app.py

import os
import time
import re
import requests
import yfinance as yf
import warnings
import streamlit as st
warnings.filterwarnings("ignore")

from bs4 import BeautifulSoup
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.utilities.alpha_vantage import AlphaVantageAPIWrapper
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_community.tools import ShellTool
from langchain_community.llms import HuggingFaceEndpoint
from langchain.tools import BaseTool, StructuredTool, Tool, tool,DuckDuckGoSearchRun
from langchain import LLMMathChain
from pydantic import BaseModel, Field
from langchain.tools import DuckDuckGoSearchRun
from langchain_community.utilities import SerpAPIWrapper
search = SerpAPIWrapper(serpapi_api_key='a8845c74bc619b37a2d2c338489ef8d2187e053b3bd38280aac8165b927a7267')
t2 = Tool.from_function(
        name="Google Search",
        func=search.run,
        description='Useful for when you are asked to perform internet search '
    )

#search=DuckDuckGoSearchRun()
llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2")

# Fetch stock data from Yahoo Finance
def get_stock_price(ticker,history=5):
    # time.sleep(4) #To avoid rate limit error
    if "." in ticker:
        ticker=ticker.split(".")[0]
    ticker=ticker+".NS"
    stock = yf.Ticker(ticker)
    df = stock.history(period="1y")
    df=df[["Close","Volume"]]
    df.index=[str(x).split()[0] for x in list(df.index)]
    df.index.rename("Date",inplace=True)
    df=df[-history:]
    # print(df.columns)
    
    return df.to_string()

# Script to scrap top5 googgle news for given company name
def google_query(search_term):
    if "news" not in search_term:
        search_term=search_term+" stock news"
    url=f"https://www.google.com/search?q={search_term}&cr=countryIN"
    url=re.sub(r"\s","+",url)
    return url

def get_recent_stock_news(company_name):
    # time.sleep(4) #To avoid rate limit error
    headers={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}

    g_query=google_query(company_name)
    res=requests.get(g_query,headers=headers).text
    soup=BeautifulSoup(res,"html.parser")
    news=[]
    for n in soup.find_all("div","n0jPhd ynAwRc tNxQIb nDgy9d"):
        news.append(n.text)
    for n in soup.find_all("div","IJl0Z"):
        news.append(n.text)

    if len(news)>6:
        news=news[:4]
    else:
        news=news
    news_string=""
    for i,n in enumerate(news):
        news_string+=f"{i}. {n}\n"
    top5_news="Recent News:\n\n"+news_string
    
    return top5_news


# Fetch financial statements from Yahoo Finance
def get_financial_statements(ticker):
    # time.sleep(4) #To avoid rate limit error
    if "." in ticker:
        ticker=ticker.split(".")[0]
    else:
        ticker=ticker
    ticker=ticker+".NS"    
    company = yf.Ticker(ticker)
    balance_sheet = company.balance_sheet
    if balance_sheet.shape[1]>=3:
        balance_sheet=balance_sheet.iloc[:,:3]    # Remove 4th years data
    balance_sheet=balance_sheet.dropna(how="any")
    balance_sheet = balance_sheet.to_string()
    return balance_sheet


#Openai function calling
function=[
        {
        "name": "get_company_Stock_ticker",
        "description": "This will get the indian NSE/BSE stock ticker of the company",
        "parameters": {
            "type": "object",
            "properties": {
                "ticker_symbol": {
                    "type": "string",
                    "description": "This is the stock symbol of the company.",
                },

                "company_name": {
                    "type": "string",
                    "description": "This is the name of the company given in query",
                }
            },
            "required": ["company_name","ticker_symbol"],
        },
    }
]


def get_stock_ticker(query):
    response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            temperature=0,
            messages=[{
                "role":"user",
                "content":f"Given the user request, what is the comapany name and the company stock ticker ?: {query}?"
            }],
            functions=function,
            function_call={"name": "get_company_Stock_ticker"},
    )
    message = response["choices"][0]["message"]
    arguments = json.loads(message["function_call"]["arguments"])
    company_name = arguments["company_name"]
    company_ticker = arguments["ticker_symbol"]
    return company_name,company_ticker

def Anazlyze_stock(query):
    #agent.run(query) Outputs Company name, Ticker
    Company_name,ticker=get_stock_ticker(query)
    print({"Query":query,"Company_name":Company_name,"Ticker":ticker})
    stock_data=get_stock_price(ticker,history=10)
    stock_financials=get_financial_statements(ticker)
    stock_news=get_recent_stock_news(Company_name)

    available_information=f"Stock Price: {stock_data}\n\nStock Financials: {stock_financials}\n\nStock News: {stock_news}"
    #available_information=f"Stock Financials: {stock_financials}\n\nStock News: {stock_news}"

    # print("\n\nAnalyzing.....\n")
    analysis=llm(f"Give detail stock analysis, Use the available data and provide investment recommendation. \
             The user is fully aware about the investment risk, dont include any kind of warning like 'It is recommended to conduct further research and analysis or consult with a financial advisor before making an investment decision' in the answer \
             User question: {query} \
             You have the following information available about {Company_name}. Write (5-8) pointwise investment analysis to answer user query, At the end conclude with proper explaination.Try to Give positives and negatives  : \
              {available_information} "
             )
    # print(analysis)

    return analysis



# Making tool list
tools=[
    Tool(
        name="get stock data",
        func=get_stock_price,
        description="Use when you are asked to evaluate or analyze a stock. This will output historic share price data. You should input the the stock ticker to it "
    ),
    
    Tool(
        name="DuckDuckGo Search",
        func=search.run,
        description="Use only when you need to get NSE/BSE stock ticker from internet, you can also get recent stock related news. Dont use it for any other analysis or task"
    ),
    Tool(
        name="get recent news",
        func=get_recent_stock_news,
        description="Use this to fetch recent news about stocks"
    ),

    Tool(
        name="get financial statements",
        func=get_financial_statements,
        description="Use this to get financial statement of the company. With the help of this data companys historic performance can be evaluaated. You should input stock ticker to it"
    ) 


]



agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=5,
    handle_parsing_errors=True
)
#Adding predefine evaluation steps in the agent Prompt

new_prompt="""You are a financial advisor. Give stock recommendations for given query.
Everytime first you should identify the company name and get the stock ticker symbole for indian stock.
Answer the following questions as best you can. You have access to the following tools:

get stock data: Use when you are asked to evaluate or analyze a stock. This will output historic share price data. You should input the the stock ticker to it 
DuckDuckGo Search: Use only when you need to get NSE/BSE stock ticker from internet, you can also get recent stock related news. Dont use it for any other analysis or task
get recent news: Use this to fetch recent news about stocks
get financial statements: Use this to get financial statement of the company. With the help of this data companys historic performance can be evaluaated. You should input stock ticker to it

steps- 
Note- if you fail in satisfying any of the step below, Just move to next one
1) Get the company name and search for the "company name + NSE/BSE stock ticker" on internet. Dont hallucinate extract stock ticker as it is from the text. Output- stock ticker
2) Use "get stock data" tool to gather stock info. Output- Stock data
3) Get company's historic financial data using "get financial statements". Output- Financial statement
4) Use this "get recent news" tool to search for latest stock realted news. Output- Stock news
5) Analyze the stock based on gathered data and give detail analysis for investment choice. provide numbers and reasons to justify your answer. Output- Detailed stock Analysis

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do, Also try to follow steps mentioned above
Action: the action to take, should be one of [get stock data, DuckDuckGo Search, get recent news, get financial statements]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!

Question: {input}
Thought:{agent_scratchpad}"""



#Setting up new prompt
agent.agent.llm_chain.prompt.template=new_prompt


#App UI starts here
st.set_page_config(page_title="STOCK Analyzer", page_icon=":robot:")
st.header("STOCK Analyzer")

#Gets the user input
def get_text():
    input_text = st.text_input("You:", "Is ITC stock a good investment Choice right now? OR Shall I invest in Asian paints?.......")
    if input_text.isalpha():
        st.write(text, 'string', )
    else:
        st.write('Please type in a string Only')
    return input_text

user_input=get_text()
response = agent(user_input)

submit = st.button('Generate')  

#If generate button is clicked
if submit:

    st.subheader("Answer:")

    st.write(response)