eps
            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]:
    try:
        if "is **" in response and "**." in response:
            return response.split("is **")[1].split("**.")[0].strip()
        return response.strip()
    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:
        # Step 1: Detect Language
        prompt = f"Detect the language for the following text: {query}"
        response = invoke_llm(prompt)
        detected_language = response.content.strip()

        # 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()

        # 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()

        if not detected_entity:
            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:
            return detected_language, detected_entity, translated_query, None

        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:
    try:
        stock = yf.Ticker(symbol)
        end_date = datetime.now()
        start_date = end_date - timedelta(days=3 * 365)
        historical_data = stock.history(start=start_date, end=end_date)
        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']]
        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]:
    try:
        stock = yf.Ticker(symbol)
        result = stock.info['currentPrice']
        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:
    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"
        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:
    try:
        stock = yf.Ticker(symbol)
        company_info = stock.info
        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:
    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")
        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]:
    try:
        stock = yf.Ticker(symbol)
        news = stock.news
        return news if news else []
    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:
    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")
        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:
    try:
        unified_content = " ".join(content)
        prompt = f"Summarize the main points of this article.\n\n{unified_content}"
        response = invoke_llm(prompt)
        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:
    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 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)
        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]:
    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}%"
            }
        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]:
    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)
    return collected_data

@app.route('/ask', methods=['POST'])
def ask():
    try:
        user_input = request.json.get('question')
        summarizer = DataSummarizer()
        language, entity, translation, stock_ticker = detect_translate_entity_and_ticker(user_input)
        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"
            answer = answer_question_with_data(f"{translation}", collected_data)
            return jsonify({"answer": 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)
            answer = answer_question_with_data(f"{user_input}", collected_data)
            return jsonify({"answer": answer})
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        return jsonify({"error": "An error occurred while processing your request. Please try again later."}), 500

# Streamlit App
def send_question_to_api(question):
    url = 'http://localhost:5000/ask'
    headers = {'Content-Type': 'application/json'}
    data = {'question': question}
    response = requests.post(url, headers=headers, data=json.dumps(data))
    if response.status_code == 200:
        return response.json().get('answer')
    else:
        return f"Error: {response.status_code} - {response.text}"

def run_streamlit():
    st.title("Financial Data Chatbot Tester")
    st.write("Enter your question below and get a response from the chatbot.")
    if 'history' not in st.session_state:
        st.session_state.history = []
    user_input = st.text_input("Your question:", "")
    if st.button("Submit"):
        if user_input:
            with st.spinner('Getting the answer...'):
                answer = send_question_to_api(user_input)
                st.session_state.history.append((user_input, answer))
                st.success(answer)
        else:
            st.warning("Please enter a question before submitting.")
    if st.session_state.history:
        st.write("### History")
        for idx, (question, answer) in enumerate(st.session_state.history, 1):
            st.write(f"**Q{idx}:** {question}")
            st.write(f"**A{idx}:** {answer}")
            st.write("---")

if __name__ == '__main__':
    threading.Thread(target=lambda: app.run(host='0.0.0.0', port=7860)).start()
    run_streamlit()