app.py

import gradio as gr
import yfinance as yf
import csv
import pandas as pd
import matplotlib.pyplot as plt
from valuation import parallel_portfolio_valuation,serialCal

def get_stock_price(stock_symbol):
    try:
        stock = yf.Ticker(stock_symbol)
        stock_data = stock.history(period="1d")
        latest_close = stock_data['Close'].iloc[-1]
        return float(latest_close)
    except Exception as e:
        return 0

def getCompoanyInfo(portfolio):
    #yfinance
    company_data = []
    
    for stock in portfolio:
        try:
            stock_info = yf.Ticker(stock['symbol']).info
            # Collect key datasets
            company_details = {
                'symbol': stock['symbol'],
                'company_name': stock_info.get('longName', 'N/A'),
                'sector': stock_info.get('sector', 'N/A'),
                'industry': stock_info.get('industry', 'N/A'),
                'market_cap': stock_info.get('marketCap', 'N/A'),
                'dividend_yield': stock_info.get('dividendYield', 'N/A'),
                'pe_ratio': stock_info.get('trailingPE', 'N/A'),
                '52_week_high': stock_info.get('fiftyTwoWeekHigh', 'N/A'),
                '52_week_low': stock_info.get('fiftyTwoWeekLow', 'N/A'),
                'price': round(stock['price'],2),
                'quantity': stock['quantity'],
            }
            
            company_data.append(company_details)
        
        except Exception as e:
            print(f"Error fetching data for {stock['symbol']}: {e}")
            continue
    
    return pd.DataFrame(company_data)

def calculatePortfolioValue(file:str):
    portfolio = [] 
    """
    portfolio = [
    {'symbol': 'AAPL', 'price': 160, 'quantity': 100},
    {'symbol': 'GOOG', 'price': 2000, 'quantity': 50},
    {'symbol': 'MSFT', 'price': 300, 'quantity': 200},
   
]
    """
    with open(file,"r") as fr:
        reader = csv.reader(fr)
        for i in list(reader)[1:]:
            #print({'symbol':i[0],'price':get_stock_price(i[0]),'quantity':i[1]})
            portfolio.append({'symbol':i[0],'price':get_stock_price(i[0]),'quantity':float(i[1])})
    
    return str(round(parallel_portfolio_valuation(portfolio),2)) + " $", getCompoanyInfo(portfolio)


def startGradio():
    with gr.Blocks() as demo:
        text1 = gr.Text(value ="Stock Portfolio Valuation using multiprocessing",interactive=False,show_label=False)
        with gr.Column():
            file = gr.File()
            df = gr.Dataframe()
            parallelValue = gr.Textbox(label="Portfolio Valuation through the process of paralleization")
            bt = gr.Button()
        bt.click(fn=calculatePortfolioValue,inputs=[file],outputs=[parallelValue,df])
        demo.load()

    demo.launch()


if __name__ == "__main__":
    startGradio()
    #calculatePortfolioValue("/Users/sarathrajan/Desktop/HPCUI/stocks_qty.csv")